Jafargholi Rangraz Esmaeel, Coudyzer Walter, Maleux Geert, Baete Kristof, Deroose Christophe M, Nuyts Johan
Nuclear Medicine, Department of imaging and pathology, UZ & KU Leuven, Leuven, Belgium.
Radiology Section, Department of imaging and pathology, UZ Leuven, Leuven, Belgium.
EJNMMI Res. 2019 Feb 20;9(1):19. doi: 10.1186/s13550-019-0485-x.
We have developed a multi-modal imaging approach for SIRT, combining Tc-MAA SPECT/CT and/or Y PET, F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required.
In this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment F-FDG PET/CT, Tc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image.
Our methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry. The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe).
This methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients.
我们开发了一种用于选择性内放射治疗（SIRT）的多模态成像方法，将锝标记的大颗粒聚合白蛋白（Tc-MAA）单光子发射计算机断层扫描/计算机断层扫描（SPECT/CT）和/或钇正电子发射断层扫描（Y PET）、氟代脱氧葡萄糖（F-FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）以及对比增强锥束计算机断层扫描（CBCT）相结合，用于基于体素的剂量测定，作为治疗计划和验证的工具。对于辐射剂量预测计算，需要对全肝体积和肝灌注区域进行分割。
在本文中，我们提出了一种多模态图像分析程序，以辅助SIRT治疗计划。使用初始刚性配准，随后进行可变形配准，将治疗前的F-FDG PET/CT、Tc-MAA SPECT/CT和对比增强CBCT图像配准到一个公共空间。由一位专家使用李克特量表对配准进行评分。基于PET/CT和SPECT/CT图像半自动分割全肝，并基于CBCT图像确定肝灌注区域。将肝脏和肝叶的分割结果与一位专家在CT图像上的手动分割结果进行比较。
我们的方法表明，多模态图像分析可用于在SIRT中利用所有治疗前研究通过CBCT确定肝脏和灌注区域。图像配准结果显示出可接受的对齐，对剂量测定的影响有限。根据专家评审，图像配准表现良好（在94%的病例中评分为完美或几乎没有错位）。半自动肝脏分割与手动肝脏分割结果吻合良好（骰子系数为0.92，平均豪斯多夫距离为3.04毫米）。结果显示，与基于CT图像分割肝叶相比，使用CBCT图像分割肝叶存在差异（平均豪斯多夫距离为14.18毫米），对剂量测定有很大影响（右肝叶差异高达9戈瑞，左肝叶差异高达21戈瑞）。
该方法可用于治疗前剂量测定和SIRT计划，包括确定为实现治疗目标应给予的活度。纳入灌注CBCT能够基于灌注定义肝叶，结果显示在一些患者中，这与解剖学定义明显不同。
