O'Connor Laura M, Choi Jae H, Dowling Jason A, Warren-Forward Helen, Martin Jarad, Greer Peter B
Department of Radiation Oncology, Calvary Mater Hospital, Newcastle, NSW, Australia.
School of Health Sciences, University of Newcastle, Callaghan, NSW, Australia.
Front Oncol. 2022 Feb 8;12:822687. doi: 10.3389/fonc.2022.822687. eCollection 2022.
There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry.
Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT.
The median percentage dose difference between the CT and sCT was <1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at -0.03% (IQR 0.13, -0.31) and bulk density assignment resulted in the greatest difference at -0.73% (IQR -0.10, -1.01). The mean 3D gamma dose agreement at 3%/2 mm among all sCT methods was 99.8%. The highest agreement at 1%/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation.
All methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.
在仅使用磁共振成像(MRI)进行治疗计划时,有多种生成合成计算机断层扫描(sCT)的方法;然而,许多研究忽略了大的盆腔治疗区域以及针对女性解剖结构的特定方法。本研究旨在应用四种最常用的sCT创建方法,以促进仅基于MRI的男性和女性肛肠及妇科肿瘤放射治疗计划的制定。针对Hounsfield单位(HU)估计和计划剂量测定,将sCT方法与传统计算机断层扫描(CT)进行了验证。
使用40例患者的配对MRI和CT扫描进行sCT生成和验证。将体密度赋值、组织类别密度赋值、混合图谱和深度学习sCT生成方法应用于所有40例患者。通过参考点处的剂量差异、剂量体积直方图(DVH)参数和三维伽马剂量比较来评估剂量测定准确性。通过每种sCT与CT之间HU值的平均误差和平均绝对误差来评估HU估计。
所有sCT方法中,CT与sCT之间的剂量差异中位数均<1.0%。深度学习方法导致与CT的剂量差异中位数最低,为-0.03%(四分位间距0.13,-0.31),体密度赋值导致的差异最大,为-0.73%(四分位间距-0.10,-1.01)。所有sCT方法在3%/2 mm时的平均三维伽马剂量一致性为99.8%。在1%/1 mm时,深度学习方法的一致性最高,为97.3%,体密度方法的一致性最低,为93.6%。深度学习和混合图谱技术在HU估计的平均误差和平均绝对误差方面与CT的差异最小。
本研究中使用的所有sCT生成方法在男性和女性癌症盆腔区域仅基于MRI的计划中均产生了相似的高剂量测定一致性。sCT生成技术的选择可根据可用的科室资源和图像引导考虑因素来指导,对剂量测定准确性的影响最小。