Artificial Intelligence in Medical Imaging and Medical Physics

Wang T, Lei Y, Fu Y, Liu T and Yang X*. " A review on medical imaging synthesis using deep learning and its clinical applications.” Journal of Applied Clinical Medical Physics, 22(1):11-36, 2021.

Liu R, Lei Y, Wang T, Zhou J, Roper J, Lin L, McDonald MW, Bradley JD, Curran WJ, Liu T, and Yang X*. “Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN.” Physics in Medicine and Biology, 66(6):065014, 2021.

Lei Y, Harms J, Wang T, Liu Y, Shu H, Jani A, Curran W, Mao H, Liu T and Yang X*. “MRI-Only Based Synthetic CT Generation Using Dense Cycle Consistent Generative Adversarial Networks,” Medical Physics, 46(8), 3565-3583, 2019. (Selected and featured on the journal cover of Medical Physics)

Lei Y, Harms J, Wang H, Tian S, Zhou J, Shu H, Zhong J, Mao H, Curran W, Liu T and Yang X*. “MRI-based Synthetic CT Generation Using Semantic Random Forest with Iterative Refinement," Physics in Medicine and Biology, 5, 64(8):085001. 2019.

Liu Y, Lei Y, Wang Y, Shafai-Erfani G, Wang T, Tian S, Pretesh P, Ashesh J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Evaluation of a Deep Learning-based Pelvic Synthetic CT Generation Technique for MRI-based Prostate Proton Treatment Planning,” Physics in Medicine and Biology, 64(20), 205022, 2019.

Yang X, Lei Y, Shu HK, Rossi P, Mao H, Shim H, Curran WJ, Liu T. Pseudo CT Estimation from MRI Using Patch-based Random Forest. Proc. of SPIE. 10133:101332Q. doi: 10.1117/12.2253936. PMID: 31607771; PMCID: PMC6788808.

Lei Y, Shu H, Liu T, Shim H, Mao H, Tian S, Jeong J, Jani A, Curran W and Yang X*. "MRI-based Pseudo CT using Anatomic Signature and Jointly Dictionary Learning", Journal of Medical Imaging, 5(3), 034001, 2018.

Lei Y, Shu H, Liu T, Mao H, Shim H, Curran W and Yang X*. "A Leaning-based Approach to Derive Electron Density from MRI Using Random Forest and Iterative Refinement Model", Journal of Medical Imaging, 5(4), 043504, 2018.

Wang T, Manohar N, lei Y, Dhabaan A, Shu H, Liu T, Curran W and Yang X*. "MRI-Based Treatment Planning for Brain Stereotactic Radiosurgery: Dosimetric Validation of a Learning-Based Pseudo-CT Generation Method", Medical Dosimetry, 44 (3), 199-204, 2019.

Liu Y, Lei Y, Wang, Wang T, Ren L, Lin L, McDonald M, Curran, W, Liu T, Zhou J, Yang X*. “MRI-based Treatment Planning for Proton Radiotherapy: Dosimetric Validation of a Deep Learning-based Liver Synthetic CT Generation Method,” Physics in Medicine and Biology, 64(14):14505, 2019.

Liu Y, Lei Y, Wang T, Kayode O, tian S, Liu T, Patel P, Curran W, Ren L and Yang X*. “MRI-based Treatment Planning for Liver Stereotactic Body Radiotherapy: Validation of a Deep Learning-based Synthetic CT Generation Method,” The British Journal of Radiology, 92, 20190067, 2019.

Shafai-Erfani G, Lei Y, Liu Y, Wang Y, Wang T, Zhong J, Liu T, McDonald M, Curran W, Zhou J, Shu HK and Yang X*. “MRI-based Proton Treatment Planning for Base of Skull Tumors,” International Journal of Particle Therapy, 6(2), 12-25. 2019.

Lei Y, Tang X, Liu T, Jwoong J, Higgins K, Dhabaan A, Wang H, Press R, Curran W and Yang X*. "Learning‐based CBCT Correction Using Alternating Random Forest Based on Auto‐Context Model", Medical Physics, 46(2), 601-6182018. (Selected and Featured in the journal cover and Editor’s Choice)

Wang T, Lei Y, Manohar N, Tian S, Jani A, Shu H, Higgins K, Dhabaan A, Patel P, Tang X, Liu T, Curran W, and Yang X*. “Dosimetric Study on Learning-Based Cone-Beam CT Correction in Adaptive Radiation Therapy," Medical Dosimetry, 19, 300342, 2019.

