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)


  • Geometry and statistics-preserving manifold emb e dding for nonlinear dimensionality reduction PATTERN RECOGNITION LETTERS Islam, M., Xing, L. 2021; 151: 155-162
  • Small field measurement and monte carlo model validation of a novel image-guided radiotherapy system. Medical physics Shi, M., Chuang, C. F., Kovalchuk, N., Bush, K. K., Zaks, D., Xing, L., Surucu, M., Han, B. 2021


    PURPOSE: The RefleXionTM X1 is a novel radiotherapy system that is designed for image-guided radiotherapy and, eventually, biology-guided radiotherapy (BgRT). BgRT is a treatment paradigm that tracks tumor motion using real-time positron emission signals. This study reports the small field measurement results and the validation of a Monte Carlo (MC) model of the first clinical RefleXion unit.METHODS: The RefleXion linear accelerator (linac) produces a 6 MV flattening filter free (FFF) photon beam and consists of a binary multi-leaf collimator (MLC) system with 64 leaves and two pairs of y-jaws. The maximum clinical field size achievable is 400 * 20 mm2 . The y-jaws provide either a 10 mm or 20 mm opening at source-to-axis distance (SAD) of 850 mm. The width of each MLC leaf at SAD is 6.25 mm. Percentage depth doses (PDDs) and relative beam profiles were acquired using an Edge diode detector in a water tank for field sizes from 12.5 * 10 mm2 to 100 * 20 mm2 . Beam profiles were also measured using films. Output factors of fields ranging from 6.25 * 10 mm2 to 100 * 20 mm2 were measured using W2 scintillator detector, Edge detector, and films. Output correction factors k of the Edge detector for RefleXion were calculated. A MC model of the linac including pre-MLC beam sources and detailed structures of MLC and lower y-jaws was validated against the measurements. Simulation codes BEAMnrc and GATE were utilized.RESULTS: The diode measured PDD at 10 cm depth (PDD10) increases from 53.6% to 56.9% as the field opens from 12.5 * 10 mm2 to 100 * 20 mm2 . The W2-measured output factor increases from 0.706 to 1 as the field opens from 6.25 * 10 mm2 to 100 * 20 mm2 (reference field size). The output factors acquired by diode and film differ from the W2 results by 1.65% (std = 1.49%) and 2.09% (std = 1.41%) on average, respectively. The profile penumbra and full width half maximum (FWHM) measured by diode agree well with the film results with a deviation of 0.60 mm and 0.73% on average, respectively. The averaged beam profile consistency calculated between the diode and film measured profiles among different depths is within 1.72%. By taking the W2 measurements as the ground truth, the output correction factors k for Edge detector ranging from 0.958 to 1 were reported. For the MC model validation, the simulated PDD10 agreed within 0.6% to the diode measurement. The MC simulated output factor differed from the W2 results by 2.3% on average (std = 3.7%) while the MC simulated beam penumbra differed from the diode results by 0.67 mm on average (std = 0.42 mm). The MC FWHM agreed with the diode results to within 1.40% on average. The averaged beam profile consistency calculated between the diode and MC profiles among different depths is less than 1.29%.CONCLUSIONS: This study represents the first small field dosimetry of a clinical RefleXion system. A complete and accurate MC model of the RefleXion linac has been validated. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15273

    View details for PubMedID 34628666

  • Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Huang, C., Yang, Y., Panjwani, N., Boyd, S., Xing, L. 2021; 68 (10): 2907-2917


    Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front.Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM).On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk.Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners.Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.

