Peer-Reviewed Publications


2019 |2018 | 20172016 | 2015 | 2014 2013 | 2012 | 2011 | 2010 | 2009 | 

2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 

2000 and before


393. Huang C, Yang Y, Panjwani N, Xing L: Pareto Optimal Projection Search (POPS): Automated Treatment Planning by Direct Search of the Pareto Surface. arXiv, 2008.08207, 2020.

392. Li X, Jia M, Islam MT, Yu L, Xing L: Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis. IEEE Transactions on Medical Imaging, Early Access: 1, 2020.

391. Shen L, Zhu W, Wang X, Xing L, Pauly JM, Turkbey B, Harmon SA, Sanford TH, Mehralivand S, Choyke P, Wood B, Xu D: Multi-Domain Image Completion for Random Missing Input Data. arXiv, 2007.05534, 2020.

390. Fan J, Xing L, Ma M, Hu W, Yang Y: Verification of the machine delivery parameters of treatment plan via deep learning. Physics in Medicine and Biology, Early Access, 2020.

389. Nomura Y, Wang J, Shirato H, Shimizu S, Xing L: Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network. Physics in Medicine and Biology, Early Access, 2020.

388. Wu Y, Ma Y, Du J, Xing L: Accelerating quantitative MR imaging with the incorporation of B1 compensation using deep learning. Magnetic Resonance Imaging, 72: 78-86, 2020.

387. Fan J, Xing L, Dong P, Wang J, Hu W, Yang Y: Data-driven dose calculation algorithm based on deep learning. arXiv, 2006.15485, 2020.

386. Olefir I, Tzoumas S, Restivo C, Mohajerani P, Xing L, Ntziachristos V: Deep Learning Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation. IEEE Transactions on Medical Imaging, Early Access: 1, 2020.

385. Khuzani MB, Ye Y, Napel S, Xing L: A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions. arXiv, 2006.13330, 2020.

384. Li X, Yu L, Chen H, Fu CW, Xing L, Heng PA: Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems, Early Access: 1-12, 2020.

383. Seo H, Khuzani MB, Vasudevan V, Huang C, Ren H, Xiao R, Jia X, Xing L: Machine learning techniques for biomedical image segmentation: An overview of technical aspects and ntroduction to state‐of‐art applications. Medical Physics, 47: e148-67, 2020.

382. Liu H, Wang H, Wu Y, Xing L: Superpixel Region Merging Based on Deep Network for Medical Image Segmentation. ACM Transactions on Intelligent Systems and Technology, 11: 1-22, 2020.

381. Lyu T, Wu Z, Zhang Y, Chen Y, Xing L, Zhao W: Dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network. arXiv, 2006.00149, 2020.

380. Dai X, Cheng K, Zhao W, Xing L: High‐speed X‐ray‐induced luminescence computed tomography. Journal of Biophotonics, Early Access: e202000066, 2020.

379. Zhao W, Lyu T, Chen Y, Xing L: A deep learning approach for virtual monochromatic spectral CT imaging with a standard single energy CT scanner. arXiv, 2005.09859, 2020.

378. Fesmire CC, Petrella RA, Fogle CA, Gerber DA, Xing L, Sano MB: Temperature Dependence of High Frequency Irreversible Electroporation Evaluated in a 3D Tumor Model. Annals of Biomedical Engineering, 48: 2233-46, 2020.

377. Ibragimov B, Toesca DAS, Chang DT, Yuan Y, Koong AC, Xing L, Vogelius IR: Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy, Medical Physics, 47: 3721-31, 2020.

376. Sano MB, Petrella RA, Kaufman JD, Fesmire CC, Xing L, Gerber D, Fogle CA: Electro-thermal therapy: Microsecond duration pulsed electric field tissue ablation with dynamic temperature control algorithms. Computers in Biology and Medicine, 121: 103807, 2020.

375. Bibault JE, Xing L: Screening for chronic obstructive pulmonary disease with artificial intelligence. The Lancet Digital Health, 2: e216-7, 2020.

374. Pan W, Sun M, Wu J, Dong H, Liu J, Gao R, Fang S, Xing L, Hu S, Yu B: Relationship between elevated plasma ceramides and plaque rupture in patients with ST-segment elevation myocardial infarction. Atherosclerosis, 302: 8-14, 2020.

373. Gao P, Cheng K, Schueler E, Jia M, Zhao W, Xing L: Restarted primal-dual Newton conjugate gradient method for enhanced spatial resolution of reconstructed cone-beam X-ray luminescence computed tomography images. Physics in Medicine and Biology, 65: 135008, 2020.

372. Sano M, Xing L: Methods for enhancing and modulating reversible and irreversible electroporation lesions by manipulating pulse waveforms. US Patent, 10589092, 2020.

371. Bush K, Hadsell MJ, Locke C, Xing L: Trajectory optimization in radiotherapy using sectioning. US Patent, 10576300, 2020.

370. Bibault JE, Xing L: Predicting Survival in Prostate Cancer Patients with Interpretable Artificial Intelligence. SSRN Electronic Journal, 3546050, 2020.

369. Sivasubramanian K, Kim J, Cheng K, Kim C, Xing L: Food based contrast agents for photoacoustic imaging. Photons Plus Ultrasound: Imaging and Sensing, 11240: 112403K, 2020.

368. Singh MKA, Sivasubramanian K, Sato N, Ichihashi F, Sankai Y, Xing L: Deep learning-enhanced LED-based photoacoustic imaging. Photons Plus Ultrasound: Imaging and Sensing, 11240: 1124038, 2020.

367. Dong P, Xing L: Deep DoseNet: a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy. Physics in Medicine and Biology, 65: 035010, 2020.

366. Zou L, Chen X, Xu C, Xing L, Xie Y: Design and Preliminary Experience of a Tele-Radiotherapy System for a Medical Alliance in China. Telemedicine and e-Health, 26: 235-43, 2020.

365. Wu Y, Ma Y, Capaldi DP, Liu J, Zhao W, Du J, Xing L: Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI. Magnetic Resonance Imaging, 66: 93-103, 2020.

364. Jia X, Mai X, Cui Y, Yuan Y, Xing X, Seo H, Xing L, Meng MGH: Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction. IEEE Transactions on Automation Science and Engineering, 17: 1570-84, 2020.

363. Sivasubramanian K, Xing L: Deep Learning for Image Processing and Reconstruction to Enhance LED-Based Photoacoustic Imaging. LED-Based Photoacoustic Imaging, 203-41, 2020.

362. Zhao W, Lv T, Lee R, Chen Y, Xing L: Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning. Pacific Symposium on Biocomputing, 25: 139-48, 2020.

361. Nomura Y, Xu Q, Peng H, Takao S, Shimizu S, Xing L, Shirato H: Modified fast adaptive scatter kernel superposition (mfASKS) correction and its dosimetric impact on CBCT‐based proton therapy dose calculation. Medical Physics, 47: 190-200, 2020.

360. Kaufman JD, Fesmire CC, Petrella RA, Fogle CA, Xing L, Gerber D, Sano MB, High-Frequency Irreversible Electroporation Using 5,000-V Waveforms to Create Reproducible 2-and 4-cm Ablation Zones—A Laboratory Investigation Using Mechanically Perfused Liver. Journal of Vascular and Interventional Radiology, 31: 162-8, 2020.


359. Khuzani MB, Vasudevan V, Ren H, Xing L: Stochastic Primal-Dual Method for Learning Mixture Policies in Markov Decision Processes. 2019 IEEE 58th Conference on Decision and Control (CDC), 1293-300, 2019.

358. Sun W, Zhang W, Su H, Leung P, Xing L, Xu L, Yang C, Xu Q: Improving cell performance and alleviating performance degradation by constructing a novel structure of membrane electrode assembly (MEA) of DMFCs. International Journal of Hydrogen Energy, 44: 32231-9, 2019.

357. Cao L, Shi R, Ge Y, Xing L, Zuo P, Jia Y, Liu J, He Y, Wang X, Luan S, Chai X, Guo W:Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning. European Journal of Radiology, 121: 108713, 2019.

356. Li Z, Wang Y, Yu Y, Fan K, Xing L, Peng H: Machine learning approaches for range and dose verification in proton therapy using proton‐induced positron emitters. Medical Physics, 46: 5748-57, 2019.

355. Shkolyar E, Jia X, Chang TC, Trivedi D, Mach KE, Meng MQH, Xing L, Liao JC: Augmented bladder tumor detection using deep learning. European Urology, 76: 714-8, 2019.

354. Vernekohl D, Ahmad M, Dai X, Zhao W, Cheng K, Xing L: Reduced acquisition time for L‐shell x‐ray fluorescence computed tomography using polycapillary x‐ray optics. Medical Physics, 46: 5696-702, 2019.

353. Huang C, Badiei M, Seo H, Ma M, Liang X, Capaldi D, Gensheimer M, Xing L: Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations. arXiv, 1911.10417, 2019.

352. Jia X, Xing X, Yuan Y, Xing L, Meng MQH: Wireless capsule endoscopy: A new tool for cancer screening in the colon with deep-learning-based polyp recognition. Proceedings of the IEEE, 108: 178-97, 2019.

351. Skinner LB, Yang Y, Hsu A, Xing L, Yu AS, Niedermayr T: Factor 10 Expedience of Monthly Linac Quality Assurance via an Ion Chamber Array and Automation Scripts. Technology in Cancer Research and Treatment, 18: 1533033819876897, 2019.

350. Shen L, Zhao W, Xing L: Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning. Nature Biomedical Engineering, 3: 880-8, 2019.

349. Zhao W, Han B, Yang Y, Buyyounouski M, Hancock SL, Bagshaw H, Xing L: Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT). Radiotherapy and Oncology, 140: 167-74, 2019.

348. Seo H, Huang C, Bassenne M, Xiao R, Xing L: Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Transactions on Medical Imaging, 39: 1316-25, 2019.

347. Zhao W, Han B, Yang Y, Buyyounouski M, Hancock SL, Bagshaw H, Xing L: Toward Markerless Image-Guided Radiotherapy Using Deep Learning for Prostate Cancer. Workshop on Artificial Intelligence in Radiation Therapy, 34-42, 2019.

346. Dai X, Cheng K, Zhao W, Xing L: X-ray-induced shortwave infrared luminescence computed tomography. Optics Letters, 44: 4769-772, 2019.

345. Fang C, Dai J, Zhang S, Wang Y, Wang J, Li L, Wang Y, Yu H, Wei G, Zhang X, Feng N, Liu H, Xu M, Ren X, Ma L, Tu Y, Xing L, Hou J, Yu B: Culprit lesion morphology in young patients with ST-segment elevated myocardial infarction: A clinical, angiographic and optical coherence tomography study. Atherosclerosis, 289: 94-100, 2019.

344. Das SK, McGurk R, Miften M, Mutic S, Bowsher J, Bayouth J, Erdi Y, Mawlawi O, Boellaard R, Bowen SR, Xing L, Bradley J, Schoder H, Yin FF, Sullivan DC, Kinahan P: Task Group 174 Report: Utilization of [18F] Fluorodeoxyglucose Positron Emission Tomography ([18F] FDG‐PET) in Radiation Therapy. Medical Physics, 46: e706-25, 2019.

343. Zhao W, Shen L, Han B, Yang Y, Cheng K, Toesca DAS, Koong AC, Chang DT, Xing L: Markerless pancreatic tumor target localization enabled by deep learning. International Journal of Radiation Oncology, Biology and Physics, 105: 432-9, 2019.

342. Shen L, Zhu W, Wang X, Xing L, Pauly J, Turkbey B, Harmon S, Sanford T, Mehralivand S, Choyke PL, Wood BJ, Xu D: Representational Disentanglement for Multi-Domain Image Completion. ICLR 2020, 2019.

341. Yuan Y, Qin W, Ibragimov B, Zhang G, Han B, Meng MQH, Xing L: Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition. IEEE Transactions on Automation Science and Engineering, 17: 574-83, 2019.

340. Liu C, Li Z, Hu W, Xing L, Peng H: Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs). Physics in Medicine and Biology, 64: 175009, 2019.

339. Khuzani MB, Shen L, Shahrampour S, Xing L: A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adversarial Networks. arXiv, 1909.11820, 2019.

338. Zhao W, Shen L, Han B, Yang Y, Cheng K, Toesca DAS, Koong AC, Chang DT, Xing L: Deep Learning Approach for Markerless Pancreatic Tumor Target Localization. International Journal of Radiation Oncology, Biology and Physics, 105: S202-3, 2019.

337. Ma M, Kavolchuk N, Buyounoski M, Xing L (co-corresponding author), Yang Y. Dosimetric Features-Driven Machine Learning Model for DVHs Prediction in VMAT Treatment Planning. Med Phys 2019; 46:857-867. PMID:30536442.

336. Yuan Y, Qin W, Buyounoski M, Hancock, S, Han B, Xing L. Prostate Cancer Classification with Multi-parametric MRI Transfer Learning Model. Med Phys 2019; 46:756-765. PMID: 30597561.

335. Lindsay C, Bazalova-Carter M, Wang A, Wu M, Newson M, Xing L, Ansbacher W, Fahrig R, Star-Lack J. Applications of combined kV/MV CBCT imaging with a high-DQE MV detector. Med. Phys 2019; 46:563-575.  PMID:30428131.

334. Zaman R, Yousufi S, Chibana H, Ikeno F, Long SR, Gambhir SS, Chin FT, McConnell MV, Xing L, Yeung A. In Vivo Translation of the CIRPI System---Revealing Molecular Pathology of Rabbit Aortic Atherosclerotic Plaques. J Nucl Med 2019; Feb 8. pii: jnumed.118.222471. doi: 10.2967/jnumed.118.222471. [Epub ahead of print]. PMID: 30737298.

