Translational and Clinical Research Institute, Newcastle University, Newcastle, UK; Northern Centre for Cancer Care, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle, UK.
GE Healthcare, Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
Radiother Oncol. 2023 Jul;184:109692. doi: 10.1016/j.radonc.2023.109692. Epub 2023 May 6.
Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel Zero Echo Time (ZTE) sequence where bones are visible and images are acquired in 65 seconds. This study evaluated the dose calculation accuracy for pelvic sites of a ZTE-based Deep Learning sCT algorithm developed by GE Healthcare.
ZTE and CT images were acquired in 56 pelvic radiotherapy patients in the radiotherapy position. A 2D U-net convolutional neural network was trained using pairs of deformably registered CT and ZTE images from 36 patients. In the remaining 20 patients the dosimetric accuracy of the sCT was assessed using cylindrical dummy Planning Target Volumes (PTVs) positioned at four different central axial locations, as well as the clinical treatment plans (for prostate (n = 10), rectum (n = 4) and anus (n = 6) cancers). The sCT was rigidly and deformably registered, the plan recalculated and the doses compared using mean differences and gamma analysis.
Mean dose differences to the PTV D98% were ≤ 0.5% for all dummy PTVs and clinical plans (rigid registration). Mean gamma pass rates at 1%/1 mm were 98.0 ± 0.4% (rigid) and 100.0 ± 0.0% (deformable), 96.5 ± 0.8% and 99.8 ± 0.1%, and 95.4 ± 0.6% and 99.4 ± 0.4% for the clinical prostate, rectum and anus plans respectively.
A ZTE-based sCT algorithm with high dose accuracy throughout the pelvis has been developed. This suggests the algorithm is sufficiently accurate for MR-only radiotherapy for all pelvic sites.
磁共振（MR）仅放疗可在不依赖磁共振计算机断层扫描（MR-CT）配准的不确定性的情况下使用 MR。这需要合成 CT（sCT）进行剂量计算，而一种新的零回波时间（ZTE）序列可以实现这一点，该序列中骨骼可见，图像可在 65 秒内采集。本研究评估了由通用电气医疗保健公司开发的基于 ZTE 的深度学习 sCT 算法在骨盆部位的剂量计算准确性。
在放疗体位下采集 56 例盆腔放疗患者的 ZTE 和 CT 图像。使用来自 36 例患者的可变形配准 CT 和 ZTE 图像对二维 U-net 卷积神经网络进行训练。在其余 20 例患者中，使用四个不同中心轴向位置的圆柱形虚拟计划靶区（PTV）以及临床治疗计划（前列腺癌 n=10、直肠癌 n=4 和肛门癌 n=6）评估 sCT 的剂量学准确性。对 sCT 进行刚性和变形配准，重新计算计划并使用平均差异和伽马分析比较剂量。
所有虚拟 PTV 和临床计划（刚性配准）的 PTV D98%剂量差异平均值均≤0.5%。1%/1mm 的平均伽马通过率分别为 98.0±0.4%（刚性）和 100.0±0.0%（变形）、96.5±0.8%和 99.8±0.1%、95.4±0.6%和 99.4±0.4%，分别为临床前列腺、直肠和肛门计划。
已开发出一种在整个骨盆区域具有高剂量准确性的基于 ZTE 的 sCT 算法。这表明该算法对于所有骨盆部位的 MR 仅放疗具有足够的准确性。
