SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution

• URL: http://arxiv.org/abs/2509.18593v1
• Date: Tue, 23 Sep 2025 03:24:32 GMT
• Title: SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution
• Authors: Xiaoman Wu, Lubin Gan, Siying Wu, Jing Zhang, Yunwei Ou, Xiaoyan Sun,
• Abstract summary: MC-MRI SR aims to enhance low-resolution (LR) contrasts leveraging high-resolution (HR) references.<n>Main challenge lies in maintaining spatial-semantic consistency.
• Score: 11.194678655584788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-contrast Magnetic Resonance Imaging super-resolution (MC-MRI SR) aims to enhance low-resolution (LR) contrasts leveraging high-resolution (HR) references, shortening acquisition time and improving imaging efficiency while preserving anatomical details. The main challenge lies in maintaining spatial-semantic consistency, ensuring anatomical structures remain well-aligned and coherent despite structural discrepancies and motion between the target and reference images. Conventional methods insufficiently model spatial-semantic consistency and underuse frequency-domain information, which leads to poor fine-grained alignment and inadequate recovery of high-frequency details. In this paper, we propose the Spatial-Semantic Consistent Model (SSCM), which integrates a Dynamic Spatial Warping Module for inter-contrast spatial alignment, a Semantic-Aware Token Aggregation Block for long-range semantic consistency, and a Spatial-Frequency Fusion Block for fine structure restoration. Experiments on public and private datasets show that SSCM achieves state-of-the-art performance with fewer parameters while ensuring spatially and semantically consistent reconstructions.

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