SIMPLE：基于各向异性数据的各向同性MRI恢复的同步多平面自监督学习
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data
AI总结：
提出SIMPLE，一种同步多平面自监督学习方法，利用现有各向异性临床数据实现各向同性MRI恢复，无需模拟降采样，在脑部和腹部数据集上优于现有方法，提升诊断能力。
摘要：
磁共振成像（MRI）在诊断各种腹部疾病和异常中至关重要。传统的MRI扫描由于技术限制，通常产生各向异性数据，导致空间维度上的分辨率不同，这限制了诊断准确性和体积分析。超分辨率（SR）技术旨在通过从各向异性数据重建各向同性高分辨率图像来解决这些限制。然而，当前的SR方法通常依赖于间接映射和稀缺的3D各向同性数据进行训练，主要关注二维增强，而不是实现真正的三维各向同性。我们引入了“SIMPLE”，一种用于从各向异性数据进行各向同性MRI恢复的同步多平面自监督学习方法。我们的方法利用在不同平面获取的现有各向异性临床数据，绕过了模拟降采样过程的需求。通过考虑MRI数据固有的三维特性，SIMPLE确保了逼真的各向同性数据生成，而不仅仅是改善层间切片。该方法的灵活性使其能够扩展到临床环境中常用的多种对比类型和采集方法。我们在两个不同的数据集（脑部和腹部）上的实验表明，SIMPLE在定量上使用核感知距离（KID）、半定量上通过放射科医生评估、定性上通过傅里叶域分析，均优于最先进的方法。生成的各向同性体积有助于更准确的体积分析和3D重建，有望显著提高临床诊断能力。
英文摘要：
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 depend on indirect mappings and scarce 3D isotropic data for training, primarily focusing on two-dimensional enhancements rather than achieving genuine 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's flexibility allows it to be extended to multiple contrast types and acquisition methods commonly used in clinical settings. Our experiments on two distinct datasets (brain and abdomen) show that SIMPLE outperforms state-of-the-art methods both quantitatively using the Kernel Inception Distance (KID), semi-quantitatively through radiologist evaluations, and qualitatively through Fourier domain analysis. The generated isotropic volume facilitates more accurate volumetric analysis and 3D reconstructions, promising significant improvements in clinical diagnostic capabilities.
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