Abstract
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.
Abstract (translated)
本文旨在创建一个深度学习框架，可以准确估计直接注册的腹部MRI-CT图像的变形矢量场（DVF）。所提出的方法基于等变形的变形。通过使用概率形态不变的变形特征提取，可以准确获得腹部运动，并用于DVF估计。模型将Swin变换器集成到卷积神经网络（CNN）中，用于变形特征提取。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MRI和CT图像之间使用了一个模态无关的邻域描述符（MIND）。表面匹配损失通过测量MRI和CT图像上轮廓结构的变形坐标之间的距离来确定。对MRI图像的变形轮廓使用目标注册误差（TRE）、余弦相似度系数（DSC）和平均表面距离（MSD）与手动CT图像的变形轮廓进行比较。与仅刚性注册相比，所提出的方法导致肝脏和门静脉的平均DSC值从0.850和0.628增加至0.903和0.763，肝脏平均MSD从7.216 mm减少至3.232 mm，TRE从26.238 mm减少至8.492 mm。基于等变形的图像注册方法，可以生成准确的可用于腹部MRI-CT图像对中的DVF。它可用于当前的肝脏放射治疗计划工作流程。
URL
https://arxiv.org/abs/2405.02692
