Multi-contrast MRI acceleration via post-reconstruction fusion.
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- Record sourced from PubMed, PMID 42753300.
- Also identified by DOI 10.1016/j.media.2026.104297.
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Abstract
Magnetic Resonance Imaging (MRI) is the gold standard for neuroimaging, yet routine brain protocols require multiple high-resolution 3D contrasts (e.g., T1, T2, and T2-FLAIR), resulting in long scan times. Many deep-learning acceleration methods assume access to raw k-space and rely on non-Cartesian trajectories or pseudo-random undersampling patterns, which can require specialized sequence implementations. In this work, we present a practical protocol-level acceleration framework enabled by complementary orthogonal Cartesian acquisitions that can be executed using standard scanner settings. Specifically, each contrast is acquired with reduced phase-encoding matrix size along a contrast-specific axis (a fourfold reduction along one contrast-specific phase-encoding dimension), producing rapidly acquired volumes with axis-specific resolution loss but complementary spatial-frequency content across contrasts. We propose the Frequency Attention Residual Denoising (FARD) network, a multi-contrast fusion model that leverages both spatial and frequency-domain processing to enhance apparent isotropic detail on a 1mm<sup>3</sup> grid for all contrasts from these complementary inputs. We evaluate the approach using two complementary settings: a prospectively acquired clinical-scanner dataset that reflects real acquisition and vendor-reconstruction conditions, and a controlled retrospective simulation on BraTS-GLI 2024, which provides large-scale pathology-containing multi-contrast data but does not model prospective scanner effects. On the prospective cohort, the proposed framework achieves approximately 4× protocol acceleration (10.4 to 2.6 min), provides the strongest or near-strongest PSNR and SSIM among the evaluated reference-free methods, and approaches reference-guided performance without requiring a high-resolution reference contrast. BraTS experiments provide complementary large-scale evidence of algorithmic effectiveness under controlled retrospective degradation.