Multi-contrast low-field MRI acceleration with k-space progressive learning and image-space hybrid attention fusion

Abstract

Multi-contrast MRI provides complementary tissue information for diagnosis and treatment planning but is limited by the long acquisition time and system noise, which deteriorates at low field strength. To jointly accelerate and denoise multi-contrast MRI acquired at low field strength, we present a novel dual-domain framework designed to reconstruct high-quality multi-contrast MR images from k-space data corrupted by under-sampling and system noise. Our dual-domain framework first enhances k-space data quality through a k-space Low-to-High Frequency Progressive (LHFP) learning network, and then further refines the k-space outputs with an image-space Hybrid Attention Fusion Network (HAFNet). In k-space learning, the magnitude imbalance between the low- and high-frequency components may cause the network to be dominated by low-frequency components, leading to sub-optimal recovery of high-frequency components. To tackle this challenge, the two-stage LHFP learning network first recovers low-frequency components and then emphasizes high-frequency learning through patient-specific adaptive prediction of the low-high frequency boundary. In image domain learning, the challenge of efficiently capturing long-range dependencies across the multi-contrast images is resolved through Hybrid Window-based Attention Fusion (HWAF) modules, which integrate features by alternately computing self-attention within dense and dilated windows. Extensive experiments on the BraTs MRI and M4Raw low-field MRI datasets demonstrate the superiority of our method over state-of-the-art MRI reconstruction methods. Our source code will be made publicly available upon acceptance.