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arXiv · 2509.18593

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

Xiaoman Wu·Lubin Gan·Siying Wu·Jing Zhang·Yunwei Ou·Xiaoyan Sun
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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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Xiaoman Wu, Lubin Gan, Siying Wu, Jing Zhang, Yunwei Ou, Xiaoyan Sun. 2025-09-23. SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution. https://arxiv.org/abs/2509.18593

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