This paper aims to create a deep learning framework 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.
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 and 0.628 to 0.903 and 0.763, a decrease of the mean
MSD of the liver from 7.216 mm to 3.232 mm, and a decrease of the TRE from
26.238 mm to 8.492 mm. The proposed deformable image registration 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 radiotherapy.

本文旨在创建一个深度学习框架，能够估计直接配准腹部 MRI 和 CT 图像的变形矢量场（DVF）。该方法以可微分形变为基础，利用从概率可微形变配准模型提取的保持拓扑特征来准确获取腹部运动，并将其用于 DVF 估计。该模型通过将在运动追踪中表现优越的 Swin transformers 集成到卷积神经网络（CNN）中进行形变特征提取。模型运用交叉模态图像相似度损失和表面匹配损失进行优化。计算图像损失时，采用变形 MRI 和 CT 图像之间的模态无关邻域描述符（MIND）。表面匹配损失通过测量 MRI 和 CT 图像的轮廓结构的变形坐标之间的距离来确定。将变形后的 MRI 图像与 CT 图像在目标配准误差（TRE）、Dice 相似系数（DSC）和平均表面距离（MSD）方面进行评估。与仅刚性配准相比，采用该方法的变形图像配准导致肝脏和门静脉的平均 DSC 值分别从 0.850 和 0.628 增加到 0.903 和 0.763，肝脏的平均 MSD 从 7.216 mm 降低到 3.232 mm，TRE 从 26.238 mm 降低到 8.492 mm。基于可微分变换器的提出的可变形图像配准方法为从腹部 MRI-CT 图像对生成精确的 DVF 提供了一种有效和高效的方式，可用于目前的肝脏放射治疗计划流程。