MRI in Cancer Radiotherapy

A potential treatment planning process with magnetic resonance imaging (MRI) as a sole imaging modality could eliminate systematic CT/MRI co-registration errors, reduce medical cost, minimize patient radiation exposure, and streamline clinical workflow. However, the main challenge in substituting CT with MRI is that MRI cannot provide the key electron density information that is needed for accurate dose calculation. Automated synthetic CT (sCT) generation based on MRI would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow.

Related publications:

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.

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.

This project aims to develop a tumor-targeted, ultrasound (US)-guided prostate focal boost HDR brachytherapy based on our MRI-US-CT deformable registration, which can incorporate multiparametric MRI-defined tumor into real-time US imaging to guide HDR catheter placement, and panning CT to guide focal tumor boost dose delivery in HDR brachytherapy. This MRI-guided HDR needle placement can improve achievable boost dose level and coverage for tumors, and accuracy of radiation delivery.

Related publications:

Wang T, Press R, Giles M, Jani A, Rossi P, Lei Y, Liu T, Curran W, Patel P and Yang X*. “Multiparametric MRI-guided Dose Boost to Dominant Intraprostatic Lesions in High-dose-rate Prostate Brachytherapy," The British Journal of Radiology, 92: 20190089, 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.

Dai X, Lei Y, Zhang Y, Qiu R, Wang T, Dresser S, Curran W, Patel P, Liu T and Yang X*. “Automatic Multi-catheter Detection using Deeply Supervised Convolutional Neural Network in MRI-guided HDR Prostate Brachytherapy,” Medical Physics, 47(9), 4115-4124, 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.

Delivering a focal radiotherapy boost dose to multiparametric MRI-defined dominant lesions/tumors using proton therapy is feasible without violating organs-at-risk constraints, and has a potential clinical benefit by improving tumor control probability.

Related publications:

Wang T, Zhou J, Tian S, Wang Y, Patel P, Jani A, Langen K, Curran W, Liu T and Yang X*. “A Feasibility Study of Focal Dose Escalations to Multiparametric MRI-defined Dominant Intraprostatic Lesions in Prostate Proton Radiation Therapy," The British Journal of Radiology, 96: 20190845, 2019.

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.

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.

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, 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 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.

Related publications:

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.

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, Lei Y, Kahn S, Liu T, Curran W, Shu H, Mao H and Yang X*. “Brain Tumor Segmentation Using 3D Mask R-CNN for Dynamic Susceptibility Contrast Enhanced Perfusion Imaging,” Physics in Medicine and Biology, 65(18):185009, 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.

Dai X, Lei Y, Liu Y, Dong X, Wang T, Curran W, Liu T, Patel P and Yang X*. "Intensity Non-uniformity Correction in MR Imaging Using Deep Learning," Proc. of SPIE, 11317, 1131727-34, 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.