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 (2022 - Present) 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)


  • Operator splitting for adaptive radiation therapy with nonlinear health dynamics OPTIMIZATION METHODS & SOFTWARE Fu, A., Xing, L., Boyd, S. 2022
  • Treatment planning system commissioning of the first clinical biology-guided radiotherapy machine. Journal of applied clinical medical physics Simiele, E., Capaldi, D., Breitkreutz, D., Han, B., Yeung, T., White, J., Zaks, D., Owens, M., Maganti, S., Xing, L., Surucu, M., Kovalchuk, N. 2022: e13638


    PURPOSE: The RefleXion X1 is a novel radiotherapy machine designed for image-guided radiotherapy (IGRT) and biology-guided radiotherapy (BgRT). Its treatment planning system (TPS) generates IMRT and SBRT plans for a 6MV-FFF beam delivered axially via 50 firing positions with the couch advancing every 2.1mm. The purpose of this work is to report the TPS commissioning results for the first clinical installation of RefleXion X1.METHODS: CT images of multiple phantoms were imported into the RefleXion TPS to evaluate the accuracy of data transfer, anatomical modeling, plan evaluation, and dose calculation. Comparisons were made between the X1, Eclipse, and MIM. Dosimetric parameters for open static fields were evaluated in water and heterogeneous slab phantoms. Representative clinical IMRT and SBRT cases were planned and verified with ion chamber, film, and ArcCHECK@ measurements. The agreement between TPS and measurements for various clinical plans was evaluated using Gamma analysis with a criterion of 3%/2mm for ArcCHECK@ and film. End-to-end (E2E) testing was performed using anthropomorphic head and lung phantoms.RESULTS: The average difference between the TPS-reported and known HU values was -1.4 ± 6.0 HU. For static fields, the agreements between the TPS-calculated and measured PDD10 , crossline profiles, and inline profiles (FWHM) were within 1.5%, 1.3%, and 0.5mm, respectively. Measured output factors agreed with the TPS within 1.3%. Measured and calculated dose for static fields in heterogeneous phantoms agreed within 2.5%. The ArcCHECK@ mean absolute Gamma passing rate was 96.4% ± 3.4% for TG 119 and TG 244 plans and 97.8% ± 3.6% for the 21 clinical plans. E2E film analysis showed 0.8mm total targeting error for isocentric and 1.1mm for off-axis treatments.CONCLUSIONS: The TPS commissioning results of the RefleXion X1 TPS were within the tolerances specified by AAPM TG 53, MPPG 5.a, TG 119, and TG 148. A subset of the commissioning tests has been identified as baseline data for an ongoing QA program.

    View details for DOI 10.1002/acm2.13638

    View details for PubMedID 35644039

  • Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study. The Lancet. Digital health Jiang, Y., Zhang, Z., Yuan, Q., Wang, W., Wang, H., Li, T., Huang, W., Xie, J., Chen, C., Sun, Z., Yu, J., Xu, Y., Poultsides, G. A., Xing, L., Zhou, Z., Li, G., Li, R. 2022; 4 (5): e340-e350


    BACKGROUND: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment. We aimed to develop predictive models for peritoneal recurrence and prognosis in gastric cancer.METHODS: In this retrospective multi-institution study of 2320 patients, we developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. Patients in the training cohort (n=510) and the internal validation cohort (n=767) were recruited from Southern Medical University, Guangzhou, China. Patients in the external validation cohort (n=1043) were recruited from Sun Yat-sen University Cancer Center, Guangzhou, China. We evaluated the prognostic accuracy of the model as well as its association with chemotherapy response. Furthermore, we assessed whether the model could improve the ability of clinicians to predict peritoneal recurrence.FINDINGS: The deep learning model had a consistently high accuracy in predicting peritoneal recurrence in the training cohort (area under the receiver operating characteristic curve [AUC] 0·857; 95% CI 0·826-0·889), internal validation cohort (0·856; 0·829-0·882), and external validation cohort (0·843; 0·819-0·866). When informed by the artificial intelligence (AI) model, the sensitivity and inter-rater agreement of oncologists for predicting peritoneal recurrence was improved. The model was able to predict disease-free survival in the training cohort (C-index 0·654; 95% CI 0·616-0·691), internal validation cohort (0·668; 0·643-0·693), and external validation cohort (0·610; 0·583-0·636). In multivariable analysis, the model predicted peritoneal recurrence and disease-free survival independently of clinicopathological variables (p<0·0001 for all). For patients with a predicted high risk of peritoneal recurrence and low survival, adjuvant chemotherapy was associated with improved disease-free survival in both stage II disease (hazard ratio [HR] 0·543 [95% CI 0·362-0·815]; p=0·003) and stage III disease (0·531 [0·432-0·652]; p<0·0001). By contrast, chemotherapy had no impact on disease-free survival for patients with a predicted low risk of peritoneal recurrence and high survival. For the remaining patients, the benefit of chemotherapy depended on stage: only those with stage III disease derived benefit from chemotherapy (HR 0·637 [95% CI 0·484-0·838]; p=0·001).INTERPRETATION: The deep learning model could allow accurate prediction of peritoneal recurrence and survival in patients with gastric cancer. Prospective studies are required to test the clinical utility of this model in guiding personalised treatment in combination with clinicopathological criteria.FUNDING: None.