333. Nomura Y, Xu Q, Shirato H, Shimizu S, Xing L. Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network. Medical Physics 2019; in press.

332. Ibragimov B, Toesca D, Yuan Y, Koong A, Daniel C, Xing L. Neural networks for deep radiotherapy dose analysis and prediction of liveer SBRT outcomes. IEEE J Biomed Health Inform 2019; Mar 11. Preview Abstract PMID:30869633.

331. Huang P, Yu G, Lu H, Liu D, Xing L,  Yin Y, Kovalchuk N, Xing L (co-corresponding author), and Li D, Attention-aware Fully Convolutional Neural Network with Convolutional Long Short-Term Memory Network for Ultrasound Motion Tracking, Med Phys. 2019 Mar 26. doi: 10.1002/mp.13510. [Epub ahead of print]PMID:30912590.

330. Ma M, Buyounoski M, Vasudevan V, Xing L (co-corresponding author), Yang Y. Dose Distribution Prediction in Isodose Feature-Preserving Voxelization Domain Using Deep Convolutional Neural Network. Med Phys 2019; 14; in press.

329. Wu Y, Ma Y, Capaldi DP, Liu J, Zhao W, Du J, Xing L. Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI. Magn Reson Imaging. 2019 Mar 14. pii: S0730-725X(19)30092-X. doi: 10.1016/j.mri.2019.03.012. [Epub ahead of print] PMID: 30880112.

328. Wu Y, Ma Y, Liu J, Du J, Xing L. Self-attention convolutional neural network for improved MR image reconstruction. Information Sciences 2019; 490:317-328.


327. Naczynski DJ , Stafford JH, Turkcan S, Jenkins C, Koh AL , Sun C, Xing L. Rare-Earth-Doped Nanoparticles for Short-Wave Infrared Fluorescence Bioimaging and Molecular Targeting of αVβ3-Expressing Tumors. Molecular Imaging 2018; in press, 2018 Jan-Dec;17:1536012118799131. doi: 10.1177/1536012118799131. PMID: 30246593

326. Deng J, El Naqa I, Xing L. Editorial: Machine Learning With Radiation Oncology Big Data. Front Oncol 2018; 8:416. doi: 10.3389/fonc.2018.00416.

325. Močnik D, Ibragimov B, Xing L, Strojan P, Likar B, Pernuš F, Vrtovec T. Segmentation of parotid glands from registered CT and MR images. Phys Med 2018; 52:33-41. doi: 10.1016/j.ejmp.2018.06.012.

324. Zarepisheh M, Xing L, Ye Y. A Computation Study on an Integrated Alternaing Direction Method of Multipliers for Large Scale Optimization. Optimization Letters 2018; 12:3-15.

323. Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM. Deep generative adversarial networks for compressive sensing (GANCS) MRI. IEEE Trans Med Ima 37 2018; in press.

322. Dong P, Liu H, Xing L. Monte Carlo tree search-based non-coplanar trajectory design for station parameter optimized radiation therapy (SPORT). Physics in Medicine and Biology 2018 Jul 2; 63(13):135014. doi:10.1088/1361-6560/aaca17. PMID:29863493.

321. Das S, Bayouth JE, Erdi YE, Malawi O, Boellaard R, Bowen S, Xing L, Bradley J, Schoder H, Yin F, McGurk R, Sullivan D, Bowsher JE, Kinahan PE. Task Group 174 Report: Utilization of [18F]Fluorodeoxyglucose Positron Emission Tomography ([18F]FDG-PET) in Radiation Therapy. Med Phys 2018; in press.   

320. Ibragimov B, Toesca D, Chang D, Yuan Y, Koong A, Xing L. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys 2018 Aug 11; in press. doi:10.1002/mp.13122 [Epub ahead of print] PMID:30098025.

319. Liu H, Xu J, Guo Q, Ibragimov B, Wu Y, Xing L. Learning Deconvolutional Deep Neural Network for High Resolution (HR) Medical Image Reconstruction. Information Sciences 2018; in press.  

318. Zaman RT, Yousefi S, Long SR, Saito T, Mandella M, Qiu Z, Chen R, Contag CH, Gambhir SS, Chin FT, Khuri-Yakub BT, McConnell MV, Shung KK, Xing L. A Dual-Modality Hybrid Imaging System Harnesses Radioluminescence and Sound to Reveal Molecular Pathology of Atherosclerotic Plaques. Sci Rep 2018 Jun 12; 8(1):8992. doi:10.1038/s41598-018-26696-8. PMID:29895966.

317. Liu H, Xing L. Isodose feature-preserving voxelization (IFPV) for radiation therapy treatment planning. Medical Physics (Letters) 2018 Jun 1; 45(7):3321-3329. doi:10.1002/mp.12977. Epub 2018. PMID:29772065.

316. Sano MB, Fan RE, Cheng K, Saenz Y, Sonn GA, Hwang GL, Xing L. Reduction of Muscle Contractions during Irreversible Electroporation Therapy Using High-Frequency Bursts of Alternating Polarity Pulses: A Laboratory Investigation in an Ex Vivo Swine Model. J Vasc Interv Radiol 2018 Jun; 29(6):893-898.e4. doi:10.1016/j.jvir.2017.12.019. Epub 2018 Apr 6. PMID:29628296.

315. Xing L, Krupinski E, Cai J. Artificial intelligence will soon change the landscape of medical physics research and practice. Med Phys 2018 May; 45(5):1791-1793. doi:10.1002/mp.12831. Epub 2018. PMID:29476545.

314. Qin W, Wu J, Han F, Yuan Y, Zhao W, Ibragimov B, Gu J, Xing L. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation. Phys Med Biol 2018 May 4; 63(9):095017. doi:10.1088/1361-6560/aabd19. PMID:29633960.

313. Vernekohl D, Tzoumas S, Zhao W, Xing L. Polarized X-ray excitation for scatter reduction in X-ray fluorescence computed tomography. Med Phys 2018 May 25. doi:10.1002/mp.12997. [Epub ahead of print], 2018. PMID:29800510.

312. Zhao W, Vernekohl D, Han F, Han B, Peng H, Yang Y, Xing L, Min JK. A unified material decomposition framework for quantitative dual- and triple-energy CT imaging. Med Phys 2018 Apr 21. doi:10.1002/mp.12933. [Epub ahead of print]. PMID:29679500.

311. Toesca DAS, Ibragimov B, Koong AJ, Xing L, Koong AC, Chang DT. Strategies for prediction and mitigation of radiation-induced liver toxicity. J Radiat Res 2018 Mar 1; 59(suppl_1):i40-i49. doi:10.1093/jrr/rrx104. PMID:29432550.

310. Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. J Radiat Res 2018 Mar 1; 59(suppl_1):i25-i31. doi:10.1093/jrr/rrx102. PMID:29385618.

309. Shirato H, Le QT, Kobashi K, Prayongrat A, Takao S, Shimizu S, Giaccia A, Xing L, Umegaki K. Selection of external beam radiotherapy approaches for precise and accurate cancer treatment. J Radiat Res 2018 Mar 1; 59(suppl_1):i2-i10. doi:10.1093/jrr/rrx92. PMID: 29373709.

308. Zaman RT, Tuerkcan S, Mahmoudi M, Saito T, Yang PC, Chin FT, McConnell MV, Xing L. Imaging cellular pharmacokinetics of 18F-FDG and 6-NBDG uptake by inflammatory and stem cells. PLoS One 2018 Feb 20; 13(2):e0192662. doi:10.1371/journal.pone.0192662. eCollection 2018. PMID 2942173.

307. King M, Sensakovic WF, Maxim P, Diehn M, Loo BW, Xing L. Line-Enhanced Deformable Registration of Pulmonary Computed Tomography Images Before and After Radiation Therapy with Radiation-Induced Fibrosis. Technol Cancer Res Treat 2018 Jan 1; 17:1533034617749419. doi: 10.1177/1533034617749419. PMID: 29343206.

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306. Yuan Y, Qin W, Han B, Meng M, Xing L. Densely Connected Neural Network with Unbalanced Discrimination and Category Sensitive Constraint for Polyp Recognition. IEEE Trans Med Ima, submitted, 2017.

305. Ibragimov B, Korez R, Likar B, Pernu F, Xing L, and Vrtove T. Segmentation of Pathological Structures by Landmark-Assisted Deformable Models. IEEE Trans Med Ima 36, 1457-1469, 2017.

304. Tzoumas S, Vernekohl D, and Xing L. Coded-Aperture Compressed Sensing X-ray Luminescence Tomography. IEEE Trans Bio Eng, in press, 2017.

303. Liu H, Dong P, Xing L. Using measurable dosimetric quantities to characterize the inter-structural tradeoff in inverse planning. Phys Med Biol 62, 6804-6821, 2017 (doi:10.1088/1361-6560/aa6fcb. [Epub ahead of print] PMID:28447959).

302. Liu H, Dong P, Xing L. A New Sparse Optimization Scheme for Simultaneous Beam Angle and Fluence Map Optimization in Radioterapy PlanningPhys Med Biol 2017; 62(16):6428-45.

301. Han H, Gao H, Xing L. Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and non-local total variation. Physics in Medicine and Biology 2017; 62(16):6408-27.

300. Zhao W, Xing L, Zhang Q, Xie Q, Niu T. Segmentation-Free X-ray Energy Spectrum Estimation for Computed Tomography Using Dual-Energy Material Decomposition. J Medical Imaging 4, 023506-10, 2017.

299. Zaman RT, Yousefi S, Long S, Saioto T, Mandella M, Qiu Z, Jenkins C, Chen R, Contag CH, Gambhir SS, Chin F, Khuri-Yakub B, Mell M, Lee JT, Chandra V, Dalman RL, McConnell M, Shung K, Fearon W, Xing L. Harnessing Radioluminescence and Sound to Reveal Molecular Pathology of Atherosclerotic Plaques. Radiology, submitted, 2017.

298. Zhang G, Liu F, Liu J, Luo J, Bai J, Xing L. Generalized adaptive Gaussian Markov random field for X-ray luminescence computed tomography. Journal of Bio-optics, submitted, 2017.

297. Naczynski D, Stafford J, Turkcan S, Jenkins C, Koh A, Sun C, Xing L. Short-wave infrared imaging of integrin targeted rare-earth doped nanoparticles. Small, submitted, 2017.

296. Zhao W, Vernekohl D, Han F, Han B, Peng H, Xing L, and Min JK. A unified image reconstruction framework for quantitative dual- and triple-energy CT imaging of material compositions. Medical Physics, submitted, 2017.

295. Cheng K, Sano M, Jenkins CH, Zhang G, Vernekohl D, Zhang Z, Cheng Z, Xing L. Strong Electric Coupling at Nanoscale Interfaces in Combination with Ionizing Radiation Enables Synergistic Therapeutic Enhancement. ACS Nano, submitted, 2017.

294. Peng H, Vinke R, Takao S, Umegaki K, Shirato H, Xing L, and Lee R. Enabling conventional cone beam CT with the capability of dual energy imaging using a simple add-on beam modifier. Journal of X-ray Technology, submitted, 2017.

293. Dong P, Liu H, Xing L. Artificial Intelligence (AI)-Based Non-Coplanar Rotational Arc Trajectory Design for Station Parameter Optimized Radiation Therapy (SPORT). Medical Physics 44, submitted, 2017.

292. Fu A, Ungan B, Xing L, Boyd S. A Convex Optimization Approach to Radiation Treatment Planning with Dose Constraints. Operations Research, submitted, 2017.

291. Ibragimov BD, Tosca D, Chang D, Kong, Xing L. Deep learning for segmentation of portal vein for liver radiation therapy. Physics in Medicine and Biology 2017; 62(23):8943-8958.

290. Ibragimov B, Diego DD, Koong A, Xing L. Individualizing toxicity prediction after SBRT by harnessing the power of deep learning. International Journal of Radiation Oncology, Biology Physics, submitted 2017.

289. Hao Peng and Xing L. Proton range verification using sparsely distributed gamma detectors and feedforward neural-network. Radiology, submitted, 2017.

288. Sano M, Volotskova O., Xing L. Treatment of cancer in vitro using radiation and high frequency bursts of sub-microsecond electrical pulses. Transactions on Biomedical Engineering, submitted, 2017.

287. Cheng K, Chen H, Jenkins C, Zhang G, Zhao W, Zhang Z, Han F, Fung J, Yang M, Jiang Y, Xing L (co-corresponding author), Cheng Z. Synthesis, Characterization and Biomedical Applications of a Targeted Dual-Modal NIR-II Fluorescence and Photoacoustic Imaging Nanoprobes. ACS Nano 2017; 11(12):12276-12291.

286. Zaman RT, Yousefi S, Long S, Saioto T, Mandella M, Qiu Z, Jenkins C, Chen R, Contag CH, Gambhir SS, Chin F, Khuri-Yakub B, Mell M, Lee JT, Chandra V, Dalman RL, McConnell M, Shung K, Fearon W, Xing L. Harnessing Radioluminescence and Sound to Reveal Molecular Pathology of Atherosclerotic Plaques. Nature Biomedical Engineering 2016; under review (tracking number nBME-16.0402).

285. Li D, Liu L, Chen J, Li H, Yin Y, Ibragimov B, Xing L. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contoursPhys Med Biol 2017; 62(1):272-288. Epub 2016 Dec 17.

284. Han B, Ding A, Lu M, Xing L. Pixel response-based EPID dosimetry for patient specific QA. J App Clinical Medical Physics 2017; 18(1):9-17. doi:10.1002/acm2.12007. Epub 2016 Dec 15.

