Feng Yuefei, Zheng Yao, Huang Dong, Wei Jie, Liu Tianci, Wang Yinyan, Liu Yang
School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an 710032, China.
Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, No. 169 Changle West Road, Xi'an 710032, China.
Bioengineering (Basel). 2024 Sep 23;11(9):951. doi: 10.3390/bioengineering11090951.
The registration of preoperative and follow-up brain MRI, which is crucial in illustrating patients' responses to treatments and providing guidance for postoperative therapy, presents significant challenges. These challenges stem from the considerable deformation of brain tissue and the areas of non-correspondence due to surgical intervention and postoperative changes. We propose a stepwise corrected attention registration network grounded in convolutional neural networks (CNNs). This methodology leverages preoperative and follow-up MRI scans as fixed images and moving images, respectively, and employs a multi-level registration strategy that establishes a precise and holistic correspondence between images, from coarse to fine. Furthermore, our model introduces a corrected attention module into the multi-level registration network that can generate an attention map at the local level through the deformation fields of the upper-level registration network and pathological areas of preoperative images segmented by a mature algorithm in BraTS, serving to strengthen the registration accuracy of non-correspondence areas. A comparison between our scheme and the leading approach identified in the MICCAI's BraTS-Reg challenge indicates a 7.5% enhancement in the target registration error (TRE) metric and improved visualization of non-correspondence areas. These results illustrate the better performance of our stepwise corrected attention registration network in not only enhancing the registration accuracy but also achieving a more logical representation of non-correspondence areas. Thus, this work contributes significantly to the optimization of the registration of brain MRI between preoperative and follow-up scans.
术前和随访脑磁共振成像（MRI）的配准在阐明患者对治疗的反应以及为术后治疗提供指导方面至关重要，但也面临重大挑战。这些挑战源于手术干预和术后变化导致的脑组织显著变形以及不对应区域。我们提出了一种基于卷积神经网络（CNN）的逐步校正注意力配准网络。该方法分别将术前和随访MRI扫描用作固定图像和移动图像，并采用多级配准策略，从粗到精地在图像之间建立精确且整体的对应关系。此外，我们的模型在多级配准网络中引入了校正注意力模块，该模块可以通过上层配准网络的变形场和由BraTS中成熟算法分割的术前图像的病理区域在局部层面生成注意力图，以增强不对应区域的配准精度。我们的方案与MICCAI的BraTS-Reg挑战赛中确定的领先方法之间的比较表明，目标配准误差（TRE）指标提高了7.5%，不对应区域的可视化效果得到改善。这些结果表明，我们的逐步校正注意力配准网络不仅在提高配准精度方面表现更好，而且在实现不对应区域更合理的表示方面也表现出色。因此，这项工作对优化术前和随访扫描之间的脑MRI配准有重大贡献。
