Task-Adaptive 3D Cross-Field MRI Translation via Field-Conditioned Content-Style Pretraining

2026-08-10

AI Summary

This work addresses the domain shift in magnetic resonance imaging (MRI) caused by variations in magnetic field strength by proposing a 3D unpaired cross-field-strength image translation framework that does not require paired multi-field-strength data. The method leverages field-strength-conditioned content–style disentanglement pretraining to separate anatomical structure from field-strength-dependent contrast features, and integrates an AdaIN-modulated decoder with multi-field-strength discriminators to enable controllable image generation across arbitrary field strengths within a unified model. Evaluated on the MRIxFields dataset encompassing five field strengths and three modalities, the approach demonstrates high-fidelity preservation of 3D anatomical structures in diverse translation tasks—including Any-to-7T, 0.1T-to-High, and Any-to-Any—significantly enhancing both translation fidelity and flexibility.

Abstract

Magnetic field strength is a major source of domain shift in magnetic resonance imaging (MRI), affecting signal-to-noise ratio, tissue contrast, spatial detail, and the visibility of anatomical boundaries. The MRIxFields 2026 challenge investigates this problem through cross-field MRI translation across acquisitions at 0.1T, 1.5T, 3T, 5T, and 7T. Its three tasks, Any-to-7T, 0.1T-to-High, and Any-to-Any synthesis, require the generation of target-field image characteristics while preserving subject-specific anatomy. This problem is particularly challenging because paired acquisitions of the same subject across multiple field strengths are rarely available for training. We propose a 3D unpaired cross-field MRI translation framework based on field-conditioned content-style pretraining. The proposed framework first learns controllable field-to-field translation across all available field strengths by disentangling anatomical content from field-dependent contrast characteristics. The pretrained backbone is then adapted to task-specific target domains. Our model comprises a 3D content encoder, a 3D style encoder, a field-conditioned style generator, an AdaIN-modulated decoder, and a multi-field discriminator. Adversarial learning encourages realistic target-field appearance, while cycle-consistency, identity, content, style, and diversity constraints promote anatomical fidelity and controllable translation. We evaluate the proposed method on MRIxFields data spanning five field strengths and three MRI modalities. Experiments on paired test data demonstrate that the framework can adapt to the three challenge settings while preserving three-dimensional anatomical structure in the synthesized volumes. The implementation code is publicly available at https://github.com/Idea89560041/3D-MRI-Field-Translation.

Problem

• cross-field MRI translation
• domain shift
• magnetic field strength
• unpaired image synthesis
• anatomical preservation

Innovation

• cross-field MRI translation
• field-conditioned pretraining
• content-style disentanglement
• 3D unpaired image synthesis
• AdaIN-modulated decoder