Magnetic resonance imaging (MRI) is crucial in diagnosing various abdominal conditions and anomalies. Traditional MRI scans often yield anisotropic data due to technical constraints, resulting in varying resolutions across spatial dimensions, which limits diagnostic accuracy and volumetric analysis. Super-resolution (SR) techniques aim to address these limitations by reconstructing isotropic high-resolution images from anisotropic data. However, current SR methods often rely on indirect mappings and limited training data, focusing mainly on two-dimensional improvements rather than achieving true three-dimensional isotropy. We introduce SIMPLE, a Simultaneous Multi-Plane Self-Supervised Learning approach for isotropic MRI restoration from anisotropic data. Our method leverages existing anisotropic clinical data acquired in different planes, bypassing the need for simulated downsampling processes. By considering the inherent three-dimensional nature of MRI data, SIMPLE ensures realistic isotropic data generation rather than solely improving through-plane slices. This approach flexibility allows it to be extended to multiple contrast types and acquisition methods commonly used in clinical settings. Our experiments show that SIMPLE outperforms state-of-the-art methods both quantitatively using the Kernel Inception Distance (KID) and semi-quantitatively through radiologist evaluations. The generated isotropic volume facilitates more accurate volumetric analysis and 3D reconstructions, promising significant improvements in clinical diagnostic capabilities.

本研究解决了因技术限制导致的MRI扫描各向异性数据问题，影响了诊断准确性和体积分析。提出了一种新的方法SIMPLE，通过同时多平面自监督学习从各向异性数据中重建各向同性的高分辨率图像，显著提升了图像质量和临床诊断能力。实验结果显示，SIMPLE在量化和半量化评估中超过了现有的先进方法。

简单：从各向异性数据中进行各向同性MRI恢复的同时多平面自监督学习