Multi-parametric MRI for radiotherapy simulation

Tian Li; Jihong Wang; Yingli Yang; Carri K Glide-Hurst; Ning Wen; Jing Cai

doi:10.1002/mp.16256

Multi-parametric MRI for radiotherapy simulation

Med Phys. 2023 Aug;50(8):5273-5293. doi: 10.1002/mp.16256. Epub 2023 Feb 9.

Authors

Tian Li¹, Jihong Wang², Yingli Yang^{3

4}, Carri K Glide-Hurst⁵, Ning Wen^{3

4

6}, Jing Cai¹

Affiliations

¹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
² Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA.
³ Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China.
⁴ SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
⁵ Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA.
⁶ The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China.

Abstract

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Keywords: MRI simulation; MRI-guided radiotherapy; MRI-linac; artificial intelligence; machine learning; mpMRI; multi-parametric MRI.

Multi-parametric MRI for radiotherapy simulation

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