Anatomical feature-prioritized loss for enhanced MR to CT translation

TL;DR

This paper introduces an anatomical feature-prioritized loss function for MR to CT translation, improving the reconstruction of clinically significant structures by leveraging pre-trained models, with applications in lung and pelvis imaging.

Contribution

The novel AFP loss function enhances medical image synthesis by focusing on important anatomical features, integrating with various networks, and demonstrating improved local detail reconstruction.

Findings

Improved bronchial structure reconstruction in lung MR to CT translation.

Enhanced organ and muscle representation in pelvis MR to CT synthesis.

Effective integration of AFP loss with GAN and CNN models.

Abstract

In medical image synthesis, the precision of localized structural details is crucial, particularly when addressing specific clinical requirements such as the identification and measurement of fine structures. Traditional methods for image translation and synthesis are generally optimized for global image reconstruction but often fall short in providing the finesse required for detailed local analysis. This study represents a step toward addressing this challenge by introducing a novel anatomical feature-prioritized (AFP) loss function into the synthesis process. This method enhances reconstruction by focusing on clinically significant structures, utilizing features from a pre-trained model designed for a specific downstream task, such as the segmentation of particular anatomical regions. The AFP loss function can replace or complement global reconstruction methods, ensuring a balanced emphasis on both global image fidelity and local structural details. Various implementations of this loss function are explored, including its integration into different synthesis networks such as GAN-based and CNN-based models. Our approach is applied and evaluated in two contexts: lung MR to CT translation, focusing on high-quality reconstruction of bronchial structures, using a private dataset; and pelvis MR to CT synthesis, targeting the accurate representation of organs and muscles, utilizing a public dataset from the Synthrad2023 challenge. We leverage embeddings from pre-trained segmentation models specific to these anatomical regions to demonstrate the capability of the AFP loss to prioritize and accurately reconstruct essential features. This tailored approach shows promising potential for enhancing the specificity and practicality of medical image synthesis in clinical applications.