283. Sano M, Fan RE, Xing L. Asymmetric Waveforms Decrease Lethal Thresholds in High Frequency Irreversible Electroporation TherapiesScientific Reports 7. 40747-51, 2017.

282. Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convoluntional neural networks. J Med Imaging (Bellingham) 2017;4(1):014501. doi:10.1117/1JMI.4.1.014501. Epub 2017 Jan 6.

281. Lee H, Fahimian B, Xing L. Binary moving-blocker-based scatter correction in cone-beam computed tomography with width-truncated projections: proof of conceptPhys Med Biol 2017. Mar 21;62(6):2176-2193. doi:10.1088/1361-6560/aa5913. Epub 2017 Jan 12.

280. Ibragimov B, Korez R, Likar B, Pernus F, Xing L, Vrtovec T. Segmentation of Pathological Structures by Landmark-Assisted Deformable Models. IEEE Trans Med Imaging 2017. doi:10.1109/TMI.2017.2667578. [Epub ahead of print].

279. Ibragimov B, Xing L. Deep learning for segmentation of organs-at-risks in head and neck CT images. Medical Physics 2017; 44,547-57.

278. Wang H, Xing L. Using Population-Based Prior Knowledge to Autopilot Radiation Therapy Treatment Planning. Medical Physics 2017; 44,389-96.

277. Jenkins C, Xing L, Yu A. Using a handheld stereo depth camera to overcome limited field-of-view in simulation imaging for radiation therapy treatment planning. Medical Physics 2017; doi:10.1002/mp.12207. [Epub ahead of print].

276. Ren S, Hara W, Wang L, Buyyounouski MK, Le QT, Xing L, Li R. Robust Estimation of Electron Density from Anatomic Magnetic Resonance Imaging of the Brain using a Unifying Multi-Atlas ApproachInt J Radiat Oncol Biol Phys 2017; Mar 15;97(4):849-857. doi: 10.1016/j.ijrobp.2016.11.053. Epub 2016 Dec 14. 

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275. Vernekohl D, Ahmad M, Chinn G, Xing L. "Feasibility study of Compton cameras for x-ray fluorescence computed tomography with humans." Phys Med Biol 2016; 61,8521-40.

274. Li D, Zang P, Chai X, Cui Y, Li R, and Xing L. Automatic multiorgan segmentation in CT images of the male pelvis using region specific hierarchical appearance cluster models. Medical Physics 2016; Dec 21; 61(24):8521-8540. Epub 2016 Nov 15.

273. Lee M, Han B, Jenkins CH, Xing L (co-corresponding author). Tae Suk Suh, A Depth Sensing Technique on 3D-Printed Compensator for Total Body Irradiation (TBI) Patient Measurement and Treatment PlanningMedical Physics 2016; 43,6137-43.

272. Zhao W, Niu T, Xing L, Xie Y, Xiong G, Elmore K, Zhu J, Wang L, JK. Using Edge-Preserving Algorithm with Non-local Mean for Significantly Improved Image-Domain Material Decomposition in Dual Energy CT. Physics in Medicine and Biology 2016; 61(3):1332-135.

271. Zhao W, Verekohl D, Zhu J, Wang L, Xing L. A Model-Based Scatter Artifacts Correction for Cone Beam CT Scatter RemovalMedical Physics 2016; 43,1736-53.

270. Xiang L, Tang S, Ahmad M, Xing L. A high resolution X-ray induced acoustic tomographyNature: Scientific Reports 2016; 6:26118. doi: 10.1038/srep26118.

269. Dong P, Ungun C, Boyd S, Xing L. Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy (SPORT). Medical Physics 2016; 43,4973-82.

268. Kim J, Na Y, Xing L, Lee R, Park S. Automatic Deformable Surface Registration for Medical Applications by Radial Basis Function-based Robust Point-Matching. Computers in Biology and Medicine 2016; Oct 1;77:173-81. doi:10.1016/j.compbiomed.2016.07.013. Epub 2016 Aug 11. 

267. Jenkins C, Naczynski N, Yu A, Yang Y, Xing L. Automating quality assurance of digital linear accelerators using a radioluminescent phosphor coated phantom and optical imagingPhysics in Medicine and Biology (Rapid Communications) 2016; 61,L29–L37.

266. Lu J, Huang BT, Xing L, Chang D, Peng X, Xie LX, Lin ZX, Li M. Dosimetric analysis of isocentrically shielded volumetric modulated arc therapy for locally recurrent nasopharyngeal cancerNature: Scientific Report 2016; May 13;6:25959. doi: 10.1038/srep25959.

265. King M, Sun C, Carpenter C.M, Jenkins C.H., Ma X, Le Q, Sunwoo J, Cheng Z, Pratx G, Xing L. Flexible radioluminescence imaging for FDG-guided surgery. Medical Physics 2016; 43, 5298-5306.

264. Dong X, Sun X, Sun L, Maxim PG, Xing L, Huang Y, Li W, Wan H, Zhao X, Xing L, Yu J. Early Change in Metabolic Tumor Heterogeneity during Chemoradiotherapy and Its Prognostic Value for Patients with Locally Advanced Non-Small Cell Lung CancerPLOS One 2016; Jun 20;11(6):e0157836. doi: 10.1371/journal.pone.0157836.

263. Sano MB, Fan RE, Hwang GL, Sonn GA, Xing L. Production of Spherical Ablations Using Nonthermal Irreversible Electroporation: A Laboratory Investigation Using a Single Electrode and Grounding Pad. J Vasc Interv Radiol 2016; Sep;27(9):1432-1440.e3. doi: 10.1016/j.jvir.2016.05.032. Epub 2016 Jul 29. 

262. Zhang G, Liu F, Liu J, Luo J, Bai J, Xing L. Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian MethodIEEE Trans Med Imaging 2016; doi:10.1109/TMI.2016.2603843. [Epub ahead of print].

261. Wang H, Xing L. Application Programming in C# Environment with Recorded Actions of a Treatment Planning System User and its Application in Autopilot of VMAT/IMRT Treatment Planning. J Appl Clinical Medical Physics 2016; 17(6):6425.doi:1.

260. Lee M, Bin Han, Jenkins C, Xing L (co-corresponding author), Suk TS. A Depth Sensing Technique on 3D-Printed Compensator for Total Body Irradiation (TBI) Patient Measurement and Treatment Planning. Medical Physics 2016; 43(11):6137.

259. Sun Y, Qu C, Chen H, He M, Tang C, Shou K, Hong S, Yang M, Jiang Y, Ding B, Xiao Y, Xing L, Hong X, Cheng Z. Novel benzo-bis(1,2,5-thiadiazole) fluorophores for in vivo NIR-II imaging of cancer. Chemical Sciences 7 2016; 6203-6207.  

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258. Kim H, Li R, Lee R, Xing L. Beam's-eye-view dosimetrics (BEVD) guided rotational station parameter optimized radiation therapy (SPORT) planning based on reweighted total-variation minimization. Physics in Medicine and Biology 2015; 60(5);N71-82. doi: 10.1088/0031-9155/60/5/N71.

257. Ding A, Xing L, Han B. Development of an Accurate EPID-Based Output Measurement and Dosimetric Verification Tool for Electron Beam Therapy. Medical Physics 2015; 42(7):4190-9. doi:10.1118/1.4922400.

256. Unkelbach J, Bortfeld T, Craft D, Alber M, Bangert M, Bokrantz R, Chen D, Li R, Xing L, Men C, Nill S, Papp D, Romeijn E, Salari E. Optimization approaches to volumetric modulated arc therapy planning. Medical Physics 2015; 42(3):1367-77. doi:10.1118/1.4908224.

255. King M, Maxim P, Diehn M, Loo B, Xing L. Analysis of Long-Term 4-Dimensional Computed Tomography Regional Ventilation after Radiation Therapy. Int J Radiat Oncol Biol Phys 2015; 92(3), 683-90. doi: 10.1016/j.ijrobp.2015.02.037. Epub 2015 Apr 28.

254. Zaman RT, Kosuge H, Carpenter C, Sun C, McConnell MV, Xing L. Scintillating-Balloon-Enabled Fiber-Optic System for Radionuclide Imaging of Atherosclerotic Plaques.J Nucl Med 2015; 56(5):771-7. doi:10.2967/jnumed.114.153239.

253. Volotskova O, Sun C, Stafford JH, Koh A, Ma X, Cheng Z, Cui B, Pratx G, and Xing L. Efficient Radioisotope Energy Transfer (RET) by Gold Nanoclusters for Molecular ImagingSmall 2015; 11(32):4002-8. doi:10.1002/smll.201500907. Epub 2015 May 13.

252. King M, Carpenter C, SunC, Ma X, Le Q, Sunwoo J, Cheng Z, MD; Pratx G, Xing L. A comparative evaluation of CCD-based beta imaging and Cerenkov luminescence imaging for FDG-guided surgery. Journal of Nuclear Medicine 2015; 56,1458-64.

251. Cui Y, Tha KK, Terasaka S, Yamaguchi S, Wang J, Kudo K, Xing L, Shirato H, Li R. Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images. Radiology 2016; 278(2):546-53. doi:10.1148/radiol.2015150358. Epub 2015 Sep 4. 

250. Bazalova M, Ahmad M, Xing L, Fahrig R. Experimental validation of L-shell x-ray fluorescence computed tomography imaging: phantom studyJournal of Medical Imaging 2015. 2(4):043501. doi:10.1117/1.JMI.2.4.043501.

249.  Ahmad M, Xiang L, Yousefi S, Xing L. Detection threshold of the proton-acoustic range verification technique. Medical Physics 42 2015; 5735-43.

248. Li D, Liu L, Kapp D, Xing L. Automatic liver contouring for radiotherapytreatment planning. Physi Med Biol 2015; 60(19), 7461-83. doi:10.1088/0031-9155/60/19/7461.

247. King M, Maxim P, Diehn M, Loo B, Xing L. Analysis of Long-term 4-Dimensional Computed Tomography Regional Ventilation after Radiation TherapyInt J Radiation Oncology Biology Physics 2015; 92(3):682-90. doi:10.1016/j.ijrobp.2015.02.037. Epub 2015 Apr 28.

246. Unkelbach J, Bortfeld T, Craft D, Alber M, Bangert M, Bokrantz R, Chen D, Li R, Xing L, Men C, Nill S, Papp D, Romeijn E, Salari E. Optimization approaches to volumetric modulated arc therapy planning. Medical Physics 2015, 42(3):1367-77. doi: 10.1118/1.4908224.

245. Ding A, Xing L, Han B. Development of an Accuate EPID-Based Output Measurement and Dosimetric Verification Tool for Electron Beam Therapy. Medical Physics 2015; 42(7):4190-8. doi:10.1118/1.4922400.

244. Kim H, Li R, Lee R, Xing L. Beam's-eye-view dosimetrics (BEVD) guided rotational station parameter optimized radiation therapy (SPORT) planning based on reweighted total-varation minimization. Physics in Medicine and Biology 2015; 60(5):N71-82. doi:10.1088/0031-9155/60/5/N71.

243. Naczynski DJ, Sun C, Türkcan S, Jenkins C, Koh AL, Ikeda D, Pratx G, Xing L. X-ray-Induced Shortwave Infrared Biomedical Imaging Using Rare-Earth Nanoprobes. Nano Lett. 2015; 15(1):96-102. doi:10.1021/nl504123r. Epub 2014 Dec 15. 

242. Bazalova-Carter M, Ahmad M, Matsuura T, Takao S, Matsuo Y, Fahrig R, Shirato H, Umegaki K, Xing L. Proton-induced X-ray fluorescence CT imagingMedical Physics 2015; 42(2):900-7. doi:10.1118/1.4906169.

241. Chen X, Bush K, Ding A, Xing L. Independent Calculation of Monitor Units for VMAT and SPORT. Medical physics 2015; 42(2):900-7. doi:10.1118/1.4906169.

240. Zarepisheh M, Li R, Ye, Xing L. Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT). Medical Physics 2015; 42(2):1012-22. doi:10.1118/1.4906253.

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239. Xing L, Li R. Inverse planning in the age of digial linacs: station parameter optimized radiation therapy (SPORT), presented at the XVII International Conference on the use of computers in radiation therapy. Journal of Physics Conference Series 2014; Vol 489, Article Number 012065.

238. Azcona JD, Li R, Mok E, Hancock S, Xing L. Automatic prostate tracking and motion assessment in volumetric modulated arc therapy with an electronic portal imaging deviceInt J Radiat Oncol Biol Phys 2013; 86(4):762-8. doi:10.1016/j.jrobp.2013.03.007.

237. Li R, Xing L, Horst K, Bush K. Multi-isocenter breast RT. Int J Radiat Oncol Biol Phys 2014; 88: 920-6.

236. Osakada Y, Pratx G, Sun C, Sakamoto M, Ahmad M, Volotskova O, Ong Q, Teranishi T, Harada Y, Xing L, Cui B. Hard X-ray-induced optical luminescence via biomolecule-directed metal clusters. Chem. Commun (Camb) 2014; 50(27):3549-51. doi: 10.1039/c3cc48661c.

235. Ahmad M, Bazalova M, Xiang L, Xing L. Order of magnitude sensitivity increase in X-ray fluorescence computed tomography (XFCT) imaging with an optimized spectro-spatial detector configuration: theory and simulationIEEE Trans Med Imaging 2014; 33(5):1119-28.

234. Taniguchi CM, Miao YR, Diep AN, Wu, Rankin EB, Atwood TF, Xing L, Giaccia A. Prolyl Hydroxylase Inhibition Mitigates and Protects Against Radiation-Induced Gastrointestinal Toxicity via HIF2Sci Transl Med 2014; 6(236):236ra64. doi:10.1126/scitranslmed.3008523.

