Path and Bone-Contour Regularized Unpaired MRI-to-CT Translation
Journal:
arXiv
Published Date:
May 6, 2025
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:
https://github.com/kennysyp/PaBoT.
