Masitho Siti, Szkitsak Juliane, Grigo Johanna, Fietkau Rainer, Putz Florian, Bert Christoph
Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Phys Imaging Radiat Oncol. 2022 Oct 22;24:111-117. doi: 10.1016/j.phro.2022.10.002. eCollection 2022 Oct.
Magnetic Resonance Imaging (MRI)-only workflow eliminates the MRI-computed tomography (CT) registration inaccuracy, which degrades radiotherapy (RT) treatment accuracy. For an MRI-only workflow MRI sequences need to be converted to synthetic-CT (sCT). The purpose of this study was to evaluate a commercially available artificial intelligence (AI)-based sCT generation for dose calculation and 2D/2D kV-image daily positioning for brain RT workflow.
T1-VIBE DIXON was acquired at the 1.5 T MRI for 26 patients in RT setup for sCTs generation. For each patient, a volumetric modulated arc therapy (VMAT) plan was optimized on the CT, then recalculated on the sCT; and vice versa. sCT-based digitally reconstructed radiographs (DRRs) were fused with stereoscopic X-ray images recorded as image guidance for clinical treatments. Dosimetric differences between planned/recalculated doses and the differences between the calculated and recorded clinical couch shift/rotation were evaluated.
Mean ΔD between planned/recalculated doses for target volumes ranged between -0.2 % and 0.2 %; mean ΔD and ΔD were -0.6 % and 1.6 % and -1.4 % and 1.0 % for organ-at-risks, respectively. Differences were tested for clinical equivalence using intervals ±2 % (dose), ±1mm (translation), and ±1° (rotation). Dose equivalence was found using ±2 % interval ( < 0.001). The median differences between lat./long./vert. couch shift between CT-based/sCT-based DRRs were 0.3 mm/0.2 mm/0.3 mm ( < 0.05); median differences between lat./long./vert. couch rotation were -1.5°/0.1°/0.1° (after improvement of RT setup: -0.4°/-0.1°/-0.4°, < 0.05).
This in-silico study showed that the AI-based sCT provided equivalent results to the CT for dose calculation and daily stereoscopic X-ray positioning when using an optimal RT setup during MRI acquisition.
仅使用磁共振成像(MRI)的工作流程可消除MRI与计算机断层扫描(CT)配准不准确的问题,而这种不准确会降低放射治疗(RT)的治疗精度。对于仅使用MRI的工作流程,需要将MRI序列转换为合成CT(sCT)。本研究的目的是评估一种基于人工智能(AI)的商用sCT生成方法,用于脑部RT工作流程中的剂量计算和二维/二维千伏图像每日定位。
在1.5T MRI上对26例接受RT治疗的患者采集T1-VIBE DIXON序列以生成sCT。对于每位患者,在CT上优化容积调强弧形放疗(VMAT)计划,然后在sCT上重新计算;反之亦然。将基于sCT的数字重建射线照片(DRR)与作为临床治疗图像引导记录的立体X射线图像进行融合。评估计划/重新计算剂量之间的剂量差异以及计算和记录的临床治疗床偏移/旋转之间的差异。
靶区计划/重新计算剂量之间的平均ΔD在-0.2%至0.2%之间;危及器官的平均ΔD和ΔD分别为-0.6%和1.6%以及-1.4%和1.0%。使用±2%(剂量)、±1mm(平移)和±1°(旋转)的区间测试差异的临床等效性。使用±2%的区间发现剂量等效性(<0.001)。基于CT/基于sCT的DRR之间在横/纵/垂向治疗床偏移的中位数差异为0.3mm/0.2mm/0.3mm(<0.05);横/纵/垂向治疗床旋转的中位数差异为-1.5°/0.1°/0.1°(在改进RT设置后:-0.4°/-0.1°/-0.4°,<0.05)。
这项计算机模拟研究表明,在MRI采集期间使用最佳RT设置时,基于AI的sCT在剂量计算和每日立体X射线定位方面提供了与CT等效的结果。