233. Choi K, Li R, Nam H, Xing L. A Fourier-Based Compressed Sensing Technique for Accelerated CT Image Reconstruction using First-Order MethodsPhysics in Medicine and Biology 2014; 59(12):3097-119. doi:10.1088/0031-9155/59/12/3097.

232. Wang C, Volotskova O, Lu K, Ahmad M, Sun C, Xing L (co-corresponding author), Lin W. Synergistic Assembly of Heavy Metal Clusters and Luminescent Organic Bridging Ligands in Metal−Organic Frameworks for Highly Efficient X-ray ScintillationJournal of the American Society of Chemistry (JACS) (Communication) 2014; 136(17): 6171-4. doi:10.1021/ja500671h.

231. Yu V, Fahiman B, Xing L, Hristov D. Quality control procedures for dynamic treatment delivery techniques involving couch motionMed Phys 2014; 41(8):081712. doi: 10.1118/1.4886757.

230. Jenkins C, Naczynski D, Yu S, Xing L. Monitoring External Beam Radiotherapy using Real-Time Beam Visualization. Medical Physics 2015; 42(1):5-13. doi:10.1118/1.4901255. 

229. Fahimian B, Wu J, Wu H, Generster S, Xing L. Dual-gated volumetric modulated arc therapyRadiation Oncology 2014; 9:209. doi:10.1186/1748-717X-9-209. 

228. Gudur M, Hara W, Le Q, Wang L, Xing L, Li R. A unifying probabilistic bayesian approach to derive electron density from MRI for radiation therapy treatment planning. Phys Med Biol 2014; 59(21):6595-606. doi:10.1088/0031-9155/59/21/6595. 

227. Carpenter C, Ma X, Liu H, Sun C, Pratx G, Wang J, Gambhir S, Xing L* (co-corresponding author), Cheng Z. Improved Cerenkov Molecular Sensitivity with Beta (minus) Emitting Radiotracers. J. Nuclear Medicine 55 2014; 1905-9.

226. Zaman RT, Kosuge H, Pratx G, Carpenter C, Xing L, McConnell MV. Fiber-Optic System for Dual-Modality Imaging of Glucose Probes 18F-FDG and 6-NBDG in Atherosclerotic PlaquesPlos One 2014; 9(9):e108108. doi:10.1371/ journal.pone.0108108.

225. Fan Q, Cheng K, Hu X, Ma X, Zhang R, Yang M, Lu X, Xing L, Huang W, Gambhir S, Cheng Z. Transferring Biomarker into Molecular Probe: Melanin Nanoparticle as a Naturally Active Platform for Multimodality ImagingJournal of the American Chemical Society (JACS) 2014; 136(43), 15185-94. doi: 10.1021/ja505412p. Epub 2014 Oct 16.

224. Gudur M, Hara W, Le Q, Wang L, Xing L, Li R. A unifying probabilistic bayesian approach to derive electron density from MRI for radiation therapy treatment planningPhys Med biol 2014; 59(21):6595-606. doi:10.1088/0031-9155/59/21/6596. 

223. Kim J, Li R, Na Y, Lee R, Xing L. Accuracy of surface registration compared to conventional volumetric registration in patient positioning for head-and-neck radiotherapy: a simulation study using patient dataMedical Physics 2014; 41(12):121701. doi:10.1118/1.4894103.

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222. Fahimian B, Yu V, Horst K, Xing L, Hristov D. Trajectory Modulated Prone Breast Irradiation: A LINAC-based Technique Combinging Intennsity Modulated Delivery and the Motion of the CouchRadiother Oncol 2013; 109(3):475-81. doi:10.1016/j.radonc.2013.10.031.

221. Bazalova M, Ahmad M, Pratx G, Xing L. L-shell x-ray fluorescence computed tomography (XFCT) imaging of cisplatinPhysics in Medicine and Biology 2014; 59(1):219-32. doi:10.1088/0031-9155/59/1/219. Epub 2013 Dec 13.

220. Azcona J, Li R, Mok E, Hancock S, Xing LAutomatic prostate tracking and motion assessment in volumetric modulated arc therapy with an electronic portal imaging deviceInt J Radiat Oncol Biol Phys 2013; 86(4):762-8.

219. Pratx G, Chen K, Sun C, Axente M, Sasportas L, Carpenter C, Xing LHigh-Resolution Radioluminescence Microscopy of 18F-FDG Uptake by Reconstructing the β-Ionization Track.  J Nucl Med 2013; 54(10):1841-6. doi:10.2967/jnumed.112.113365.

218. Li R, Han, Meng B, Maxim P, Xing L, Koong A, Diehn M, Loo B. Clinical Implementation of Intrafraction Cone Beam Computed Tomography Imaging During Lung Tumor Stereotactic Ablative Radiation TherapyInt J Radiat Oncol Biol Phys 2013; 87(5):917-23. doi: 10.1016/j.ijrobp.2013.08.015.

217. Meng B, Xing L, Han B, Koong A, Chang D, Cheng J C.H.and Li R. Cone beam CT imaging with limited angle of projections and prior knowledge for volumetric verification of non-coplanar beam radiation therapy: A proof of concept study. Phys Med Biol 2013; 58(21):7777–89. doi:10.1088/0031-9155/58/21/7777.

216. Wen N, Kumarasiri A, Nurushev T, Burmeister J, Xing L, Liu D, Glide-Hurst C, Kim J, Zhong H, Movsas B, Chetty IJ. An assessment of PTV margin based on actual accumulation dose for prostate cancer radiotherapy. Phys Med Biol 2013; 58(21):7733–44. doi:10.1088/0031-9155/58/21/7733. 

215. Munleya M, Kagadis G, McGeec K, Kirovd A, Jang S, Mutic S, Jeraj R, Xing L, Bourland D. An introduction to molecular imaging in radiation oncology: a report by the AAPM Working Group on Molecular Imaging in Radiation Oncolog (WGMIR). Medical Physics 2013; 40(10):101501. doi:10.1118/1.4819818. Highlighted under the Editor’s Picks column.

214. Na Y, Suh TK, Kapp DS, Xing L. Towards web-based real-time radiation treatment planning system in cloud computing environment. Phys Med Biol 2013; 58, 6525-40.

213. Cho W, Bush K, Mok E, Xing L, Suh TS. Development of a fast and feasible spectrum modeling technique for flattening filter free beams. Med Phys 2013; 40(4):041721.

212. Kim H, Becker S, Lee R, Lee S, Shin S, Candes E, Xing L, Li R. Improving IMRT delivery efficiency with reweighted L1-minimization for inverse planning. Med Phys 2013; 40(7):071719.

211. Li R, Xing L. An adaptive planning strategy for station parameter optimized radiation therapy (SPORT): Segmentally boosted VMAT. Medical Physics (Letters) 2013; 40(5):050701.

210. Zhang X, Xing L. Sequentially reweighted TV minimization for CT metal artifact reduction. Med Phys 2013; 40(7):071907. doi:10.1118/1.4811129.

209. Xie Y, Xing L, Gu J, Liu W. Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy. Phys Med Biol 2013; 58(11):3615-30. doi:10.1088/0031-9155/58/11/3615.

208. Su X, Cheng K, Wang C, Xing L, Wu H, Cheng Z. Image-guided resection of malignant gliomas using fluorescent nanoparticles, Nanomedicine & Nanobiotechnology. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2013; 5(3):219-32. doi:10.1002/wnan.1212.

207. Ding C, Solberg TD, Hrycushko B, Xing L, Heinzerling J, Timmerman RD. Optimization of normalized prescription isodose selection for stereotactic body radiation therapy: conventional vs robotic linac. Med Phys 2013; 40(3):051705.

206. Choi K, Xing L, Koong A, Li R. First study of on-treatment volumetric imaging during respiratory gated VMAT. Med Phys 2013; 40(4): 040701. doi:10.1118/1.4794925.

205. Azcona JD, Li R, Mok E, Hancock S, Xing L. Development and clinical implementation of real-time tumor tracking by fiducial detection in cine megavoltage images during volumetric modulated arc therapy. Med Phys 2013; 40(3): 031708-14.

204. Kuang Y, Pratx G, Bazalova M, Qian J, Meng B, Xing L. Development of XFCT imaging strategy for monitoring the spatial distribution of platinum-based chemodrugs: Instrumentation and phantom validation. Medical Physics Letters 2013; 40(3):030701. doi:10.1118/1.4789917.

203. Osakada Y, Pratx G, Solomon PE, Hanson L, Xing L, Cui B. X-ray excitable luminescent polymer dots doped with an iridium(III) complex. Chem. Commun. 2013; 49(39):4319-21. doi:10.1039/c2cc37169c. 

202. Kuang Y, Bazalova M, Pratx G, Meng B, Qian J, Xing L. First demonstration of multiplexed X-ray fluorescence computed tomography (XFCT) imaging. IEEE Transactions on Medical Imaging 2013; 32:262-7.

201. Taniguchi CM, Murphy JD, Eclov N, Atwood TF, Kielar K, Christman-Skieller C, Mok E.,  Xing L, Koong AC, Chang DT. Dosimetric Analysis of Organs at Risk during Expiratory Gating with Stereotactic Body Radiotherapy for Pancreatic CancerInt J Radiat Oncol Biol Phys 2013; 85(4):1090-5. doi:10.1016/j.ijrobp.2012.07.2366.

200. Meng B, Xing L, Lee H, Fahimian BP. Stationary Edge Detection and Compressed Sensing Based Scatter Estimation for Scatter Correction in Computed Tomography. Med Phys 2013; 40,011907-1-12.

199. Xing L, Phillips M, Orton C. DASSIM-RT is likely to become the method of choice over IMRT and VMAT for delivery of highly conformal radiotherapyMed Phys 2013; 40(2):020601. 

198.  Xiang L, Han B, Carpenter C, Pratx G, Yu K, Xing L. X-ray acoustic computed tomography with pulsed X-ray beam from a medical linear acceleratorMedical Physics Letters 2013l 40(1):010701.

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197. Gao H, Lin R, Lin Y Xing L. 4D cone beam CT via spatiotemporal tensor frameletMedical Physics 2012; 39(11):6943-6. doi:10.1118/1.4762288.

196. Pratx G, Chen K, Sun C, Martin L, Carpenter CM, Olcott PD, Xing L. Radioluminescence Microscopy: Measuring the Heterogeneous Uptake of Radiotracers in Single Living CellsPLOS One 2012; 7(10):e46285. doi:10.1371/journal/pone.0046285.

195. Liu H, Carpenter C, Jiang H, Pratx G, Sun, Gambhir SS, Xing L, Cheng Z. Fiber-based system and demonstration for imaging tumor margins with Cerenkov Luminescence. J. Nuclear Medicine 2012; 53:1579-1584.

194. Davidi R, Schulte R, Censor Y, Xing L. Fast superiorization using a dual perturbation scheme for proton computed tomography. Trans Amer Nuc Soc 2012; 106:73-76.

193. Wen N, Glide-Hurst C, Nurushev T., Xing L, Kim J, Zhong H, Liu D, Liu M, Burmeister J, Movsas B and Chetty I, Evaluation of the deformation and corresponding dosimetric implications in prostate cancer treatmentPhys Med Biol 2012; 57(17):5361-79. doi:10.1088/0031-9155/57/17/5361. Highlighted in the medicalphysicsweb

192. Xiong G, Chen C, Chen J, Xie Y, Xing L. A Novel Method of Tracking the Motion Trajectories of Junction Points in 4D CT Images of the Lung. Physics in Medicine and Biology 2012; 57:4905-30.

191. Chen J, Chen C, Atwood TF, Gibbs IC, Soltys SG, Fasola C, Xing L. Volumetric Modulated Arc Therapy Planning Method for Supine Craniospinal Irradiation. Journal of Radiation Oncology 2012; 1:291-297.

190. Li R, Lewis JH, Berbeco RI, Xing L. Real-time tumor motion estimation using respiratory surrogate via memory-based learningPhys Med Biol 2012; 57(15):4771-86. doi:10.1088/0031-9155/57/15/4771.

189. Xiong G, Taylor TA, and Xing L. A Novel Method of Tracking the Motion Trajectories of Junction Points in 4D CT Images of the Lung. Physics in Medicine and Biology 2012; 57:4905-30.

188. Kim H, Li R, Lee R, Goldstein T, Boyd S, Xing L. Dose optimization with first-order total-variation minimization for dense angularly sampled and sparse intensity modulated radiation therapy (DASSIM-RT). Med Phys 2012; 39(7):4316-27, 2012.

187. Wang L, Kielar KN, Mok E, Hsu A, Dieterich S, Xing L. An end-to-end examination of geometric accuracy of IGRT using a new digital accelerator equipped with onboard imaging systemPhysics in Medicine and Biology 2012; 57(3):757–769.

186. Lee H, Xing L, Davidi R, Li R, Qian J, Lee R. Improved Compressed Sensing-based Cone-Beam CT Reconstruction using adaptive prior image constraintsPhysics in Medicine and Biology 2012; 57(8),2287–307.

185. Zhu L, Choi K, Xing LInverse planning for modulated arc therapy using compressed sensing technique. Technology in Cancer Research Treatment 2012; 11,149-162.

184. H Lee, L Xing, R Lee, B Fahimian. Scatter Correction in Cone-Beam CT via a Half Beam Blocker Technique Allowing Simultaneous Acquisition of Scatter and Image InformationMed Phys 2012; 39(5);2386-95. doi:10.1118/1.3691901.