Harms J, Lei Y, Wang T, Zhang R, Zhou J, Tang X, Curran W, Liu T, and Yang X*. “Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography,” Medical Physics, 46(7), 3325-3337, 2019.

Liu Y, Lei Y, Wang T, Fu Y, Tang X, Curran W, Liu T, Patel P and Yang X*. “CBCT-based Synthetic CT Generation using Self-attention CycleGAN for Pancreatic Adaptive Radiotherapy,” Medical Physics, 2020.

Lei Y, Fu Y, Harms J, Wang T, Curran W, Liu T, Higgins K and Yang X*. CBCT-Based Synthetic MRI Generation for CBCT-Guided Adaptive Radiotherapy. In: Nguyen D., Xing L., Jiang S. (eds) Artificial Intelligence in Radiation Therapy. AIRT 2019. Lecture Notes in Computer Science, vol 11850. Springer, Cham.

Lei Y, Dong X, Tian Z, Liu Y, Wang T, Tian S, Patel P, Jani A, Curran W, Mao H, Liu T and Yang X*. “CT Prostate Segmentation Based on Synthetic MRI-aided Deep Attention Fully Convolution Network,” Medical Physics, 47(1), 530-540, 2020. 

Dong X, Lei Y, Liu Y, Wang T, Tian S, Jani A, Patel P, Shuster D, Curran W, Liu T and Yang X*. "Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network," Radiotherapy and Oncology, 141, 192-199 2019.

Lei Y, Wang T, Tian S, Dong X, Jani A, Schuster D, Curran W, Patel P, Liu T and Yang X*. "Male Pelvic Multi-Organ Segmentation Aided by CBCT-based Synthetic MRI," Physics in Medicine and Biology, 65(3), 035013, 2020.

Fu Y, Lei Y, Fu Y, Wang T, Tian S, Patel P, Jani A, Curran W, Liu T and Yang X*. “Pelvic Multi-organ Segmentation on CBCT for Prostate Adaptive Radiotherapy," Medical Physics, 47(8):3415-3422, 2020.

Liu Y, Lei Y, Fu Y, Wang T, Zhou J, Jiang X, McDonald M, Beitler J, Curran W, Liu T and Yang X*. “Head and Neck Multi-Organ Auto-Segmentation on CT Images Aided by Synthetic MRI," Medical Physics, 47(9):4294-4302, 2020.

Dai X, Lei Y, Wang T, McDonald M, Beilter J, Curran W, Zhou J, Liu T and Yang X*. “Synthetic MRI-aided Head-and-Neck Organs-at-Risk Auto-Delineation for CBCT-guided Adaptive Radiotherapy,” Physics in Medicine and Biology, 66(4):045021, 2021.

Yang X*, Wang T, Lei Y, Kristin H, Liu T, Shim H, Curran W, Mao H, and Jonathon N. " MRI-based Attenuation Correction for Brain PET/MRI based on Anatomic Signature and Machine Learning", Physics in Medicine and Biology, 64 (2), 025001, 2019.

Wang T, Lei Y, Fu Y, Curran W, Liu T and Yang X*. “Machine Learning in Quantitative PET: A Review of Attenuation Correction and Low-count Image Reconstruction Methods,” European Journal of Medical Physics (Physica Medica), 76, 294-306, 2020.

Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J and Yang X*. “Whole-body PET Estimation from Low Count Statistics using 3D Cycle-Consistent Generative Adversarial Networks,” Physics in Medicine and Biology, 64(21): 215017, 2019.

Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J and Yang X*. “Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging,” Physics in Medicine and Biology, 64(21): 215016, 2019.

Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J and Yang X*. “Deep Learning-based Attenuation Correction in the Absence of Structural Information for Whole-body PET imaging,” Physics in Medicine and Biology, 65 (5), 036015, 2020. (Jack Krohmer Junior Investigator Competition Winner (1/174) at annual meeting of AAPM in 2019)

Dai X, Lei Y, Fu Y, Curran W, Liu T, Mao H and Yang X*. "Multimodal MRI Synthesis Using Unified Generative Adversarial Networks," Medical Physics, 47(12):6343-6354, 2020.

Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J and Yang X*. "MRI-aided Attenuation Correction for PET imaging with Deep Learning," Proc. of SPIE, 11317, 1131723, 2020.

Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran W, Mao H, Nye J and Yang X*. "Low Dose PET Imaging Based with CT-aided Cycle-consistent Adversarial Networks," Proc. of SPIE, 11312, 1131247, 2020.

Lei Y, Fu Y, Liu T, Curran W, Mao H and Yang X*. "Multi-Modality MRI Arbitrary Transformation Using Unified Generative Adversarial Networks," Proc. of SPIE, 11313, 1131303, 2020.

He X, Lei Y, Fu Y, Mao H, Curran W, Liu T* and Yang X*. "Super-Resolution Magnetic Resonance Imaging Reconstruction Using Deep Attention Networks," Proc. of SPIE, 11313, 113132J, 2020.

Charyyev S, Wang T, Lei Y, Ghavidel B, Beitler J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy,” arXiv preprint arXiv:2005.12908, 2020.

Wang T, Lei Y, Tian Z, Liu Y, Jiang X, Curran W, Liu T and Yang X*. “Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy,” Journal of Medical Imaging, 6(4):043504, 2019.

Harms J, Lei Y, Wang T, Ghavidel B, Stokers W, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Cone-beam CT-derived relative stopping power map generation via deep learning for adaptive proton radiotherapy," Medical Physics, 47(9), 4416-4427, 2020.

Wang T, Lei Y, Harms J, Ghavidel B, Lin L, Beitler J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. "Learning-Based Stopping Power Mapping on Dual Energy CT for Proton Radiation Therapy," International Journal of Particle Therapy, 7(3):46-60, 2021.

Lei Y, Higgins K, Zhou Z, Dong X, Shim H, Liu T, Dhabaan A, Curran W and Yang X*. “High-resolution CT Image Retrieval Using Sparse Convolutional Neural Network.” Proc. of SPIE, 10573-10573F-8, 2018.

Xie H, Lei Y, Wang T, Tian Z, Roper J, Bradley JD, Curran WJ, Tang X, Liu T and Yang X*. “High through-plane resolution CT imaging with self-supervised deep learning,” Physics in Medicine and Biology, 66(14), 145013, 2021.

Zhou B, Yang X, Curran W and Liu T. "Artificial Intelligence in Quantitative Ultrasound Imaging: A Review,” Journal of Ultrasound in Medicine, 2021 (In press)

Dai X, Lei Y, Wang T, Axente M, Xu D, Patel P, Jani A, Curran WJ, Liu T, Yang X*. “Self-supervised Learning for Accelerated 3D High-resolution Ultrasound Imaging.” Medical Physics, 48(7):3916-3926, 2021.

Fu Y, Lei Y, Wang T, Curran W, Liu T and Yang X*. ‘A Review of Deep Learning based Methods for Medical Image Multi-Organ Segmentation,” European Journal of Medical Physics (Physica Medica), 85, 107-122, 2021.

Yang X, Wu N, Cheng G, Zhou Z, Yu D, Beitler J, Curran W and Liu T, " Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-And-Neck Radiation Therapy.” International Journal of Radiation Oncology • Biology • Physics (IJROBP), 90(5), 1225-1233, 2014.

Zhang Y, He X, Lei Y, Wang T, Mao H, Jani A, Patel P, Curran W, Liu T and Yang X*. “Multi-needle Detection in 3D Ultrasound Images Using Unsupervised Order-graph Regularized Sparse Dictionary Learning,” IEEE Transactions on Medical Imaging, 39(7), 2302-2315, 2020.

Dong X, Lei Y, Liu Y, Wang T, Tian S, Jani A, Patel P, Shuster D, Curran W, Liu T and Yang X*. “Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network,” Radiotherapy and Oncology, 141, 192-199 2019.

Dong X, Lei Y, Wang T, Thomas M, Tang L, Curran W, Liu T and Yang X*. “Automatic Multi-Organ Segmentation in Thorax CT Images Using U-Net-GAN," Medical Physics, 46(5):2157-2168, 2019. (Editor’s Choice)

Wang T, Lei Y, Tang H, He Z, Castillo R, Wang C, Li D, Higgins K, Liu T, Curran W, Zhou W* and Yang X*. “A Learning-based Automatic Segmentation and Quantification Method on Left Ventricle in Gated Myocardial Perfusion SPECT Imaging: A Feasibility Study," Journal Nuclear Cardiology, 1-12, 2019.

