Pyramid Convolutional Recurrent Network for Serial Medical Image Registration With Adaptive Motion Regularizations

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2024-06-05 DOI:10.1109/TRPMS.2024.3410021
Jiayi Lu;Renchao Jin;Enmin Song
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Abstract

Objective: Serial medical image registration plays an important role in radiation therapy treatment planning. However, current deep learning-based deformable registration models suffer from excessive resource consumption and suboptimal precision issues. Moreover, the global regularization term may result in unrealistic deformations due to displacement field noise and intertissue sliding motion omission. Methods: This article proposes a patch-based pyramid convolutional recurrent neural network (pyramid CRNet) for serial medical image registration. Patch-wise training is employed to alleviate resource constraints. Incorporating spatiotemporal features across multiple scales is beneficial for focusing on more details to improve accuracy. Moreover, two motion adaptive techniques are introduced to provide anatomically plausible displacement fields. The first uses a guided filter to reduce noise and maintain motion continuity within organs. The second involves a pixel-wise weight regularization term within the loss function to provide a tailored solution for distinctive tissue characteristics, especially for sliding motion at organ boundaries. Results: Experiments were conducted on lung 4DCT images and cardiac cine MR images. Quantitative and qualitative results have demonstrated that our method can align anatomical structures across multiple images in a physiologically sensible manner. Conclusion: The significance of this work lies in its potential to address pressing challenges in clinical applications, and further investigations could be extended to explore different modalities and dimensions.
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利用自适应运动正则化实现串行医学图像配准的金字塔卷积递归网络
目的:序列医疗图像配准在放射治疗规划中发挥着重要作用。然而,目前基于深度学习的可变形配准模型存在资源消耗过多和精度不理想的问题。此外,由于位移场噪声和组织间滑动运动遗漏,全局正则化项可能会导致不切实际的变形。方法:本文提出了一种基于补丁的金字塔卷积递归神经网络(pyramid CRNet),用于序列医学图像配准。为了缓解资源限制,采用了片段式训练。纳入多个尺度的时空特征有利于关注更多细节,从而提高准确性。此外,还引入了两种运动自适应技术,以提供解剖学上可信的位移场。第一种技术使用引导滤波器来减少噪声,并保持器官内部运动的连续性。第二种是在损失函数中加入像素权重正则化项,为独特的组织特征,尤其是器官边界的滑动运动提供量身定制的解决方案。实验结果对肺部 4DCT 图像和心脏椎体磁共振图像进行了实验。定量和定性结果表明,我们的方法能以生理学上合理的方式对准多幅图像上的解剖结构。结论这项工作的意义在于它有可能解决临床应用中的紧迫挑战,进一步的研究可以扩展到探索不同的模式和维度。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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