183. Li R, Han B, Mok E, Koong A, Xing L. Evaluation of the geometric accuracy of surrogate-based gated VMAT usingintrafraction kilovoltage x-ray imagesMed Phys 2012; 39(5);2686-93. doi:10.1118/1.4704729.

182. Chen JJ, Chen J, Atwood T, Gibbs I, Solty S, Xing L. Volumetric Modulated Arc Therapy Planning Method for Supine Craniospinal Irradiation. Journal of Radiation Oncology 2012.

181. Liu H, Carpenter C, Jiang H, Pratx G, Sun C, Gambhir SS, Xing L, Cheng Z. Fiber-based system and demonstration for imaging tumor margins with Cerenkov Luminescence. J. Nuclear Medicine, in press.

180. Li R, Mok E, Daly M, Loo B, Chang D, Diehn M, Le Q, Koong A, Xing L. Intrafraction verification of gated RapidArc using beam-level kilovoltage x-ray imagesInt J Radiat Oncol Biol Phys 2012; 83(5):e709-15. doi:10.1016/j.ijrobp.2012.03.006.

179. Carpenter C, Pratx G, Sun C, Liu H, Cheng Z, Xing L. Radioluminescent Nanophosphors Enable Multiplexed Small-Animal ImagingOptics Express 2012; 20(11):11598-604. doi:10.1364/OE.20.011598.

178. Kim H, Xing L, Li R. Efficient IMRT inverse planning with a new L1-solver: template for first-order conic solverPhys Med Biol 2012; 57(13);4139-53. doi:10.1088/0031-9155/57/13/4139.

177. Bazalova M, Kuang Y, Pratx G, Xing L. Investigation of x-ray fluorescence computed tomography (XFCT) and K-edge CT imagingIEEE Transactions on Medical Imaging 2012; 31(8);1620-7. doi:10.1109/TMI.2012.2201165.


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176. Pratx G, Xing L. Monte Carlo simulation of photon migration in a cloud computing environment with MapReduce. J Biomed Opt 2011; 16(12):125003. doi:10.1117/1.3656964.

175. Meng B, Pratx G, Xing L. Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environmentMedical Physics 2011; 38(21):6603-9. doi:10.1118/1.3660200.

174. Wang J, Liang Z, Lu H, Xing L. Recent Development of Low-dose X-ray Cone-beam Computed Tomography. Current Medical Imaging Reviews 2011; 6;72-81.

173. Wang H, Ma Y, Pratx, G, and Xing L. Toward real-time Monte Carlo Simulation using a commercial cloud computing infrastructurePhysics in Medicine and Biology 2011; 56(17):N175-81. doi:10.1088/0031-9155/56/17/N02.

172. Li R, Xing L. Bridging the gap between IMRT and VMAT: Dense angularly sampled and sparse intensity modulated radiation therapyMedical Physics 2011; 38(9):4913-9. doi:10.1118/1.3618736. (Editor’s pick & Top 20 most downloaded article)

171. Schellenberg D, Kim J, Christman-Skieller C, Chun CL, Columbo L, Ford J, Fisher G, Kunz P, Van Dam J, Quon A, Desser TS, Norton J, Hsu A, Maxim P, Xing L, Goodman KA, Chang D, Koong AC. Single Fraction Stereotactic Body Radiation Therapy (SBRT) and Sequential Gemcitabine for the Treatment of Locally Advanced Pancreatic CancerInternational Journal of Radiation Oncology, Biology and Physics 2011; 81(1):181-8. doi:10.1016/j.ijrobp.2010.05.006.

170. Censor Y, Xing L. Iterative prescription refinement in fully-discretized inverse problems of radiation therapy planning. Inverse Problems in Science & Engineering 2011; 19:125-1137.

169. Qian J, Xing L, Liu W, Luxton G. Dose verification for respiratory-gated volumetric modulated arc therapy (VMAT)Physics in Medicine and Biology 2011; 56(15):4827–38.

168. Li R, Fahimian B, Xing L. A nonparametric Bayesian approach to real-time 3D tumor localization via monoscopic x-ray imaging during treatment delivery. Medical Physics 2011; 38:4205-14.

167. Carpenter C.M, Sun C, Pratx G, Ravilisetty R, Xing L. Limited angle X-ray luminescent tomography. Physics in Medicine and Biology 2011; 56:3487–3502.

166. Sun C, Pratx G, Carpenter CM, Liu H, Cheng Z, Gambhir SS, Xing L. Synthesis and Radioluminescence of PEGylated Eu3+-doped Nanophosphors as Bioimaging ProbesAdvanced Materials 2011; 23(24):H195-9.

165. Pratx G, Xing L. GPU Computing in Medical Physics: A ReviewMedical Physics 2011; 38(5):2685-97.

164. Cho W, Kielar K, Mok E, Xing L, Park JK, Jung WG, and Suh TS. Multisource modeling of flattening filter free (FFF) beam and optimization of model parametersMedical Physics 2011; 38(4):1931-41.

163. Zhang X, Wang J, Xing L. Metal artifacts reduction in X-ray CT by constrained optimizationMedical Physics 2011; 38(2):701-11.

162. Kim T, Zhu L, Suh TS, Geneser S, Xing L. Inverse Planning for IMRT with Non-Uniform Beam Profiles Using Total-Variation Regularization (TVR)Medical Physics 2011; 38(1):57-66.

161. Mao W, Speiser M, Medin P, Papiez L, Xing L, Solberg T. Initial application of a geometric QA tool for integrated MV and kV imaging systems on three image guided radiotherapy systemsMedical Physics 2011; 38(5):2335-41.

160. Darpolor MM, Yen YF, Xing L,  Clarke-Katzenberg RH, Chua M, Shi W, Mayer D, Josan S, Hurd RE, Pfefferbaum A,  Senadheera L, So S, Hofmann LV, Glazer GM, Spielman DM. In Vivo Magnetic Resonance Spectroscopic Imaging of Hyperpolarized [1-13C]-Pyruvate Metabolism in Rat Hepatocellular CarcinomaNMR in Biomedicine 2011; 24(5):506-13. doi:10.1002/nbm.1616.

159. Sun C, Carpenter C., Pratx G, Xing L. Facile Synthesis of Amine-Functionalized Eu3+-Doped La(OH)3 Nanophosphors for BioimagingNanoscale Res Letter 2011; 6(1):24.

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158. Lougovski P, LeNoach J, Zhu L, Ma Y, Xing L. IMRT inverse planning with locally tailored prescription. Technology in Cancer Research Treatment 2010; 8:629-36.

157. Pratx G, Carpenter CM, Sun C, Xing L. X-ray luminescence computed tomography via selective excitation: a feasibility studyIEEE Trans Med Imaging 2010; 29(12):1992-9. doi:10.1109/TMI.2010.2055883. 

156. Meng B, Zhu L, Widrow B, Boyd S, Xing L. A Unified Framework for 3D Radiation Therapy and IMRT planning: Plan Optimization in Beamlet Domain by Constraining or Regularizing the Fluence Map VariationsPhysics in Medicine and Biology 2010; 55:N521–531.

155. Meng B, Wang J, Xing L. Sinogram preprocessing and binary reconstruction for determination of the shape and location of metal objects in computed tomography (CT). Medical Physics 2010; 37(11):5867-75.

154. Ma Y, Chang D, Keall P, Xie Y, Park J, Suh T, Xing L. Inverse planning for 4D modulated arc therapy. Medical Physics 2010; 37(11):5627-33.

153. Choi K, Wang J, Zhu L, Suh T-S, Boyd S, Xing L. Compressed sensing based cone-beam computed tomography reconstruction with first-order methodMedical Physics 2010; 37(9):5113-25. 

152. Pratx G, Carpenter C, Sun C, Xing L. First Experimental Demonstration of X-Ray Luminescence Computed Tomography. Optical Letter 2010; 35(20):345-7.

151. Liu W, Qian J, Xing L, Hancock S, Luxton G. Clinical development of a failure detection-based online repositioning strategy for prostate IMRT – experiments, simulation, and dosimetry studyMedical Physics 2010; 37(10):5287-97.

150. Wang J, Xing L. A binary image reconstruction technique for accurate determination of the shape and location of metal objects in x-ray computed tomographyJournal of X-Ray Science and Technology 2010; 18(4):403-14. doi:10.3233/XST-2010-0271. 

149. Carpenter CM, Sun C, Pratx G, Ravilisetty R, Xing L. Hybrid X-ray/Optical Luminescence Imaging; Characterization of Experimental ConditionsMedical Physics 2010; 37;4011-18. – Editor's Picks in Medical Physics

148. Liu W, Wiersma R, Xing L. Optimized Hybrid MV-kV Imaging Protocol for Volumetric Prostate Arc TherapyInt J Radiat Oncol Biol Phys 2010; 78(2):595–604. doi:10.1016/j.ijrobp.2009.11.056.

147. Liu W, Luxton G, Xing L. A Failure Detection Strategy for Intrafraction Prostate Motion Monitoring with On-board Imagers for Fixed-Gantry IMRTInt J Radiat Oncol Biol Phys 2010; 78(3):904-11, 2010. doi:10.1016/j.ijrobp.2009.12.068.

146. Qian J, Lee L, Liu W, Chu K, Mok E, Luxton, G, Le Q, Xing L. Dose reconstruction for volumetric modulated arc therapy (VMAT) using cone-beam CT and dynamic log-filesPhysics in Medicine and Biology 2010; 55(13):3597-610. doi:10.1088/0031-9155/55/13/002. – Featured article in PMB and highlighted in the

145. Lee H, Lee J, Shin YG, Lee R, Xing L. Fast and accurate marker-based projective registration method for uncalibrated transmission electron microscope tilt seriesPhysics in Medicine and Biology 2010; 55(12):3417-40.

144. Chao M, Xie Y, Moros E, Le Q, Xing L. Image-based modeling of tumor shrinkage in head and neck radiation therapyMedical Physics 2010; 37(5):2351-8.

143. Potters L, Gaspar L, Kavanagh B, Galvin JM, Hartford AC, Hevezi JM, Kupelian PA, Mohiden N, Samuels MA, Timmerman R, Tripuraneni P, Vlachaki MT, Xing L, Rosenthal SA. American Society for Therapeutic Radiology and Oncology (ASTRO) and American College of Radiology (ACR) practice guidelines for image-guided radiation therapy (IGRT)Int J Radiat Oncol Biol Phys 2010; 76(2):319-25.

143. Zhu L, Wang J, Xie Y, Starman J, Fahrig R, Xing L. A patient set-up protocol based on partially blocked cone-beam CTTechnology in Cancer Research Treatment 2010; 9(2),91-198.

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142. Xu Q, He Z, Fan J, Hamilton RJ, Chen Y, Ma CM, Xing L. Registration of on-board X-ray images with 4DCT: a proposed method of phase and setup verification for gated radiotherapy. Phys Med 2010; 26(3):117-25. doi:10.1016/j.ejmp.2009.09.001.

141. Riaz N, Shanker P, Gudmundsson O, Wiersrma R, Mao W, Widrow B, Xing L. Predicting respiratory tumor motion with Multi-dimensional Adaptive Filters and Support Vector RegressionPhys Med Biol 2009; 54(19):5735-48.

140. Lee L, Ma Y, Ye Y, Xing L. Conceptual formulation on four-dimensional inverse planning for intensity modulated radiation therapyPhys Med Biol 2009; 54(13),N255-66.

139. Mao W, Hsu A, Riaz N, Lee L, Wiersma R, Luxton G, King C, Xing L, Solbert T. Image-guided radiotherapy in near real time with intensity-modulated radiotherapy megavoltage treatment beam imagingInt J Radiat Oncol Biol Phys 2009; 75(2):603-10.

138. Zhu L, Xing L. Search for IMRT inverse plans with piecewise constant fluence maps using compressed sensing techniquesMedical Physics 2009; 36(5):1895-905.

137. Ma Y, Lee L, Keshet O, Keall P, Xing L. Four-dimensional inverse planning with inclusion of implanted fiducials in IMRT segmented fieldsMedical Physics 2009; 36(6):2215-21.

136. Zhu L, Xie Y, Wang J, Xing L. Scatter correction for cone beam CT in radiation therapy. Medical Physics 2009; 36(6):2258-68.

135. Ma Y, Popple R, Suh T, Xing L. Beam's-eye-view Dosimetrics-guided inverse planning for aperture-modulated arc therapyInt J Radiat Oncol Biol Phys 2009; 75(5):1587-95. doi:10.1016/j.ijrobp.2009.05.003.

134. Wang J, Zhu L, Xing L. Noise reduction in low-dose x-ray fluoroscopy for image-guided radiation therapyInt J Radiat Oncol Biol Phys 2009; 74(2):637-43. 

133. Xie Y, Chao M, Xing L. Tissue feature0based and segmented deformable image registration for improved modeling of shear movement of lungsInt J Radiat Oncol Biol Phys 2009; 74(4):1256-65. doi:10.1016/j.irobp.2009.02.023.

132. Minn AY, Schellenberg D, Maxim P, Suh Y, McKenna S, Cox B, Dieterich S, Xing L, Graves E, Goodman KA, Chang D, Koong AC. Pancreatic tumor motion on a single planning 4D-CT does not correlate with intrafraction tumor motion during treatmentAm J Clin Oncol 2009; 32(4):364-8. doi:10.1097/COC.0b013e31818da9e0.  

131. Xu Q, He Z, Fan J, Hamilton RJ, Chen Y, Ma CM, Xing L. Registration of on-board X-ray images with 4DCT: A proposed method of phase and setup verification for gated radiotherapyPhys Med 2009; 26(30):117-25.

130. Zhu L, Wang J, Xing L. Noise Suppression in Scatter Correction for Cone-Beam CT. Medical Physics 2009; 36(3):741-52.

