Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Phys Med Biol. 2021 Apr 16;66(8). doi: 10.1088/1361-6560/abf1ba.
A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCT); (2) volumes for sCTplus chest wall and ipsilateral breast (sCT); (3) volumes for sCTplus ribs (sCT); and a deep learning-generated sCT (sCT) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCT/sCT/sCT/sCTrespectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTVwas 0.15/0.00/0.00/-0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCT. Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCTshowed the optimal number of bulk-density levels for a bulk-density approach.
对于在磁共振引导直线加速器（MRI 直线加速器）上进行日常计划优化，需要进行合成计算机断层扫描（sCT）。然而，目前关于在乳腺癌放射治疗中使用 sCT 进行剂量计算的准确性，仅有有限的信息可用。本研究旨在：（1）评估在 1.5 T MRI 直线加速器上使用 a）模拟当前 MRI 直线加速器工作流程的体密度 sCT 和 b）深度学习生成的 sCT 对单次分割新辅助部分乳腺照射（PBI）的治疗计划的剂量计算准确性，以及（2）研究所需的体密度水平数量。我们从计划 CT 为 10 名乳腺癌患者创建了三种复杂程度逐渐增加的体密度 sCT，分别使用体密度：（1）身体、肺和 GTV（sCT）；（2）胸壁和同侧乳腺的体积（sCTplus）；（3）肋骨的体积（sCTplus）；以及仰卧位 1.5 T MRI 的深度学习生成的 sCT（sCT）。在每个 sCT 上优化了单次分割新辅助 PBI 的治疗计划，并在计划 CT 上重新计算。通过评估 sCT 与计划 CT 之间的平均绝对误差（MAE）和平均误差（ME），评估图像质量。通过评估剂量差异、伽马通过率和剂量体积直方图（DVH）差异，评估剂量学评估。获得以下结果（每个患者的中位数 sCT/sCT/sCT/sCT 分别）：体内轮廓内的 MAE 为 106/104/104/75 HU，ME 为 8/9/6/28 HU，PTV 内的平均剂量差异为 0.15/0.00/0.00/-0.07 Gy，中位数伽马通过率（2%/2mm，10%剂量阈值）为 98.9/98.9/98.7/99.4%，除了 sCT 中的皮肤外，所有结构的 DVH 参数差异均低于 2%。在 MRI 直线加速器上进行单次分割新辅助 PBI 的准确剂量计算可以在体密度和深度学习 sCT 上进行，这有助于进一步实施乳腺癌的 MRI 引导放射治疗。在平衡简单性和准确性方面，sCT 显示了体密度方法的最佳体密度水平数量。
