Fahad Hafiz Muhammad, Dorsch Stefan, Zaiss Moritz, Karger Christian P
German Cancer Research Center (DKFZ), Medical Physics in Radiation Oncology, Heidelberg, Germany.
University of Heidelberg, Faculty of Medicine, Heidelberg, Germany.
Phys Imaging Radiat Oncol. 2023 Oct 2;28:100497. doi: 10.1016/j.phro.2023.100497. eCollection 2023 Oct.
Magnetic Resonance Imaging (MRI) is widely used in oncology for tumor staging, treatment response assessment, and radiation therapy (RT) planning. This study proposes a framework for automatic optimization of MRI sequences based on pulse sequence parameter sets (SPS) that are directly applied on the scanner, for application in RT planning.
A phantom with seven in-house fabricated contrasts was used for measurements. The proposed framework employed a derivative-free optimization algorithm to repeatedly update and execute a parametrized sequence on the MR scanner to acquire new data. In each iteration, the mean-square error was calculated based on the clinical application. Two clinically relevant optimization goals were pursued: achieving the same signal and therefore contrast as in a target image, and maximizing the signal difference (contrast) between specified tissue types. The framework was evaluated using two optimization methods: a covariance matrix adaptation evolution strategy (CMA-ES) and a genetic algorithm (GA).
The obtained results demonstrated the potential of the proposed framework for automatic optimization of MRI sequences. Both CMA-ES and GA methods showed promising results in achieving the two optimization goals, however, CMA-ES converged much faster as compared to GA.
The proposed framework enables for automatic optimization of MRI sequences based on SPS that are directly applied on the scanner and it may be used to enhance the quality of MRI images for dedicated applications in MR-guided RT.
磁共振成像（MRI）在肿瘤学中广泛用于肿瘤分期、治疗反应评估和放射治疗（RT）计划。本研究提出了一种基于直接应用于扫描仪的脉冲序列参数集（SPS）自动优化MRI序列的框架，用于RT计划。
使用具有七种内部制造对比剂的体模进行测量。所提出的框架采用无导数优化算法，在MR扫描仪上反复更新并执行参数化序列以获取新数据。在每次迭代中，根据临床应用计算均方误差。追求两个临床相关的优化目标：获得与目标图像相同的信号并因此获得相同的对比度，以及最大化指定组织类型之间的信号差异（对比度）。使用两种优化方法对该框架进行评估：协方差矩阵自适应进化策略（CMA-ES）和遗传算法（GA）。
获得的结果证明了所提出的MRI序列自动优化框架的潜力。CMA-ES和GA方法在实现两个优化目标方面均显示出有前景的结果，然而，与GA相比，CMA-ES收敛得更快。
所提出的框架能够基于直接应用于扫描仪的SPS自动优化MRI序列，并且可用于提高MRI图像质量，以用于MR引导RT中的特定应用。
