Abstract
Accurate MRI-to-CT translation promises the integration of complementary imaging information without the need for additional imaging sessions. Given the practical challenges associated with acquiring paired MRI and CT scans, the development of robust methods capable of leveraging unpaired datasets is essential for advancing the MRI-to-CT translation. Current unpaired MRI-to-CT translation methods, which predominantly rely on cycle consistency and contrastive learning frameworks, frequently encounter challenges in accurately translating anatomical features that are highly discernible on CT but less distinguishable on MRI, such as bone structures. This limitation renders these approaches less suitable for applications in radiation therapy, where precise bone representation is essential for accurate treatment planning. To address this challenge, we propose a path- and bone-contour regularized approach for unpaired MRI-to-CT translation. In our method, MRI and CT images are projected to a shared latent space, where the MRI-to-CT mapping is modeled as a continuous flow governed by neural ordinary differential equations. The optimal mapping is obtained by minimizing the transition path length of the flow. To enhance the accuracy of translated bone structures, we introduce a trainable neural network to generate bone contours from MRI and implement mechanisms to directly and indirectly encourage the model to focus on bone contours and their adjacent regions. Evaluations conducted on three datasets demonstrate that our method outperforms existing unpaired MRI-to-CT translation approaches, achieving lower overall error rates. Moreover, in a downstream bone segmentation task, our approach exhibits superior performance in preserving the fidelity of bone structures. Our code is available at: this https URL.
Abstract (translated)
准确的MRI到CT图像转换有望整合互补成像信息，而无需进行额外的成像会话。鉴于获取配对的MRI和CT扫描在实践中所面临的挑战，开发能够利用未配对数据集的强大方法对于推进MRI至CT的转换至关重要。目前基于未配对数据的方法主要依赖于循环一致性及对比学习框架，在将MRI图像准确翻译为含有CT图像中明显可见但在MRI上不太可区分的解剖特征（如骨骼结构）时，经常遇到困难。这一限制使得这些方法在放射治疗应用中的适用性降低，因为在该领域精确地表示骨骼对于制定准确的治疗计划至关重要。 为了应对这一挑战，我们提出了一种针对未配对MRI到CT转换的路径和骨轮廓正则化的方法。在此方法中，我们将MRI和CT图像映射到一个共享的潜在空间，在此空间内，从MRI到CT的映射被视为由神经常微分方程所调控的连续流。通过最小化该流动过程中的过渡路径长度来获得最佳映射。为了提高翻译后的骨骼结构的准确性，我们引入了一种可训练的神经网络以生成MRI上的骨轮廓，并实施机制直接和间接地鼓励模型专注于骨轮廓及其附近区域。 在三个数据集上进行的评估表明，我们的方法超越了现有的未配对MRI至CT转换方案，在总体错误率方面表现更佳。此外，在下游骨骼分割任务中，我们的方法展示了保持骨骼结构保真度方面的卓越性能。我们的代码可在此链接获取：this https URL.
URL
https://arxiv.org/abs/2505.03114