Wang B, Lei Y, Tian S, Wang T, Liu Y, Pretesh P, Jani A, Mao H, Curran W, Liu T and Yang X*. “Deeply Supervised 3D FCN with Group Dilated Convolution for Automatic MRI Prostate Segmentation" Medical Physics, 46(4):1707-1718, 2019.

Wang T, Lei Y, Tian S, Jiang X, Zhou J, Dresser S, Walter C, Shu K, Yang X*. “Learning-based Automatic Segmentation of Arteriovenous Malformations on Contrast CT Images in Brain Stereotactic Radiosurgery,” Medical Physics, 46(5):3133-3141, 2019.

Lei Y, Tian S, He X, Wang T, Wang B, Pretesh P, Jani A, Mao H, Curran W, Liu T and Yang X*. “Ultrasound Prostate Segmentation Based on Multi-Directional Deeply Supervised V-Net," Medical Physics, 46(6):3194-3206, 2019.

Lei Y, Dong X, Tian Z, Liu Y, Wang T, Tian S, Patel P, Jani A, Curran W, Mao H, Liu T and Yang X*. “CT Prostate Segmentation Based on Synthetic MRI-aided Deep Attention Fully Convolution Network,” Medical Physics, 47(1), 530-540, 2020. 

Lei Y, Wang T, Tian S, Dong X, Jani A, Schuster D, Curran W, Patel P, Liu T and Yang X*. “Male Pelvic Multi-Organ Segmentation Aided by CBCT-based Synthetic MRI,” Physics in Medicine and Biology, 65(3), 035013, 2020.

Fu Y, Lei Y, Fu Y, Wang T, Tian S, Patel P, Jani A, Curran W, Liu T and Yang X*. “Pelvic Multi-organ Segmentation on CBCT for Prostate Adaptive Radiotherapy," Medical Physics, 47(8):3415-3422, 2020.

Liu Y, Lei Y, Fu Y, Wang T, Tang X, Jiang X, Curran W, Liu T, Patel P and Yang X*. “CT-based Multi-Organ Segmentation Using a Deep-learning-based Network for Pancreatic Radiotherapy,” Medical Physics, 47(9), 4316-4324, 2020.

Lei Y, Wang T, Tian S, Fu Y, Patel P, Jani A, Curran W, Liu T and Yang X*. “Male Pelvic CT Multi-organ Segmentation using Synthetic MRI-aided Dual Pyramid Networks,” Physics in Medicine and Biology, 66(8): 085007 2021.

He X, Guo B, Lei Y, Wang T, Fu Y, Curran W, Zhang L, Liu T* and Yang X*. "Automatic Segmentation and Quantification of Epicardial Adipose Tissue from Coronary Computed Tomography Angiography," Physics in Medicine and Biology, 65(9):095012 2020.

Zhang Y, Lei Y, Qiu R, Wang T, Wang H, Jani A, Curran W, Patel P, Liu T and Yang X*. “Multi-needle Localization with Total Variation Regularized Deep Supervised Attention U-Net in Ultrasound-guided HDR Prostate Brachytherapy," Medical Physics, 47(7):2735-2745, 2020.

Guo B, He X, Lei Y, Harms J, Wang T, Curran W, Liu T, Zhang L and Yang X*. “Automated Left Ventricular Myocardium Segmentation Using 3D Deeply Supervised Attention U-Net for Coronary Computed Tomography Angiography,” Medical Physics, 47(4):1775-1785, 2020.

Lei Y, Fu Y, Justin R, Kristin H, Curran W, Liu T and Yang X*. “Echocardiographic Image Multi-Structure Segmentation using Cardiac-SegNet,” Medical Physics, 48(5): 2426-2437 2021.

Harms J, Lei Y, Tian S, McCall N, Higgins K, Bradley J, Curran WJ, Liu T, Yang X*. “Automatic Delineation of Cardiac Substructures using a Region-Based Fully Convolutional Network.” Medical Physics, 48(6):2867-2876, 2021.

He X, Guo B, Lei Y, Wang H, Curran W, Zhang L, Liu T and Yang X*. “Automatic Quantification of Left Ventricle Myocardium and Epicardial Adipose Tissue from Coronary Computed Tomography Angiography: A Multicenter Study,” European Radiology, 31 (6), 3826-3836, 2021.

Lei Y, Guo B, Fu Y, Wang T, Liu T, Curran W, Zhang L and Yang X*. "Automated coronary artery segmentation in Coronary Computed Tomography Angiography (CCTA) using deep learning neural networks.” Proc. of SPIE, 11318, 1131812-19, 2020.