129. Paquin D, Levy D, Xing L. Multiscale registration of planning CT and daily cone beam CT images for adaptive radiation therapyMedical Physics 2009; 36(1):4-11.

128. Wang J, Li T, Xing L. Iterative image reconstruction for CBCT using edge-preserving priorMedical Physics 2009; 34(1):252–60.

127. Wiersma RD, Riaz N, Dieterich S, Suh Y, Xing L. Use of MV and kV imager correlation for maintaining continuous real-time 3D internal marker tracking during beam interruptions. Physics in Medicine and Biology 2009; 54,89-103. 

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126. Kashani R, Hub M, Balter JM, Kessler ML, Dong L, Zhang L, Xing L, Xie Y, Hawkes D, Schnabel JA, McClelland J, Joshi S, Chen Q, Lu W. Objective assessment of deformable image registration in radiotherapy: a multi-institution studyMed Phys 2008; 35(12):5944-53.

125. Liu W, Wiersma R, Mao W, Luxton G, and Xing L. Real-time 3D internal marker tracking during arc radiotherapy by use of combined MV-kV imagingPhysics in Medicine and Biology 2008; 53(24):7197-213. doi:10.1088/0031-9155/53/24/013.

124. Zhu L, Lee L, Ma Y, Ye Y, Mazzeo R, Xing L. Using total-variation regularization for intensity modulated radiation therapy inverse planning with field specific numbers of segmentsPhysics in Medicine and Biology 2008; 53(23),6653-72. 

123. Lee L, Mao W, Xing L. The use of EPID-measured leaf sequence files for IMRT dose reconstruction in adaptive radiation therapyMedical Physics 2008; 35(11):5019-29, 2008. 

122. Wang C, Chao M, Lee L, Xing L. MRI-based Treatment Planning with Electron Density Information Mapped from CT Images: A Preliminary StudyTechnology in Cancer Research Treatment 2008; 7(5):341-348.

121. Mao W, Riaz N, Lee L, Wiersma R, Xing L. A fiducial detection algorithm for real-time image guided IMRT based on simultaneous MV and kV imagingMedical Physics 2008; 35(8);3554-64.

120. Chao M, Xie Y, Xing L. Auto-propagation of contours for adaptive prostate raidation therapy. Physics in Medicine and Biology 2008; 53(17):4533-42. 

119. Wang J, Li T, Liang Z, Xing L. Dose reduction for kilovotage cone-beam computed tomography in radiation therapyPhysics in Medicine and Biology 2008; 53(11):2897-909. doi:10.1088/0031-9155/53/11/009. 

118. Xie Y, Chao M, Lee P, Xing L. Feature-based rectal contour propagation from planning CT to cone beam CT. Medical Physics 2008; 35(10):4450-9.

117. Xie Y, Djajaputra D, King C, Hossain S, Ma L, Xing L. Intrafractional motion of the prostate during hypofractionated radiotherapyInternational Journal of Radiation Oncology, Biology and Physics 2008; 72(1):236-4.

116. Mao W, Wiersma R, Xing L. Fast internal marker tracking algorithm for onboard MV and kV imaging systemsMedical Physics 2008; 35(5):1942-9.

115. Wiersma R, Mao W, Xing L. Combined kV and MV imaging for real-time tracking of implanted fiducial markersMedical Physics 2008; 35(4):1191-8.

114. Schreibmann E, Thorndyke B, Li T, Wang J, Xing L. 4D Image registration for image guided radiation therapy (IGRT)International Journal of Radiation Oncology, Biology and Physics 2008; 71(2):578-86. - highlighted article of the issue.

113. Mao W, Lee L, Xing L. Development of a QA phantom and automated analysis tool for geometric quality assurance of on-board MV and kV x-ray imaging systemsMedical Physics 2008; 35(4):1497-506, 2008.

112. Xing L. Quality assurance of positron emission tomography/computed tomography for radiation therapyInternational Journal of Radiation Oncology, Biology and Physics 2008; 71(1 Suppl):38-41. doi:10.1016/j.jrobp.2007.05.091.

111. Thornydyke B, Koong A, Xing L. Reducing respiratory motion artifacts in radionuclide imaging through retrospective stacking: A simulation study. Linear Algebra and its Applications 2008; 428:1325-1344.

110. Chao M, Schreibamnn E, Li T, Xing L. Automated contour mapping with a regional deformable modelInternational Journal of Radiation Oncology, Biology and Physics 2008; 70(2):599-608.

109. Lee L, Le Q, Xing L. Retrospective IMRT dose reconstruction based on cone-beam CT and MLC log-fileInternational Journal of Radiation Oncology, Biology and Physics 2008; 70(2):634-44, 2008.

108. Wink NM, Chao M, Antony J, Xing L. Individualized gating windows based on 4D CT information for respiration-gated radiotherapyPhysics in Medical Biology 2008; 53(1):165-75.

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107. Xing L, Chang J, Orton CG. Point/Counterpoint. Kilovoltage imaging is more suitable than megavoltage imagining for guiding radiation therapyMed Phys 2007; 34(12):4563-6. No abstract available. 

106. Chao M, Scheibmann E, Li T, Wink N, Xing L. Automated contour mapping using sparse volume sampling for 4D radiation therapyMedical Physics 20078; 34(10):4023-9.

105. Li T, Koong A, Xing L. Enhanced 4D cone-beam CT with inter-phase motion modelMedical Physics 2007; 34(9):3688-95. – figures featured in the cover of the issue of the journal. 

104. Wiersma RD, Xing L. Examination of geometric and dosimetric accuracies of gated step-and-shoot IMRTMedical Physics 2007; 34(10):3962-70.

103. Mao W, Li T, Wink N, Xing L. CT image registration in sinogram spaceMedical Physics 2007; 34(9):3596-602.

102. Paquin D, Levy D, Xing L. Hybrid multiscale landmark and deformable image registrationMathematical Bioscience and Engineering 2007; 4(4): 711-37.

101. Xing L, Siebers J, Keall P. Computational challenges for image-guided radiation therapy: framework and current research. Seminar in Radiation Oncology 2007; 17(4):245-57.

100. de la Zerda A, Armbruster B, Xing L. Formulating adaptive radiation therapy (ART) treatment planning into a closed-loop control frameworkPhysics in Medical Biology 2007; 52(14): 4137-53, 2007. 

99. Pawlicki T, Kim GY, Hsu A, Cotrutz C, Boyer AL, Xing L, King CR, Luxton G. Investigation of linac-based image-guided hypofractionated prostate radiotherapyMedical Dosimetry 2007; 32(2): 71-9.

98. Timmerman RD, Kavanagh BD, Cho LC, Papiez L, Xing L. Stereotactic body radiation therapy in multiple organ sitesJournal of Clinical Oncology 2007; 25(8):947-52, 2007.

97. Niu G, Xiong Z, Cheng Z, Cai W, Gambhir SS, Xing L, Chen X: In vivo bioluminescence tumor imaging of RGD peptide-modified adenoviral vector encoding firefly luciferase reporter gene. Molecular Imaging and Biology 2007; 9:(3)126-34.

96. Yang Y, Schreibmann E, Li T, Wang C, Xing L. Evaluation of on-board kV cone beam CT (CBCT) based dose calculationPhysics in Medical Biology 2007; 52(3):685-705. Among the most highly downloaded articles of PMB in 2007.

95. Li T, Xing L. Optimizing 4D cone-beam CT acquisition protocol for external beam radiotherapyInternational Journal of Radiation Oncology, Biology and Physics 2007; 67(4):1211-9.

94. Daly M, Lieskovsky Y, Pawlicki T, Yau J, Pinto H, Kaplan M, Fee WE, Koong A, Goffinet D, Xing L, Le QT. Evaluation of patterns of failure and subjective salivary function in patients treated with intensity modulated radiotherapy for head and neck squamous cell carcinomaHead Neck 2007; 29(3):211-20.

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93. Thorndyke B, Schreibmann E, Koong A, Xing L. Reducing respiratory motion artifacts in positron emission tomography through retrospectie stackingMedical Physics 2006; 33(7):2632-41.

92. Li T, Schreibmann E, Yang Y, Xing L. Motion correction for improved target localization with on-board cone-beam computed tomographyPhysics in Medical Biology, 2006; 51(2):253-67. – listed as “Highlights of 2006”  of research published in PMB. 

91. Xing L, Thorndyke B, Schreibmann E, Yang Y, Li TF, Kim GY, Luxton G, Koong A. Overview of image-guided radiation therapyMedical Dosimetry 2006; 31(2): 91-112.

90. Li T, Xing L, Munro P, McGuiness C, Chao M, Yang Y, Loo B, Koong A. Four-dimensional cone-beam computed tomography using an on-board imagerMedical Physics, 2006; 33(10): 3825-33.

89. Li T, Thorndyke B, Scheibmann E, Yang Y, Xing L. Model-based image reconstruction for four-dimensional PET. Medical Physics 2006; 33(5):1288-98. 

88. Cao Q, Cai W, Li T, Yang Y,  Chen K, Xing L, Chen X. Combination of integrin an siRNA therpy and radiotherapy for breast cancerBiochemical and Biophysical Research Communications 2006; 351:726-32. 

87. Loo B, Draney MT, Sivanadan R, Ruehm S, Pawlicki T, Xing L, Herfkens RJ, Q.T. Le. Indirect MR lymphangiography of the head and neck using conventional gadolinium contrast: a pilot study in humansInternational Journal of Radiation Oncology Biology and Physics 2006; 66(2):462-8.

86. Paquin D, Levy D, Schreibmann E, Xing L. Multistage image registration. Mathematical Biosciences and Engineering 2006; 3:389-418. – figures featured in the cover of the issue of the journal.

85. Zhang X, Cai W, Cao F, Schreibmann E, Wu Y, Wu JC, Xing L, Chen X. 18F-Labeled Bombesin Analogs for Targeting GRP Receptor-Expressing Prostate CancerJournal of Nuclear Medicine 2006; 47(3): 492-501.

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84. Maxim PG, Carson JJ, Benaron DA, Loo BW Jr, Xing L, Boyer AL, Friedland S. Optical detection of tumors in vivo by visible light tissue oximetryTechnol Cancer Res Treat 2005; 4(3):227-34. 

83. Schreibmann E, Xing L. Image registration with auto-mapped control volumesMedical Physics 2006; 33(4):1165-79.

82. Schreibmann E, Chen GT, Xing L. Image interpolation in 4D CT using a Bsline deformable registration modelInternational Journal of Radiation Oncology, Biology and Physics 2005; 64(5):1537-50.

81. Li T, Schreibmann E, Thorndyke B, Tillman G, Boyer A, Koong A., Goodman K, Xing L. Radiation dose reduction in 4D computed tomographyMedical Physics 2005; 32(12): 3650-60. 

80. Yang Y, Xing L. Optimization of radiation dose-time-fractionation scheme with consideration of tumor specific biologyMedical Physics 2005; 32(12):3666-77.

79. Schreibmann E, Xing L. Dose-volume based ranking of incident beam direction and its utility in facilitating IMRT beam placementInternational Journal of Radiation Oncology, Biology and Physics 2005; 63(2):584-93, 2005. 

78. Schreibmann E, Xing L. Narrow band deformable registration of prostate magnetic resonance imaging, magnetic resonance spectroscopic imaging, and computed tomography studiesInternational Journal of Radiation Oncology, Biology and Physics 2005; 62(2):595-605. – figures featured in the cover of the issue of the journal. 

77. Xing L. The value of PET/CT is being over-sold as a clinical tool in radiation oncologyMedical Physics 2005; 32(6): 1457-8.

76. Xia P, Yu N, Xing L, Syn X, Verhey L. Investigation of using a power function as a cost function in inverse planning optimizationMedical Physics 2005; 32(4): 920-7.

75. Yang Y, Xing L. Towards biologically conformal radiation therapy (BCRT): selective IMRT dose escalation under the guidance of spatial biology distributionMedical Physics 2005; 32:1473-84.

74. Kim D, Margolis D, Xing L, Daniel B, Spielman D. In vivo prostate magnetic resonance spectroscopic imaging using two-dimensional J-resolved PRESS at 3TMagnetic Resonance Imaging in Medicine 2005; 53:1177-82.

73. Tang X, Yang Y, Kim W, Wang Q, Qi P, Dai H, Xing L. Measurement of ionizing radiation using carbon nanotube-field effect transistorPhysics in Medicine and Biology 2005; 50(3):N23-N31.

72. Shou Z, Yang Y, Cotrutz C, Levy D, Xing L. Quantification of the a priori dosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution spacePhysics in Medicine and Biology 2005; 50:1469-82. 

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71. Yang Y, Xing L. Clinical knowledge-based inverse treatment planningPhys Med Biol 2004; 49(22):5105-17.

70. Schreibmann E, Xing L. Feasibility study of beam orientation class-solutions for prostate IMRTMedical Physics 2004; 31(10):2863-70.

69. Yong Y, Xing L. Inverse planning with adaptively evolving voxel dependent penalty scheme. Medical Physics 2004; 31(10):2819-44.

68. Lian J, Xing L, Hunjan S, Dumoulin C, Levin J, Lo A, Watkins R, Rohling K, Giaquinto R, Kim D, Spielman D, Daniel B. Mapping of the prostate in endorectal coil-based MRI/MRSI and CT: a deformable registration and validation studyMedical Physics 2004; 31(11): 3087-94.

67. Lian J, Xing L. Incorporating model parameter uncertainties into inverse treatment planningMedical Physics 2004; 31(9):2711-20.

66. Schreibmann E, Lahanas M, Xing L, Baltas D. Multiobjective evolutionary optimization of number of beams, their orientation and weights for IMRT. Physics in Medicine and Biology 2004; 49(5):737-70.