    View details for DOI 10.1016/S2589-7500(22)00040-1

    View details for PubMedID 35461691

  • Beam commissioning of the first clinical biology-guided radiotherapy system. Journal of applied clinical medical physics Han, B., Capaldi, D., Kovalchuk, N., Simiele, E., White, J., Zaks, D., Xing, L., Surucu, M. 2022: e13607


    This study reports the beam commissioning results for the first clinical RefleXion Linac.METHODS: The X1 produces a 6MV photon beam and the maximum clinical field size is 40*2cm2 at source-to-axis distance of 85cm. Treatment fields are collimated by a binary multileaf collimator (MLC) system with 64 leaves with width of 0.625cm and y-jaw pairs to provide either a 1 or 2cm opening. The mechanical alignment of the radiation source, the y-jaw, and MLC were checked with film and ion chambers. The beam parameters were characterized using a diode detector in a compact water tank. In-air lateral profiles and in-water percentage depth dose (PDD) were measured for beam modeling of the treatment planning system (TPS). The lateral profiles, PDDs, and output factors were acquired for field sizes from 1.25*1 to 40*2cm2 field to verify the beam modeling. The rotational output variation and synchronicity were tested to check the gantry angle, couch motion, and gantry rotation.RESULTS: The source misalignments were 0.049mm in y-direction, 0.66% out-of-focus in x-direction. The divergence of the beam axis was 0.36mm with a y-jaw twist of 0.03°. Clinical off-axis treatment fields shared a common center in y-direction were within 0.03mm. The MLC misalignment and twist were 0.57mm and 0.15°. For all measured fields ranging from the size from 1.25*1 to 40*2cm2 , the mean difference between measured and TPS modeled PDD at 10cm depth was -0.3%. The mean transverse profile difference in the field core was -0.3%±1.1%. The full-width half maximum (FWHM) modeling was within 0.5mm. The measured output factors agreed with TPS within 0.8%.CONCLUSIONS: This study summarizes our specific experience commissioning the first novel RefleXion linac, which may assist future users of this technology when implementing it into their own clinics.

    View details for DOI 10.1002/acm2.13607

    View details for PubMedID 35482018

  • Implicit neural representation for radiation therapy dose distribution. Physics in medicine and biology Vasudevan, V., Shen, L., Huang, C., Chuang, C. F., Islam, M. T., Ren, H., Yang, Y., Dong, P., Xing, L. 2022


    OBJECTIVE: Dose distribution data plays a pivotal role in radiotherapy treatment planning. The data is typically represented using voxel grids, and its size ranges from 10^6--10^8. A concise representation of the treatment plan is of great value in facilitating treatment planning and downstream applications. This work aims to develop an implicit neural representation of 3D dose distribution data.APPROACH: Instead of storing the dose values at each voxel, in the proposed approach, the weights of a multilayer perceptron (MLP) are employed to characterize the dosimetric data for plan representation and subsequent applications. We train a coordinate-based MLP with sinusoidal activations to map the voxel spatial coordinates to the corresponding dose values. We identify the best architecture for a given parameter budget and use that to train a model for each patient. The trained MLP is evaluated at each voxel location to reconstruct the dose distribution. We perform extensive experiments on dose distributions of prostate, spine, and head and neck tumor cases to evaluate the quality of the proposed representation. We also study the change in representation quality by varying model size and activation function.MAIN RESULTS: Using coordinate-based MLPs with sinusoidal activations, we can learn implicit representations that achieve a mean-squared error of 10^{-6} and peak signal-to-noise ratio greater than 50 dB at a target bitrate of ~1 across all the datasets, with a compression ratio of ~32. Our results also show that model sizes with a bitrate of 1--2 achieve optimal accuracy. For smaller bitrates, performance starts to drop significantly.SIGNIFICANCE: The proposed model provides a low-dimensional, implicit, and continuous representation of 3D dose data. In summary, given a dose distribution, we systematically show how to find a compact model to fit the data accurately. This study lays the groundwork for future applications of neural representations of dose data in radiation oncology.

    View details for DOI 10.1088/1361-6560/ac6b10

    View details for PubMedID 35477171

  • Meta-optimization for fully automated radiation therapy treatment planning. Physics in medicine and biology Huang, C., Nomura, Y., Yang, Y., Xing, L. 2022


    OBJECTIVE: Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propose a meta-optimization framework, called MetaPlanner (MP).APPROACH: Our MP algorithm automates planning by performing meta-optimization of treatment planning hyperparameters. The algorithm uses a derivative-free method (i.e. parallel Nelder-Mead simplex search) to search for weight configurations that minimize a meta-scoring function. Meta-scoring is performed by constructing a tier list of the relevant considerations (e.g. dose homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical decision-making process. Additionally, we have made our source code publicly available via github.MAIN RESULTS: The proposed MP method is evaluated on two datasets (21 prostate cases and 6 head and neck cases) collected as part of clinical workflow. MP is applied to both IMRT and VMAT planning and compared to a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has comparable or better performance than manual VMAT planning for all evaluated metrics.SIGNIFICANCE: Our proposed MP provides a general framework for fully automated treatment planning that produces high quality treatment plans. Our MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality.