Lin M, Momin S, Lei Y, Wang H, Curran WJ, Liu T, Yang X*. “Fully Automated Segmentation of Brain Tumor from Multiparametric MRI Using 3D Context Deep Supervised U-Net,” Medical Physics, 48(8):4365-4374, 2021.

Momin S, Lei Y, Tian Z, Wang T, Roper J, Kesarwala A, Higgins K, Bradley j, Liu T and Yang X*. “Lung Tumor Segmentation in 4D CT Images Using Motion Convolutional Neural Networks,” Medical Physics, 2021. (In press)

Fu Y, Lei Y, Wang T, Curran W, Liu T and Yang X*. “Deep Learning in Medical Image Registration: A Review,” Physics in Medicine and Biology, 65 (20), 20TR01, 2020.

Lei Y, Fu Y, Wang T, Liu Y, Higgins K, Curran W, Liu T and Yang X*. “4D-CT Deformable Image Registration Using Multiscale Unsupervised Deep Learning,” Physics in Medicine and Biology, 65(8), 085003, 2020.

Fu Y, Lei Y, Wang T, Liu Y, Higgins K, Bradley J, Curran W, Liu T and Yang X*. “LungRegNet: An Unsupervised Deformable Image Registration Method for 4D-CT Lung,” Medical Physics, 2020. 47 (4):1763-1774, 2020. (Editor’s Choice)

Lei Y, Fu Y, Harms J, Wang T, Curran W, Liu T, Higgins K and Yang X*. 4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network. In: Nguyen D., Xing L., Jiang S. (eds) Artificial Intelligence in Radiation Therapy. AIRT 2019. Lecture Notes in Computer Science, vol 11850. Springer, Cham.

Fu Y, Lei Y, Liu Y, Wang T, Curran W, Liu T, Patel P and Yang X*. "Cone-beam Computed Tomography (CBCT) and CT Image Registration Aided by CBCT-based Synthetic CT," Proc. of SPIE, 11313, 113132U-9U, 2020.

Fu Y, Lei Y, Zhou J, Wang T, Yu D, Beitler J, Curran W, Liu T and Yang X*. "Synthetic CT-aided MRI-CT Image Registration for Head and neck Radiotherapy," Proc. of SPIE, 11313, 113132U-9U, 2020.

Lei Y, Wang T, Tian S, Dong X, Jani A, Schuster D, Curran W, Patel P, Liu T and Yang X*. "Synthetic MRI-aided Pelvic Multi-organ Segmentation in Cone-beam Computed Tomography," Proc. of SPIE, 11313, 1131338-45, 2020.

Fu Y, Lei Y, Zhou J, Wang T, Jani A, Patel P, Mao H, Curran W, Liu T and Yang X*. "Non-rigid MRI-CT Image Registration with Unsupervised Deep Learning-based Deformation Prediction," Proc. of SPIE, 11313, 1131329-36, 2020.

Zeng Q, Fu Y, Tian Z, Lei Y, Zhang Y, Wang T, Wang H, Mao H, Liu T, Curran W, Jani A, Patel P and Yang X*. “Label-Driven MRI-US Registration Using Weakly-Supervised Learning for MRI-guided Prostate Radiotherapy," Physics in Medicine and Biology, 65(13):135002, 2020.

Fu Y, Lei Y, Wang T, Patel P, Jani A, Mao H, Curran W, Liu T and Yang X*. "Biomechanically Constrained Non-rigid MR-TRUS Prostate Registration using Deep Learning based 3D Point Cloud Matching," Medical Image Analysis, 67,101845, 2021.

Fu Y, Wang T, Lei Y, Patel P, Jani A, Curran W, Liu T and Yang X*. “Deformable MR-CBCT Prostate Registration using Biomechanically Constrained Deep Learning Networks,” Medical Physics, 48(1):253-263, 2021.

Jeong J, Press B, Wang L, Shu H, Liu T, Walter C, Mao H and Yang X*. “Machine-learning Based Classification of Glioblastoma Using Delta-Radiomic Features Derived from Dynamic Susceptibility Contrast Enhanced MR Images,” Quantitative Imaging in Medicine and Surgery, 9(7), 1201-1213, 2019.