65. Galvin JM, Ezzell G, Eisbrauch A, Yu C, Butler B, Xiao Y, Rosen I, Rosenman J, Sharpe M,Xing L, Xia P, Lomax T, Low DA, Palta J: Implementing IMRT in clinical practice: a joint document of the American Society for Therapeutic Radiology and Oncology and the Americn Association of Physicists in MedicineInternational Journal of Radiation Oncology, Biology and Physics 2004; 58(5):1616-34.

64. Yang Y, Xing L: Quantitative measurement of MLC leaf displacements using an electronic portal image devicePhysics in Medicine and Biology, 49(8):1521-1533.

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63. Yang Y, Xing L, Li JG, Palta J, Chen Y, Luxton G, Boyer A. Independent dosimetric calculation with inclusion of head scatter and MLC transmission for IMRT. Med Phys 2003; 30(11):2937-47.

62. Cotrutz C, Xing L. Segment-based dose optimization using a genetic algorithmPhysics in Medicine and Biology 2003; 48(18): 2987-98. 

61. Ezzell G, Low D, Palta J, Rosen I, Sharpe M, Xia P, Xiao Y, Xing L, Yu C, Galvin J: Guidance Document on Delivery, Treatment Planning, and clinical Implementation of IMRT: Report of the IMRT Subcommittee of the AAPM Radiation Therapy CommitteeMedical Physics 2003; 30(8):2089-115.

60. Hunjan S, Adalsteinsson E, Kim D, Harsh GR, Boyer A, Spielman D, Xing L. Quality assurance of magnetic resonance spectroscopic imaging-derived metabolic dataInternational Journal of Radiation Oncology, Biology and Physics 2003; 57(4):1159-73.

59. Crooks S, Wu X, Takita C, Watzich M, Xing L. Aperture Modulated Arc TherapyPhysics in Medicine and Biology 2003; 48(10):1333-44.

58. Yang Y, Xing L. Incorporating Leaf Transmission and Head Scatter Corrections into Step-and-Shoot Leaf Sequences for IMRTInternational Journal of Radiation Oncology, Biology and Physics 2003; 55(4):1121-34. 

57. Cotrutz C, Xing L. IMRT Dose Shaping with Regionally Variable Penalty SchemeMedical Physics 2003; 30(4):544-51.

56. Lian J, Cotrutz C, Xing L. Therapeutic treatment plan optimization with probability density-based dose prescriptionMedical Physics 2003; 30(4):655-66.

55. Yang Y, Xing L. Using the Volumetric Effect of a Finite-Sized Detector for Routine Quality Assurance of MLC Leaf PositioningMedical Physics 2003; 30(3):433-42.

54. Zhang P, Wu J, Dean D, Xing L, Xue J, Maciunas R, Sibata C. Plug Pattern Optimization for Gamma Knife Radiosurgery Treatment PlanningInternational Journal of Radiation Oncology, Biology and Physics 2003; 55(2):420-27. 

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53. Pugachev A, Xing L. Incorporating prior knowledge into beam orientation optimization in IMRTInt J Radiat Oncol Bio Phys 2002; 54(5):1565-74.

52. Xing L, Cotrutz C, Hunjun S, Boyer AL, Adalsteinsson E, Spielman D. Inverse planning for functional image-guided IMRTPhysics in Medicine and Biology 2002; 47(20):3567-78.

51. Crooks S, Pugachev A, King C, Xing L. Examination of the effect of increasing the number of radiation beams on a radiation treatment planPhysics in Medicine and Biology 2002; 47(19):3485-501.

50. Chen Z, Xing L, Nath R. Independent monitor unit calculation for intensity modulated radiotherapy using the MIMiC multileaf collimatorMedical Physics 2002; 29(9):2041-51.

49. Yang Y, Xing L, Boyer A, Song Y, Hu YM. A three-source model for the calculation of head scatter factorsMedical Physics 2002; 29(9):2024-33.

48. Crooks SM, McAven LF, Robinson DF, Xing L. Minimizing delivery time and monitor units in static IMRT by leaf-sequencingPhysics in Medicine and Biology 2002; 47(17):3105-16.

47. Cotrutz C, Xing L. Using voxel-dependent importance factors for interactive DVH-based dose optimizationPhysics in Medicine and Biology 2002; 47(10):1659-69.

46. Crooks S, Xing L. Application of constrained least-squares techniques to IMRT treatment planningInternational Journal of Radiation Oncology, Biology and Physics 2002; 54(4):1217-24.

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45. Pugachev A, Xing L. Pseudo beam's-eye-view as applied to beam orientation selection in intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 2001; 51(5):1361-70.

44. Beavis AW, Ganney PS, Whitton VJ, Xing L. Optimization of the step-and-shoot leaf sequence for delivery of intensity modulated radiation therapy using a variable division schemePhysics in Medicine and Biology 2001; 46(9):2457-65.

43. Pugachev A, Xing L. Computer-Assisted Selection of Coplanar Beam Orientations in IMRTPhysics in Medicine and Biology 2001; 46(9):2467-76.

42. Pugachev A, Li J, Boyer AL, Hancock S, Le QT, Donaldson SS, Xing L. Role of Beam Orientation Optimization in IMRTInternational Journal of Radiation Oncology, Biology and Physics 2001; 50(2): 551-60.

41. Crooks S, Xing L. Linear algebraic methods applied to intensity modulated radiation therapyPhysics in Medicine and Biology 2001; 46(10): 2587-2605.

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2000 and before

40. Chen Y, Boyer AL, Xing L. A dose-volume histogram based optimization algorithm for ultrasound guided prostate implants. Medical Physics 2000; 27(10):2286-92. 

39. Xing L, Li JG. Computer Verification of Fluence Map for intensity modulated radiation therapy. Medical Physics 2000; 27(9): 2084-92.

38. Ma CM, Pawlicki T, Jiang S, Li J, Deng J, Mok E, Kapur A, Xing L, Ma L, Boyer A. Monte Carlo Verification of IMRT Dose Distributions from a Commercial Treatment Planning Optimization SystemPhysics in Medicine and Biology 2000; 45(9):2483-95.

37. Li JG, Williams SS, Goffinet DR, Boyer AL, Xing L. Breast-Conserving Radiation Therapy Using Combined Electron and IMRT TechniqueRadiotherapy Oncology 2000; 56(1):65-71.

36. Xing L, Lin Z, Donaldson SS, Le QT, Tate D, Goffinet DR, Wolden S, Ma L, Boyer AL. Dosimetric effects of patient displacement and collimator and gantry angle misalignment on intensity modulated radiation therapyRadiotherapy Oncology 2000; 56(1):97-108.

35. Li JG, Xing L. Inverse Planning Incorporating Organ MotionMedical Physics 2000; 27(7):1573-8, 2000

34. Pugachev A, Boyer AL, Xing L. Beam orientation optimization in intensity-modulated radiation treatment planningMedical Physics 2000; 27(6):1238-45.

33. Xing L, Chen Y, Luxton G, Li JG, Boyer AL. Monitor unit calculation for an intensity modulated photon field by a simple scatter-summation algorithmPhys Med Biol 2000; 45(3):N1-7.

32. Li JG, Boyer AL, Xing L. Clinical Implementation of Wedge Filter Optimization in Three Dimensional Radiotherapy Treatment PlanningRadiotherapy and Oncology 1999; 53(3):257-64.

31. Li JG, Xing L, Boyer AL, Hamilton R, Spelbring D, Turian J. Matching Photon and Electron Fields with Dynamic Intensity ModulationMedical Physics 1999; 26(11):2379-84.

30. Xing L, Li JG, Donaldson S, Le QT, Boyer AL. Optimization of Importance Factors in Inverse Planning. Phys Med Biol 1999; 44(10):2525-36.

29. Xing L, Li J, Pugachev A, Le QT, Boyer AL. Estimation Theory and Model Parameter in Selection for Therapeutic Plan OptimizationMedical Physics 1999; 26(11):2348-58.

28. Ma L, Ma CM, Boyer AL, Xing L. Synchronizing dynamic multileaf collimators for producing two-dimensional intensity-modulated fields with minimum beam delivery time. International Journal of Radiation Oncology, Biology and Physics 1999; 44(5):1147-54.

27. Xing L, Curran B, Holmes T, Hill RW, Ma L, Forster KM, Boyer AL. Dosimetric verification of a commercial inverse treatment planning systemPhysics in Medicine and Biology 1999; 44(2):463-78.

26. Xia P, Geis P, Xing L, Ma CM, Findley D, Forster KM, Boyer AL. Physical characteristics of a miniature multileaf collimatorMedical Physics 1999; 26(1):65-70.

25. Boyer A, Xing L, Ma CM, Curran B, Hill R, Kania A, Bleier. Theoretical Considerations of Monitor Unit Calculations for Intensity Modulated Beam Treatment Planning. Medical Physics 1999; 26(2):187-95.

24. Ma L, Boyer AL, Xing L, Ma CM. The Optimized Leaf Setting Algorithm for Beam Intensity Modulation Using Dynamic Multileaf CollimatorsPhysics in Medicine and Biology 1998; 43(6):1629-43.

23. Xing L, Hamilton RJ, Spelbring D, Pelizzari C, Chen GT, Boyer AL. Fast iterative algorithms for three-dimensional inverse treatment planningMedical Physics 1998; 25(10):1845-9.

22. Xing L, Hamilton RJ, Pelizzari C, Chen GT. A three-dimensional algorithm for optimizing beam weights and wedge filtersMedical Physics 1998; 25(10): 1858-65.

21. Xing L, Pelizzari C, Kuchnir FT, Chen GT. Optimization of Relative Weights and Wedge Angles in Treatment Planning. Medical Physics 1997; 24(2):215-21.

20. Xing L, Chen GT. Iterative Methods for Inverse Treatment PlanningPhysics in Medicine and Biology 1996; 41(10):2107-23.

19. Xing L, Chang YC. Nuclear Spin-Lattice Relaxation Induced by ThermallyFluctuating Flux Lines. Physical Review Letters 1994; 73:488-91.

18. Kim J, Xiao JQ, Chien CL, Tesanovic Z, Xing L. A Model for Giant Magnetoresistance in Magnetic Granular Solids. Solid State Communications 1994; 89:157-161.

17. Kim JH, Vagner I, Xing L. Phonon-Assisted Mechanism for Quantum Nuclear Spin Relaxation. Physical Review 1994; B49: 16777-80.

16. Shi J, Kita E, Xing L, Salamon MB. Magnetothermopower and Magnetoresistance in Co/Al Granular Solids. Physical Review 1993; B48: 16119-22.

15. Xing L, Chang YC, Salamon MB, Shi J, Frenkel DM, Lu JP. Magneto-Transport Properties of Magnetic Granular Systems. Physical Review 1993; B48:(Rapid Communications) 6728-6731.

14. Shi J, Parkin SSP, Xing L, Salamon MB. Giant Magnetoresistance and Magnetothermopower in Co/Cu Multilayers. Journal of Magnetism and Magnetic Materials 1993;125: L251-6.

13. Xing L, Chang YC. Theory of Giant Magnetoresistance of Magnetic Granular Solid. Physical Review 1993; B48: 4156-9.

12. Tesanovic Z, Xing L, Bulaevskii L, Li Q, Suenaga M. Critical Fluctuations in the Thermodynamics of Quasi-Two-Dimensional Type-II Superconductors. Physical Review Letters 1992; 69:3563-6.

11. Xing L. Reentrance of a Flux Liquid Near Hc1 in Superconductors. Physical Review 1992; B46:11084-91.

10. Xing L. A Thermodynamic Description of the Irreversibility Line in Strongly Type-II Superconductors. Zeitschrift für Physik 1992; B88:303-7.

9. Xing L, Tesanovic Z: Dense Vortex Plasma in Type-II Superconductors. Physica 1992; C196:241-45.

8. Tesanovic Z, Xing L. Critical Fluctuations in Strongly Type-II Quasi Two-Dimensional Superconductors. Physical Review Letters 1991; 67:2729-2732.

7. Tesanovic Z, Rasolt M, Xing L. Superconductivity in a Very High Magnetic Field. Physical Review 1991; 43:288-298.

6. Tesanovic Z, Rasolt M, Xing L. On Superconductivity in a Very High Magnetic Field.  Phys Review Letters, 66 1991: 843-4.

5. Xing L. Monte Carlo Simulations of a 2D Hard-Disk Boson System. Physical Review 1990; B42:8426-30.

4. Xing L. Tesanovic Z: Transition Between Flux Liquid and Flux Solid in High-Tc Superconductors. Physical Review Letters 1990; 65:794-7.

3. Tesanovic Z, Rasolt M, Xing L. Quantum Limit of a Flux Lattice: Superconductivity and Magnetic Field in a New Relationship. Physical Review Letters 1989; 63: 2425-2428.

2. Xing L, Macek J. Dynamical Theory of the Conversion Among the Multipoles in the Collisions of Hydrogenlike Species with Ions. Physical Review 1989; A39:545-553.

1. Judd BR, Xing L. Two-Electron Crystal-Field Matrix Elements for Half-Filled Shells. Journal of Physics C: Solid State Physics 1988; 21: 4071-4081.