    View details for DOI 10.1109/TBME.2021.3055822

    View details for Web of Science ID 000697820800006

    View details for PubMedID 33523802

  • Deep learning-augmented radioluminescence imaging for radiotherapy dose verification. Medical physics Jia, M., Yang, Y., Wu, Y., Li, X., Xing, L., Wang, L. 2021


    PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.METHODS: The CRIS consists of a cylindrical chamber coated with scintillator material on the inner surface of the cylinder, coupled with a hemispherical mirror and a digital camera at the two ends. After training, the deep learning model is used for image-to-dose conversion to provide absolute dose prediction at multiple depths of a specific water phantom from a single CRIS image under the assumption of a good consistency between the TPS setting and actual beam energy. The model was trained using a set of captured radioluminescence images and the corresponding dose maps from the clinical treatment planning system (TPS) for the sake of acceptable data collection. To overcome the latent error and inconsistency that exists between the TPS calculation and the corresponding measurement, the model was trained in an unsupervised manner. Validation experiments were performed on five square fields (ranging from 2 * 2 cm2 to 10 * 10 cm2 ), and three clinical IMRT cases. The results were compared to the TPS calculations in terms of gamma index at 1.5 cm, 5 cm and 10 cm depths.RESULTS: The mean 2% / 2mm gamma pass rates were 100% for square fields and 97.2% (range from 95.5% to 99.5%) for the IMRT fields. Further validations were performed by comparing the CRIS results with measurements on various regular fields. The results show a mean gamma pass rate of 91% (1% / 1mm) for cross-profiles and a mean percentage deviation of 1.15% for percentage depth doses (PDDs).CONCLUSIONS: The system is capable of converting the irradiated radioluminescence image to corresponding water-based dose maps at multiple depths with a spatial resolution comparable to the TPS calculations. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15229

    View details for PubMedID 34523131

  • Fully automated noncoplanar radiation therapy treatment planning. Medical physics Huang, C., Yang, Y., Xing, L. 2021


    PURPOSE: To perform fully automated noncoplanar treatment planning, we propose a method called NC-POPS to produce noncoplanar (NC) plans using the Pareto Optimal Projection Search (POPS) algorithm.METHODS: Noncoplanar radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planner's active treatment planning time and remove inter-planner variability. Our NC-POPS algorithm extends the original POPS algorithm to the noncoplanar setting with potential applications to both IMRT and VMAT. The proposed algorithm consists of two main parts: 1) noncoplanar beam angle optimization (BAO) and 2) fully automated inverse planning using the POPS algorithm.RESULTS: We evaluate the performance of NC-POPS by comparing between various noncoplanar and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram (DVH) statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity.CONCLUSIONS: Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of noncoplanar IMRT cases with the potential to substantially improve treatment planning workflow and plan quality. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15223

    View details for PubMedID 34519064

  • Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy. Medical physics Jia, M., Wu, Y., Yang, Y., Wang, L., Chuang, C., Han, B., Xing, L. 2021


    PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).METHODS: A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations.RESULTS: Validation experiments were performed using five regular fields and three clinical IMRT cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to corresponding TPS calculation in terms of the 3D gamma index. The mean 2% / 2mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields.CONCLUSIONS: The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15218

    View details for PubMedID 34519365

  • Rotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis IEEE TRANSACTIONS ON MEDICAL IMAGING Li, X., Hu, X., Qi, X., Yu, L., Zhao, W., Heng, P., Xing, L. 2021; 40 (9): 2284-2294


    The automatic diagnosis of various conventional ophthalmic diseases from fundus images is important in clinical practice. However, developing such automatic solutions is challenging due to the requirement of a large amount of training data and the expensive annotations for medical images. This paper presents a novel self-supervised learning framework for retinal disease diagnosis to reduce the annotation efforts by learning the visual features from the unlabeled images. To achieve this, we present a rotation-oriented collaborative method that explores rotation-related and rotation-invariant features, which capture discriminative structures from fundus images and also explore the invariant property used for retinal disease classification. We evaluate the proposed method on two public benchmark datasets for retinal disease classification. The experimental results demonstrate that our method outperforms other self-supervised feature learning methods (around 4.2% area under the curve (AUC)). With a large amount of unlabeled data available, our method can surpass the supervised baseline for pathologic myopia (PM) and is very close to the supervised baseline for age-related macular degeneration (AMD), showing the potential benefit of our method in clinical practice.