Jeong J, Ali A, Liu T, Mao H, Swartz H, Curran W and Yang X*. "Radiomics in Cancer Radiotherapy: A Review," arXiv: 1910.02102, 2019.

He X, Guo B, Wang T, Lei Y, Liu T Curran W, Zhang L and Yang X*. "Benign and Malignant Thyroid Classification Using Computed Tomography Radiomics," Proc. of SPIE, 11314, 1131440, 2020.

Cui G, Jeong J, Press B, Lei Y, Shu HK, Liu T, Curran W, Mao H and Yang X*. "Machine-learning-based Classification of Lower-grade gliomas and High-grade gliomas using Radiomic Features in Multi-parametric MRI," arXiv: 1911.10145, 2019.

Guo B, He X, Wang T, Lei Y, Liu T, Curran W, Zhang L and Yang X*. "Classification of Lesion Specific Myocardial Ischemia Using Cardiac Computed Tomography Radiomics," Proc. of SPIE, 11314, 113143P, 2020.

Liu Y, Lei Y, Wang, Wang T, Ren L, Lin L, McDonald M, Curran, W, Liu T, Zhou J, Yang X*. “MRI-based Treatment Planning for Proton Radiotherapy: Dosimetric Validation of a Deep Learning-based Liver Synthetic CT Generation Method,” Physics in Medicine and Biology, 64(14):14505, 2019.

Shafai-Erfani G, Lei Y, Liu Y, Wang Y, Wang T, Zhong J, Liu T, McDonald M, Curran W, Zhou J, Shu HK and Yang X*. “MRI-based Proton Treatment Planning for Base of Skull Tumors,” International Journal of Particle Therapy, 6(2), 12-25. 2019.

Liu Y, Lei Y, Wang Y, Shafai-Erfani G, Wang T, Tian S, Pretesh P, Ashesh J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Evaluation of a Deep Learning-based Pelvic Synthetic CT Generation Technique for MRI-based Prostate Proton Treatment Planning,” Physics in Medicine and Biology, 64(20), 205022, 2019.

Wang T, Lei Y, Harms J, Liu Y, Ghavidel B, Lin L, Beitler J, Curran W, Liu T, Zhou J and Yang X*. "Stopping Power Map Estimation from Dual-Energy CT Using Deep Learning-Based Method," Proc. of SPIE, 11312, 113124M-30M, 2020.

Harms J, Lei Y, Wang T, Ghavidel B, Stokers W, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Cone-beam CT-derived relative stopping power map generation via deep learning for adaptive proton radiotherapy," Medical Physics, 47(9), 4416-4427, 2020.

Wang T, Lei Y, Harms J, Ghavidel B, Lin L, Beitler J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. "Learning-Based Stopping Power Mapping on Dual Energy CT for Proton Radiation Therapy," International Journal of Particle Therapy, 7(3):46-60, 2021.

Charyyev S, Lei Y, Harms J, Zhou J, Eaton B, McDonald M, Curran W, Liu T, Zhang R and Yang X*. "Learning-Based Dual Energy CT Imaging from Single Energy CT for Proton Radiation Therapy," Biomedical Physics & Engineering Express, 6(3), 035029, 2020.

Charyyev S, Wang T, Lei Y, Ghavidel B, Beitler J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy,” arXiv preprint arXiv:2005.12908, 2020.

Liu R, Lei Y, Wang T, Zhou J, Roper J, Lin L, McDonald MW, Bradley JD, Curran WJ, Liu T, Yang X*. “Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN.” Physics in Medicine and Biology, 66(6):065014, 2021.

Momin S, Fu Y, Lei Y, Roper J, Bradley J, Curran W, Liu T and Yang X*. Knowledge-based Radiation Treatment Planning: A Data-driven Method Survey. Journal of Applied Clinical Medical Physics, 22(8), 16-44, 2021.

Momin S, Lei Y, Wang T, Zhang J, Roper J, Bradley J, Curran W, Patel P, Liu T and Yang X*. "Learning-Based Dose Prediction for Pancreatic Stereotactic Body Radiation Therapy using Dual Pyramid Adversarial Network," Physics in Medicine and Biology, 66(12), 125019, 2021.

Harms J, Zhang J, Kayode O, Wolf J, Tian S, McCall N, Higgins K, Castillo R and Yang X*. “Implementation of a Knowledge-based Treatment Planning Model for Cardiac-Sparing Lung Radiotherapy," Advances in Radiation Oncology, 100745, 2021.