Abstract
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: this https URL
Abstract (translated)
临床磁共振成像（MRI）常通过采用高平面分辨率、低层间分辨率的各向异性扫描来缩短采集时间，因此需采集多角度视图以提供互补的解剖信息。传统多视图融合方法依赖图像配准与插值，可能损害精细结构细节。近期基于深度学习的超分辨率（SR）方法在单视图图像增强上表现优异，但其临床可靠性常受限于大规模训练数据集的需求，导致对队列级先验的依赖增强。自监督策略提供了一种替代方案，可直接从目标扫描数据中学习。既往工作要么忽略多视图信息的存在，要么假设图像预对齐后平面信息可监督层间重建，然而该假设在临床场景中极少成立。本研究提出单被试隐式多视图MRI超分辨率（SIMS-MRI）框架，该框架仅基于单患者各向异性多视图扫描运行，无需预处理或后处理。我们的方法结合多分辨率哈希编码隐式表示与学习的视图间对齐，生成空间一致的各向同性重建。我们在模拟脑部与临床前列腺MRI数据集上验证了SIMS-MRI流程。代码将公开以确保可复现性：此 https URL
URL
https://arxiv.org/abs/2603.22627
