Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Department of Information Technology, Uppsala University, Uppsala, Sweden.
Biomed Eng Online. 2024 Apr 13;23(1):42. doi: 10.1186/s12938-024-01235-x.
Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.
The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.
Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.
The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.
计算机断层扫描（CT）是一种常用于内部身体结构研究的成像方式，对于身体成分的详细研究非常有用。本研究的目的是开发和评估一种用于受试者间 CT 切片配准的全自动图像配准框架。目的也是利用这些结果，在一组概念验证研究中，对成像和非成像数据之间的相关性进行体素水平的统计身体成分分析（Imiomics）。
本研究利用了来自两个大型队列研究 SCAPIS 和 IGT 的三个肝脏、腹部和大腿的单切片 CT 图像。所开发和评估的图像配准方法同时使用了 CT 图像以及图像衍生的组织和器官分割掩模。为了评估配准方法的性能，使用了来自 SCAPIS 和 IGT 队列的一组基线 3 个单切片 CT 图像（包括 8285 个切片的 2780 个受试者）进行了注册。使用矢量幅度和强度幅度误差指示逆一致性进行评估。图像配准结果进一步用于对 CT 图像（以体素单位的组织体积和雅可比行列式表示）与两个队列研究中收集的各种组织、脂肪沉积和器官的各种显式测量值之间的关联进行体素分析。
我们的研究结果表明，关键器官和解剖结构得到了适当的注册。逆一致性的评估参数，如矢量幅度和强度幅度误差，平均小于 3 毫米和 50 个 Hounsfield 单位。配准后进行的 Imiomics 分析使我们能够检查各种显式测量值（肝脏、脾脏、腹部肌肉、内脏脂肪组织（VAT）、皮下脂肪组织（SAT）、大腿 SAT、肌肉间脂肪组织（IMAT）和大腿肌肉）与体素水平图像信息之间的关联。
所开发和评估的框架允许对收集的三个单切片 CT 图像进行准确的图像配准，并能够对身体成分与相关疾病和风险因素之间的关联进行详细的体素水平研究。
