用单个患者多视角MRI实现无预处理的高分辨率重建
Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations
- 用隐式神经表示融合多视角信息，自适应对齐视图
- 在脑和前列腺MRI上实现各向同性重建，提升结构细节
- 无需配准或大规模数据，适合临床个体化应用
临床MRI常采用各向异性扫描，即平面内分辨率高、层面间分辨率低，以缩短采集时间。因此需获取多个方位图像以补充解剖信息。传统整合方法依赖配准后插值，易损失细微结构。现有深度学习超分辨率方法虽表现良好，但临床可靠性受限于大规模训练数据需求，过度依赖群体先验。自监督策略可直接从目标扫描中学习，避免此问题。先前工作要么忽略多视角信息，要么假设平面信息可指导层面重建，前提是图像已对齐，但在临床中往往不成立。本文提出单患者隐式多视角磁共振超分辨率框架（SIMS-MRI），仅使用单个患者的各向异性多视角扫描，无需预处理或后处理。方法结合多分辨率哈希编码的隐式表示与学习到的跨视角对齐机制，生成空间一致的各向同性重建结果。在模拟脑部和临床前列腺MRI数据集上验证了该方法的有效性。代码将公开以保障可复现性：https://github.com/abhshkt/SIMS-MRI
原文摘要 · Abstract (English)
Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore acquired to provide complementary anatomical information. Conventional integration of these views relies on registration followed by interpolation, which can degrade fine structural details. Recent deep learning-based super-resolution (SR) approaches have demonstrated strong performance in enhancing single-view images. However, their clinical reliability is often limited by the need for large-scale training datasets, resulting in increased dependence on cohort-level priors. Self-supervised strategies offer an alternative by learning directly from the target scans. Prior work either neglects the existence of multi-view information or assumes that in-plane information can supervise through-plane reconstruction under the assumption of pre-alignment between images. However, this assumption is rarely satisfied in clinical settings. In this work, we introduce Single-Subject Implicit Multi-View Super-Resolution for MRI (SIMS-MRI), a framework that operates solely on anisotropic multi-view scans from a single patient without requiring pre- or post-processing. Our method combines a multi-resolution hash-encoded implicit representation with learned inter-view alignment to generate a spatially consistent isotropic reconstruction. We validate the SIMS-MRI pipeline on both simulated brain and clinical prostate MRI datasets. Code will be made publicly available for reproducibility: https://github.com/abhshkt/SIMS-MRI
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