Scholay

学术搜索 · AI 审稿 · LaTeX 协作

Abdominal MR Multitasking for radiotherapy treatment planning: A motion‐resolved and multicontrast 3D imaging approach

作者:Junzhou Chen, Anthony Christodoulou, Pei Han, Jiayu Xiao, Fei Han, Zhehao Hu, Nan Wang, Hui Han, Diane C. Ling, Eric Chang, Mary Feng, Jessica Scholey, Sophia Cui, Debiao Li, Wensha Yang, Zhaoyang Fan · 发表于:Magnetic Resonance in Medicine · 年份:2024 · DOI:10.1002/mrm.30256 · 被引用次数:4 · 研究领域:Advanced Radiotherapy Techniques、Advanced MRI Techniques and Applications、Hepatocellular Carcinoma Treatment and Prognosis

Abstract Purpose Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion‐resolved imaging are desired for accurate delineation of the target and various organs‐at‐risk and patient‐tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)–based technique to produce volumetric, motion‐resolved, multicontrast images for abdominal radiotherapy treatment planning. Methods The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization‐prepared (saturation recovery and T 2 preparations) spoiled gradient‐echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. Results In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R 2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast‐weighting selection for better visualization. Initial clinical testing with interobserver analysis demo...