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Associate Editor, Medical Physics Journal (2003 - 2008) Member of international advisory board, Physics in Medicine and Bilogy (2008 - Present) Member of Clinical Research and Cancer Epidemiology (CCE) Committee, American Cancer Society (2006 - Present) Member of ZRG1 (Quick Trials on Imaging and Image-Guided Intervention) section, National Institute of Health (2008 - Present) Director of Medical Physics Division, Department of Radiation Oncology, Stanford University (2009 - Present) Director of Radiation Physics Division, Department of Radiation Oncology, Stanford University (2010 - Present) Member of Senior Editorial Board, American Journal of Nuclear Medicine and Molecular Imaging (2010 - Present) Member of Editorial Board, Journal of Gastrointestinal Oncology (2010 - Present)


  • Multi-Domain Image Completion for Random Missing Input Data IEEE TRANSACTIONS ON MEDICAL IMAGING Shen, L., Zhu, W., Wang, X., Xing, L., Pauly, J. M., Turkbey, B., Harmon, S., Sanford, T., Mehralivand, S., Choyke, P. L., Wood, B. J., Xu, D. 2021; 40 (4): 1113–22


    Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

    View details for DOI 10.1109/TMI.2020.3046444

    View details for Web of Science ID 000637532800002

    View details for PubMedID 33351753

  • Prior-image-based CT reconstruction using attenuation mismatched prior. Physics in medicine and biology Zhang, H., Capaldi, D. P., Zeng, D., Ma, J., Xing, L. 2021


    Prior-image-based reconstruction (PIBR) methods are powerful in reducing radiation dose and improving image quality for low-dose CT. Besides anatomical changes, the prior and current images can also have different attenuation due to different scanners or the same scanner but with different x-ray beam quality (e.g., kVp setting, beam filtration) during data acquisitions. PIBR is challenged in such scenarios with attenuation mismatched prior. In this work, we investigate a specific PIBR method, called statistical image reconstruction using normal dose image induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation mismatched prior and achieve quantitative low-dose CT imaging. We proposed two corrective schemes for the original SIR-ndiNLM method, 1) a global histogram matching approach and 2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validated the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to emulate attenuation mismatches. Meanwhile, we utilized different CT slices to emulate anatomical mismatches/changes between the prior and the current low-dose images. We observed that the original SIR-ndiNLM introduces artifacts to the reconstruction when using attenuation mismatched prior. Furthermore, we found that larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our proposed two corrective schemes enabled SIR-ndiNLM to effectively handle attenuation mismatch and anatomical changes between two images and successfully eliminate the artifacts. We demonstrated that the proposed techniques permit SIR-ndiNLM to leverage the attenuation mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.

    View details for DOI 10.1088/1361-6560/abe760

    View details for PubMedID 33596553

  • Modularized Data-Driven Reconstruction Framework for Non-ideal Focal Spot Effect Elimination in Computed Tomography. Medical physics Zhang, Z., Yu, L., Zhao, W., Xing, L. 2021


    PURPOSE: High-performance computed tomography (CT) plays a vital role in clinical decision making. However, the performance of CT imaging is adversely affected by the non-ideal focal spot size of the X-ray source or degraded by an enlarged focal spot size due to aging. In this work, we aim to develop a deep learning-based strategy to mitigate the problem so that high spatial resolution CT images can be obtained even in the case of a non-ideal X-ray source.METHODS: To reconstruct high-quality CT images from blurred sinograms via joint image and sinogram learning, a cross-domain hybrid model is formulated via deep learning into a modularized data-driven reconstruction (MDR) framework. The proposed MDR framework comprises several blocks, and all the blocks share the same network architecture and network parameters. In essence, each block utilizes two sub-models to generate an estimated blur kernel and a high-quality CT image simultaneously. In this way, our framework generates not only a final high-quality CT image but also a series of intermediate images with gradually improved anatomical details, enhancing the visual perception for clinicians through the dynamic process. We used simulated training datasets to train our model in an end-to-end manner and tested our model on both simulated and realistic experimental datasets.RESULTS: On the simulated testing datasets, our approach increases the information fidelity criterion (IFC) by up to 34.2%, the universal quality index (UQI) by up to 20.3%, the signal-to-noise (SNR) by up to 6.7%, and reduces the root mean square error (RMSE) by up to 10.5% as compared with FBP. Compared with the iterative deconvolution method (NSM), MDR increases IFC by up to 24.7%, UQI by up to 16.7%, SNR by up to 6.0%, and reduces RMSE by up to 9.4%. In the modulation transfer function (MTF) experiment, our method improves the MTF50% by 34.5% and MTF10% by 18.7% as compared with FBP, Similarly remarkably, our method improves MTF50% by 14.3% and MTF10% by 0.9% as compared with NSM. Also, our method shows better imaging results in the edge of bony structures and other tiny structures in the experiments using phantom consisting of ham and a bottle of peanuts.CONCLUSIONS: A modularized data-driven CT reconstruction framework is established to mitigate the blurring effect caused by a non-ideal X-ray source with relatively large focal spot. The proposed method enables us to obtain high-resolution images with less ideal X-ray source.

    View details for DOI 10.1002/mp.14785

    View details for PubMedID 33595900

  • Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network. Medical image analysis Lyu, T., Zhao, W., Zhu, Y., Wu, Z., Zhang, Y., Chen, Y., Luo, L., Li, S., Xing, L. 2021; 70: 102001


    Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data. We demonstrate the feasibility of the approach with two independent cohorts (the first cohort including contrast-enhanced DECT scans of 5753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients) and show its superior performance on DECT applications. The deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.

    View details for DOI 10.1016/

    View details for PubMedID 33640721

  • Automated Contour Propagation of the Prostate From pCT to CBCT Images Via Deep Unsupervised Learning. Medical physics Liang, X., Bibault, J., Leroy, T., Escande, A., Zhao, W., Chen, Y., Buyyounouski, M. K., Hancock, S. L., Bagshaw, H., Xing, L. 2021


    PURPOSE: To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT).METHODS: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow band mapping to augment the conventional strategy. 251 anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two Groups. Group one contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group two contained 9 CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSC), the Hausdorff distances, and the distances of the center-of-mass.RESULTS: The average DSCs between DUL-based prostate contours and reference contours for test data in Group one and Group two-consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively.CONCLUSIONS: This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

    View details for DOI 10.1002/mp.14755

    View details for PubMedID 33544390

  • Multicellular spheroids as in vitro models of oxygen depletion during FLASH irradiation. International journal of radiation oncology, biology, physics Khan, S., Bassenne, M., Wang, J., Manjappa, R., Melemenidis, S., Breitkreutz, D. Y., Maxim, P. G., Xing, L., Loo, B. W., Pratx, G. 2021


    PURPOSE: The differential response of normal and tumor tissues to ultra-high dose rate radiation (FLASH) has raised new hope for treating solid tumors but, to date, the mechanism remains elusive. One leading hypothesis is that FLASH radiochemically depletes oxygen from irradiated tissues faster than it is replenished through diffusion. The purpose of this study is to investigate these effects within hypoxic multicellular tumor spheroids, through simulations and experiments.MATERIALS AND METHODS: Physicobiological equations were derived to model (i) the diffusion and metabolism of oxygen within spheroids; (ii) its depletion through reactions involving radiation-induced radicals; and (iii) the increase in radioresistance of spheroids, modeled according to the classical oxygen enhancement ratio and linear-quadratic response. These predictions were then tested experimentally in A549 spheroids exposed to electron irradiation at conventional (0.075 Gy/s) or FLASH (90 Gy/s) dose rates. Clonogenic survival, cell viability, and spheroid growth were scored post-radiation. Clonogenic survival of two other cell lines was also investigated.RESULTS: The existence of a hypoxic core in unirradiated tumor spheroids is predicted by simulations and visualized by fluorescence microscopy. Upon FLASH irradiation, this hypoxic core transiently expands, engulfing a large number of well-oxygenated cells. In contrast, oxygen is steadily replenished during slower conventional irradiation. Experimentally, clonogenic survival was around 3-fold higher in FLASH-irradiated spheroid compared to conventional irradiation, but no significant difference was observed for well-oxygenated 2D-cultured cells. This differential survival is consistent with the predictions of the computational model. FLASH irradiation of spheroids resulted in a dose-modifying factor of around 1.3 for doses above 10 Gy.CONCLUSION: Tumor spheroids can be used as a model to study FLASH irradiation in vitro . The improved survival of tumor spheroids receiving FLASH radiation confirms that ultra-fast radiochemical oxygen depletion and its slow replenishment are critical components of the FLASH effect.

    View details for DOI 10.1016/j.ijrobp.2021.01.050

    View details for PubMedID 33545301

  • Closing the Gap Between Deep Neural Network Modeling and Biomedical Decision-Making Metrics in Segmentation via Adaptive Loss Functions IEEE TRANSACTIONS ON MEDICAL IMAGING Seo, H., Bassenne, M., Xing, L. 2021; 40 (2): 585–93


    Deep learning is becoming an indispensable tool for various tasks in science and engineering. A critical step in constructing a reliable deep learning model is the selection of a loss function, which measures the discrepancy between the network prediction and the ground truth. While a variety of loss functions have been proposed in the literature, a truly optimal loss function that maximally utilizes the capacity of neural networks for deep learning-based decision-making has yet to be established. Here, we devise a generalized loss function with functional parameters determined adaptively during model training to provide a versatile framework for optimal neural network-based decision-making in small target segmentation. The method is showcased by more accurate detection and segmentation of lung and liver cancer tumors as compared with the current state-of-the-art. The proposed formalism opens new opportunities for numerous practical applications such as disease diagnosis, treatment planning, and prognosis.

    View details for DOI 10.1109/TMI.2020.3031913

    View details for Web of Science ID 000615044900012

    View details for PubMedID 33074800

    View details for PubMedCentralID PMC7858236

  • Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images IEEE TRANSACTIONS ON MEDICAL IMAGING Yu, L., Zhang, Z., Li, X., Xing, L. 2021; 40 (1): 228–38


    Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.

    View details for DOI 10.1109/TMI.2020.3025064

    View details for Web of Science ID 000604883800020

    View details for PubMedID 32956044

  • MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy. Medical physics Chen, Y. n., Xing, L. n., Yu, L. n., Liu, W. n., Fahimian, B. P., Niedermayr, T. n., Bagshaw, H. P., Buyyounouski, M. n., Han, B. n. 2021


    Propagation of contours from high-quality magnetic resonance (MR) images to treatment planning ultrasound (US) images with severe needle artifacts is a challenging task, which can greatly aid the organ contouring in high dose rate (HDR) prostate brachytherapy. In this study, a deep learning approach was developed to automatize this registration procedure for HDR brachytherapy practice.Because of the lack of training labels and difficulty of accurate registration from inferior image quality, a new segmentation-based registration framework was proposed for this multi-modality image registration problem. The framework consisted of two segmentation networks and a deformable registration network, based on the weakly-supervised registration strategy. Specifically, two 3D V-Nets were trained for the prostate segmentation on the MR and US images separately, to generate the weak supervision labels for the registration network training. Besides the image pair, the corresponding prostate probability maps from the segmentation were further fed to the registration network to predict the deformation matrix, and an augmentation method was designed to randomly scale the input and label probability maps during the registration network training. The overlap between the deformed and fixed prostate contours was analyzed to evaluate the registration accuracy. Three datasets were collected from our institution for the MR and US image segmentation networks, and the registration network learning, which contained 121, 104 and 63 patient cases, respectively.The mean Dice similarity coefficient (DSC) results of the two prostate segmentation networks are 0.86±0.05 and 0.90±0.03, for MR images and the US images after the needle insertion, respectively. The mean DSC, center-of-mass (COM) distance, Hausdorff distance (HD) and averaged symmetric surface distance (ASSD) results for the registration of manual prostate contours were 0.87±0.05, 1.70±0.89 mm, 7.21±2.07 mm, 1.61±0.64 mm, respectively. By providing the prostate probability map from the segmentation to the registration network, as well as applying the random map augmentation method, the evaluation results of the four metrics were all improved, such as an increase of DSC from 0.83±0.08 to 0.86±0.06 and from 0.86±0.06 to 0.87±0.05, respectively.A novel segmentation-based registration framework was proposed to automatically register prostate MR images to the treatment planning US images with metal artifacts, which not only largely saved the labor work on the data preparation, but also improved the registration accuracy. The evaluation results showed the potential of this approach in HDR prostate brachytherapy practice.

    View details for DOI 10.1002/mp.14901

    View details for PubMedID 33905566

  • Prior-image-based CT reconstruction using attenuation-mismatched priors. Physics in medicine and biology Zhang, H. n., Capaldi, D. n., Zeng, D. n., Ma, J. n., Xing, L. n. 2021; 66 (6): 064007


    Prior-image-based reconstruction (PIBR) methods are powerful tools for reducing radiation doses and improving the image quality of low-dose computed tomography (CT). Apart from anatomical changes, prior and current images can also have different attenuations because they originated from different scanners or from the same scanner but with different x-ray beam qualities (e.g., kVp settings, beam filters) during data acquisition. In such scenarios, with attenuation-mismatched priors, PIBR is challenging. In this work, we investigate a specific PIBR method, called statistical image reconstruction, using normal-dose image-induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation-mismatched priors and achieve quantitative low-dose CT imaging. We propose two corrective schemes for the original SIR-ndiNLM method, (1) a global histogram-matching approach and (2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validate the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to simulate attenuation mismatches. Meanwhile, we utilize different CT slices to simulate anatomical mismatches or changes between the prior and the current low-dose image. We observe that the original SIR-ndiNLM introduces artifacts to the reconstruction when an attenuation-mismatched prior is used. Furthermore, we find that a larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our two proposed corrective schemes enable SIR-ndiNLM to effectively handle the attenuation mismatch and anatomical changes between the two images and successfully eliminate the artifacts. We demonstrate that the proposed techniques permit SIR-ndiNLM to leverage the attenuation-mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.

    View details for DOI 10.1088/1361-6560/abe760

    View details for PubMedID 33729997