Chen Xinru, Ding Yao, Weng Julius, Wu Carol C, Zhao Yao, Sobremonte Angela, Mohammedsaid Mustefa, Xu Zhan, Zhang Xiaodong, Niedzielski Joshua S, Shete Sanjay S, Court Laurence E, Liao Zhongxing, Wang Jihong, Subashi Ergys, Lee Percy P, Yang Jinzhong
Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, Texas.
Adv Radiat Oncol. 2025 Jul 4;10(9):101845. doi: 10.1016/j.adro.2025.101845. eCollection 2025 Sep.
Accurate cardiac chamber segmentation is crucial for improving cardiac sparing in magnetic resonance (MR)-guided adaptive radiation therapy, especially in patients at risk for radiation-induced cardiotoxicity. Here, we developed and evaluated automatic segmentation models for cardiac chambers that use daily MR images acquired on a 1.5-T MR-Linac system.
Twenty healthy volunteers underwent daily MR scanning on a 1.5-T MR-Linac, with 2 radial sequences: T2/T1 3DVaneXD balanced fast field echo with spectral attenuated inversion recovery (bFFE-SPAIR) and T1 3DVaneXD mDixon. Three flip angles were tested for each sequence to determine optimal image quality for chamber segmentation. Full-resolution 3D nnU-Net models were trained for the following: (1) bFFE-SPAIR (bFFE model); (2) T1 mDixon (mDixon model); and (3) both sequences (hybrid model). Models were evaluated based on Dice similarity coefficient (DSC) and mean surface distance against manual contours. Clinical acceptance of the automatic segmentation was assessed with a 5-point Likert scale. An in-silico planning study was performed to assess cardiac chamber sparing during plan adaptation.
The average contrast-to-noise ratios in bFFE-SPAIR were 8.7 (20°), 34.2 (50°), and 37.3 (80°); for T1 mDixon, these values were 3.6 (5°), 5.9 (10°), and 4.9 (20°). The bFFE model achieved the highest segmentation performance (average DSC 0.85 ± 0.05 and mean surface distance 2.2 ± 0.6 mm). The T1 mDixon sequence, despite lower contrast-to-noise ratios, provided similar segmentation accuracy (DSC 0.83 ± 0.06). A hybrid model combining both sequences showed no significant improvement over the bFFE model. Clinical evaluation indicated that 95% of the autosegmented contours from the bFFE model were acceptable for clinical use (score ≥4). Adaptive plan greatly reduced individual cardiac chamber dose while maintaining similar target coverage.
This study demonstrated the feasibility of using bFFE-SPAIR and T1 mDixon sequences to accurately segment cardiac chambers on a 1.5-T MR-Linac. These models offer potential for improved cardiac sparing in MR-guided adaptive radiation therapy.
准确的心脏腔室分割对于在磁共振（MR）引导的自适应放射治疗中改善心脏保护至关重要，尤其是对于有辐射诱发心脏毒性风险的患者。在此，我们开发并评估了用于心脏腔室的自动分割模型，该模型使用在1.5-T MR直线加速器系统上获取的每日MR图像。
20名健康志愿者在1.5-T MR直线加速器上接受每日MR扫描，采用2个径向序列：T2/T1 3DVaneXD平衡快速场回波与频谱衰减反转恢复（bFFE-SPAIR）和T1 3DVaneXD mDixon。对每个序列测试了3个翻转角，以确定用于腔室分割的最佳图像质量。对全分辨率3D nnU-Net模型进行了如下训练：（1）bFFE-SPAIR（bFFE模型）；（2）T1 mDixon（mDixon模型）；以及（3）两个序列（混合模型）。基于Dice相似系数（DSC）和相对于手动轮廓的平均表面距离对模型进行评估。使用5点李克特量表评估自动分割的临床可接受性。进行了一项计算机模拟计划研究，以评估计划调整期间的心脏腔室保护情况。
bFFE-SPAIR中的平均对比度噪声比分别为8.7（20°）、34.2（50°）和37.3（80°）；对于T1 mDixon，这些值分别为3.6（5°）、5.9（10°）和4.9（20°）。bFFE模型实现了最高的分割性能（平均DSC 0.85±0.05，平均表面距离2.2±（0.6）mm）。T1 mDixon序列尽管对比度噪声比更低，但提供了相似的分割精度（DSC 0.83±0.06）。结合两个序列的混合模型与bFFE模型相比没有显著改善。临床评估表明，bFFE模型中95%的自动分割轮廓在临床上是可接受的（评分≥4）。自适应计划在保持相似靶区覆盖的同时，大大降低了各个心脏腔室的剂量。
本研究证明了使用bFFE-SPAIR和T1 mDixon序列在1.5-T MR直线加速器上准确分割心脏腔室的可行性。这些模型为在MR引导的自适应放射治疗中改善心脏保护提供了潜力。