    View details for DOI 10.1088/1361-6560/ac5672

    View details for PubMedID 35176734

  • Dose Prediction for Cervical Cancer Brachytherapy Using 3-D Deep Convolutional Neural Network IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES Ma, M., Kidd, E., Fahimian, B. P., Han, B., Niedermayr, T. R., Hristov, D., Xing, L., Yang, Y. 2022; 6 (2): 214-221
  • Novel-view X-ray projection synthesis through geometry-integrated deep learning. Medical image analysis Shen, L., Yu, L., Zhao, W., Pauly, J., Xing, L. 2022; 77: 102372


    X-ray imaging is a widely used approach to view the internal structure of a subject for clinical diagnosis, image-guided interventions and decision-making. The X-ray projections acquired at different view angles provide complementary information of patient's anatomy and are required for stereoscopic or volumetric imaging of the subject. In reality, obtaining multiple-view projections inevitably increases radiation dose and complicates clinical workflow. Here we investigate a strategy of obtaining the X-ray projection image at a novel view angle from a given projection image at a specific view angle to alleviate the need for actual projection measurement. Specifically, a Deep Learning-based Geometry-Integrated Projection Synthesis (DL-GIPS) framework is proposed for the generation of novel-view X-ray projections. The proposed deep learning model extracts geometry and texture features from a source-view projection, and then conducts geometry transformationon the geometry features to accommodate the change of view angle.At the final stage, the X-ray projection in the target view is synthesized from the transformed geometry and the shared texture features via an image generator. The feasibility and potential impact of the proposed DL-GIPS model are demonstrated using lung imaging cases.The proposed strategy can be generalized to a general case of multiple projections synthesis from multiple input views and potentially provides a new paradigm for various stereoscopic and volumetric imaging with substantially reduced efforts in data acquisition.

    View details for DOI 10.1016/

    View details for PubMedID 35131701

  • Attention-guided deep learning for gestational age prediction using fetal brain MRI. Scientific reports Shen, L., Zheng, J., Lee, E. H., Shpanskaya, K., McKenna, E. S., Atluri, M. G., Plasto, D., Mitchell, C., Lai, L. M., Guimaraes, C. V., Dahmoush, H., Chueh, J., Halabi, S. S., Pauly, J. M., Xing, L., Lu, Q., Oztekin, O., Kline-Fath, B. M., Yeom, K. W. 1800; 12 (1): 1408


    Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81-0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.

    View details for DOI 10.1038/s41598-022-05468-5

    View details for PubMedID 35082346

  • Mechanoporation enables rapid and efficient radiolabeling of stem cells for PET imaging. Scientific reports Jung, K. O., Theruvath, A. J., Nejadnik, H., Liu, A., Xing, L., Sulchek, T., Daldrup-Link, H. E., Pratx, G. 2022; 12 (1): 2955


    Regenerative medicine uses the patient own stem cells to regenerate damaged tissues. Molecular imaging techniques are commonly used to image the transplanted cells, either right after surgery or at a later time. However, few techniques are fast or straightforward enough to label cells intraoperatively. Adipose tissue-derived stem cells (ADSCs) were harvested from knee joints of minipigs. The cells were labeled with PET contrast agent by flowing mechanoporation using a microfluidic device. While flowing through a series of microchannels, cells are compressed repeatedly by micro-ridges, which open transient pores in their membranes and induce convective transport, intended to facilitate the transport of 68Ga-labeled and lipid-coated mesoporous nanoparticles (MSNs) into the cells. This process enables cells to be labeled in a matter of seconds. Cells labeled with this approach were then implanted into cartilage defects, and the implant was imaged using positron emission tomography (PET) post-surgery. The microfluidic device can efficiently label millions of cells with 68Ga-labeled MSNs in as little as 15 min. The method achieved labeling efficiency greater than 5 Bq/cell on average, comparable to 30 min-long passive co-incubation with 68Ga-MSNs, but with improved biocompatibility due to the reduced exposure to ionizing radiation. Labeling time could also be accelerated by increasing throughput through more parallel channels. Finally, as a proof of concept, ADSCs were labeled with 68Ga-MSNs and quantitatively assessed using clinical PET/MR in a mock transplant operation in pig knee joints. MSN-assisted mechanoporation is a rapid, effective and straightforward approach to label cells with 68Ga. Given its high efficiency, this labeling method can be used to track small cells populations without significant effects on viability. The system is applicable to a variety of cell tracking studies for cancer therapy, regenerative therapy, and immunotherapy.

    View details for DOI 10.1038/s41598-022-06938-6

    View details for PubMedID 35194089