Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
J Appl Clin Med Phys. 2022 Sep;23(9):e13731. doi: 10.1002/acm2.13731. Epub 2022 Aug 3.
Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long-standing interest in multimodality co-registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT-CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT-CT DIR is applied to the MRI to register it with the CT. Twenty-five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI- and LPD-based methods, and the multimodality DIR provided by a state of the art, commercially available FDA-cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD- and MI-based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI- and LPD-based methods.
准确的计算机断层扫描(CT)和磁共振(MR)成像配准可以提供临床相关和互补的信息,并有助于完成多种临床任务,包括手术和放射治疗计划,以及为没有可用的正电子发射断层扫描(PET)/MR 系统的部位生成虚拟 PET/MR。尽管多模态配准的兴趣由来已久,但稳健的常规临床解决方案仍然是未满足的需求。部分挑战可能是使用互信息(MI)最大化和局部相位差(LPD)作为相似性度量,这些度量的鲁棒性、效率有限,并且难以优化。因此,我们提出通过将 MR 映射到合成 CT 中间体(sCT)来将 MR 配准到 CT,并进一步在最小化均方差的 sCT-CT 可变形图像配准(DIR)中使用它。sCT-CT DIR 的变形场应用于 MRI 以将其与 CT 配准。使用二十五组腹部成像数据进行评估。将所提出的方法与基于标准 MI 和 LPD 的方法以及最先进的、商业上可用的经 FDA 批准的临床软件包提供的多模态 DIR 进行比较。使用全局相似性度量、修改后的 Hausdorff 距离和六个体结构的骰子相似性指数对结果进行比较。此外,四名医生对注册图像进行了视觉评估和评分,以评估其注册准确性。从定量和定性评估都可以明显看出,所提出的方法在注册准确性上优于基于 LPD 和 MI 的方法,并且可以在使用其结果作为起点时改进商业软件包 DIR 的结果。基于这些,本文得出结论,所提出的注册方法比基于 MI 和 LPD 的方法更稳健、准确和高效。