    View details for DOI 10.1109/TMI.2021.3075244

    View details for Web of Science ID 000692208500009

    View details for PubMedID 33891550

  • Metal artifact reduction in 2D CT images with self-supervised cross-domain learning. Physics in medicine and biology Yu, L., Zhang, Z., Li, X., Ren, H., Zhao, W., Xing, L. 2021


    The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need for anatomically identical CT image pairs (\ie, metal artifact-corrupted CT image and metal artifact-free CT image) for network learning, we propose a self-supervised cross-domain learning framework. Specifically, we train a neural network to restore the metal trace region values in the given metal-free sinogram, where the metal trace is identified by the forward projection of metal masks. We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images. To preserve the fine structure details and fidelity of the final MAR image, instead of directly adopting CNN-refined images as output, we incorporate the metal trace replacement into our framework and replace the metal-affected projections of the original sinogram with the prior sinogram generated by the forward projection of the CNN output. We then use the filtered backward projection (FBP) algorithms for final MAR image reconstruction. We conduct an extensive evaluation on simulated and real artifact data to show the effectiveness of our design. Our method produces superior MAR results and outperforms other compelling methods. We also demonstrate the potential of our framework for other organ sites.

    View details for DOI 10.1088/1361-6560/ac195c

    View details for PubMedID 34330119

  • Noise2Context: Context-assisted Learning 3D Thin-layer for Low Dose CT. Medical physics Zhang, Z., Liang, X., Zhao, W., Xing, L. 2021


    PURPOSE: Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of X-ray radiation exposure attract more and more attention. To lower the X-ray radiation, low-dose CT (LDCT) has been widely adopted in certain scenarios, while it will induce the degradation of CT image quality. In this paper, we proposed a deep learning-based method that can train denoising neural networks without any clean data.METHODS: In this work, for 3D thin-slice LDCT scanning, we first drive an unsupervised loss function which was equivalent to a supervised loss function with paired noisy and clean samples when the noise in the different slices from a single scan was uncorrelated and zero-mean. Then, we trained the denoising neural network to map one noise LDCT image to its two adjacent LDCT images in a single 3D thin-layer LDCT scanning, simultaneously. In essence, with some latent assumptions, we proposed an unsupervised loss function to train the denoising neural network in an unsupervised manner, which integrated the similarity between adjacent CT slices in 3D thin-layer LDCT.RESULTS: Further experiments on Mayo LDCT dataset and a realistic pig head were carried out. In the experiments using Mayo LDCT dataset, our unsupervised method can obtain performance comparable to that of the supervised baseline. With the realistic pig head, our method can achieve optimal performance at different noise levels as compared to all the other methods that demonstrated the superiority and robustness of the proposed Noise2Context.CONCLUSIONS: In this work, we present a generalizable LDCT image denoising method without any clean data. As a result, our method not only gets rid of the complex artificial image priors but also amounts of paired high-quality training datasets.

    View details for DOI 10.1002/mp.15119

    View details for PubMedID 34287948

  • Detection of Carotid Artery Stenosis with Intraplaque Hemorrhage and Neovascularization Using a Scanning Interferometer. Nano letters Zaman, R. T., Kosuge, H., Gambhir, S. S., Xing, L. 2021


    Carotid artery stenosis (CAS) is a major cause of stroke or transient ischemic attack (TIA, mini-stroke) in the United States. Carotid endarterectomy (CEA), a surgical procedure, is used to treat CAS. According to the American Heart Association, 1 out of 5 patients underwent CEA inappropriately, which was most commonly due to apparent overestimation of stenosis severity, and half had uncertain indicators. The current imaging modalities are limited in providing critical information on carotid arterial plaque content, extent, and biology. To circumvent these limitations, we developed a sensing interferometer (SI) imaging system to assess vulnerable carotid plaques noninvasively to detect stenosis, neovascularization, and intraplaque hemorrhage (IPH). We have custom-built a SI prototype and its peripheral systems with back-mode-projection capability. We detected stenosis, neo-vessels, and IPH through SI imaging system in in vivo mice carotid atherosclerotic plaques and further verified the same plaques ex vivo through a histology scope, CRi Maestro, and histological analysis.

    View details for DOI 10.1021/acs.nanolett.1c01441

    View details for PubMedID 34156253