Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
Med Phys. 2024 Sep;51(9):6176-6184. doi: 10.1002/mp.17235. Epub 2024 May 31.
Stereotactic body radiotherapy (SBRT) is a well-established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs-at-risk (OAR) toxicity from radiation. MRI-CT deformable image registration (DIR) is required to propagate the contours defined on high-contrast MRI to CT images. An accurate DIR method could lead to more precisely defined treatment volumes and superior OAR sparing on the treatment plan. Therefore, it is beneficial to develop an accurate MRI-CT DIR for liver SBRT.
To create a new deep learning model that can estimate the deformation vector field (DVF) for directly registering abdominal MRI-CT images.
The proposed method assumed a diffeomorphic deformation. By using topology-preserved deformation features extracted from the probabilistic diffeomorphic registration model, abdominal motion can be accurately obtained and utilized for DVF estimation. The model integrated Swin transformers, which have demonstrated superior performance in motion tracking, into the convolutional neural network (CNN) for deformation feature extraction. The model was optimized using a cross-modality image similarity loss and a surface matching loss. To compute the image loss, a modality-independent neighborhood descriptor (MIND) was used between the deformed MRI and CT images. The surface matching loss was determined by measuring the distance between the warped coordinates of the surfaces of contoured structures on the MRI and CT images. To evaluate the performance of the model, a retrospective study was carried out on a group of 50 liver cases that underwent rigid registration of MRI and CT scans. The deformed MRI image was assessed against the CT image using the target registration error (TRE), Dice similarity coefficient (DSC), and mean surface distance (MSD) between the deformed contours of the MRI image and manual contours of the CT image.
When compared to only rigid registration, DIR with the proposed method resulted in an increase of the mean DSC values of the liver and portal vein from 0.850 ± 0.102 and 0.628 ± 0.129 to 0.903 ± 0.044 and 0.763 ± 0.073, a decrease of the mean MSD of the liver from 7.216 ± 4.513 mm to 3.232 ± 1.483 mm, and a decrease of the TRE from 26.238 ± 2.769 mm to 8.492 ± 1.058 mm.
The proposed DIR method based on a diffeomorphic transformer provides an effective and efficient way to generate an accurate DVF from an MRI-CT image pair of the abdomen. It could be utilized in the current treatment planning workflow for liver SBRT.
立体定向体放射治疗（SBRT）是一种针对不适合手术的肝转移患者的成熟治疗方式。在治疗计划中，CT 和 MRI 都有助于进行准确的靶区勾画，并减少来自辐射的潜在危及器官（OAR）毒性。MRI-CT 变形图像配准（DIR）需要将高对比度 MRI 上定义的轮廓传播到 CT 图像上。准确的 DIR 方法可以使治疗体积更精确地定义，并在治疗计划中更好地保护 OAR。因此，开发一种用于肝脏 SBRT 的准确 MRI-CT DIR 是有益的。
创建一种新的深度学习模型，该模型可以估计用于直接注册腹部 MRI-CT 图像的变形向量场（DVF）。
所提出的方法假设了一种可变形的变形。通过使用从概率变形配准模型中提取的拓扑保持变形特征，可以准确地获得腹部运动，并将其用于 DVF 估计。该模型将在运动跟踪方面表现出色的 Swin 转换器集成到卷积神经网络（CNN）中，用于变形特征提取。该模型使用跨模态图像相似性损失和表面匹配损失进行优化。为了计算图像损失，在变形的 MRI 和 CT 图像之间使用了一种模态独立的邻域描述符（MIND）。通过测量 MRI 和 CT 图像上勾画结构的曲面变形坐标之间的距离来确定表面匹配损失。为了评估模型的性能，对 50 例接受 MRI 和 CT 扫描刚性配准的肝脏病例进行了回顾性研究。使用目标配准误差（TRE）、Dice 相似系数（DSC）和 MRI 图像变形轮廓与 CT 图像手动轮廓之间的平均表面距离（MSD）评估变形的 MRI 图像与 CT 图像之间的吻合度。
与仅进行刚性配准相比，使用所提出的方法进行 DIR 可将肝脏和门静脉的平均 DSC 值从 0.850±0.102 和 0.628±0.129 分别提高到 0.903±0.044 和 0.763±0.073，将肝脏的平均 MSD 从 7.216±4.513mm 降低到 3.232±1.483mm，将 TRE 从 26.238±2.769mm 降低到 8.492±1.058mm。
基于可变形变压器的所提出的 DIR 方法为从腹部 MRI-CT 图像对生成准确的 DVF 提供了一种有效且高效的方法。它可以在当前的肝脏 SBRT 治疗计划工作流程中使用。
