Giraldo Diana L, Khan Hamza, Pineda Gustavo, Liang Zhihua, Lozano-Castillo Alfonso, Van Wijmeersch Bart, Woodruff Henry C, Lambin Philippe, Romero Eduardo, Peeters Liesbet M, Sijbers Jan
Imec-Vision Lab, University of Antwerp, Antwerp, Belgium.
μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium.
Front Neurosci. 2024 Oct 22;18:1473132. doi: 10.3389/fnins.2024.1473132. eCollection 2024.
Magnetic resonance imaging (MRI) is crucial for diagnosing and monitoring of multiple sclerosis (MS) as it is used to assess lesions in the brain and spinal cord. However, in real-world clinical settings, MRI scans are often acquired with thick slices, limiting their utility for automated quantitative analyses. This work presents a single-image super-resolution (SR) reconstruction framework that leverages SR convolutional neural networks (CNN) to enhance the through-plane resolution of structural MRI in people with MS (PwMS).
Our strategy involves the supervised fine-tuning of CNN architectures, guided by a content loss function that promotes perceptual quality, as well as reconstruction accuracy, to recover high-level image features.
Extensive evaluation with MRI data of PwMS shows that our SR strategy leads to more accurate MRI reconstructions than competing methods. Furthermore, it improves lesion segmentation on low-resolution MRI, approaching the performance achievable with high-resolution images.
Results demonstrate the potential of our SR framework to facilitate the use of low-resolution retrospective MRI from real-world clinical settings to investigate quantitative image-based biomarkers of MS.
磁共振成像（MRI）对于多发性硬化症（MS）的诊断和监测至关重要，因为它用于评估脑和脊髓中的病变。然而，在实际临床环境中，MRI扫描通常采用厚层扫描，这限制了其在自动定量分析中的效用。这项工作提出了一种单图像超分辨率（SR）重建框架，该框架利用SR卷积神经网络（CNN）来提高MS患者（PwMS）结构MRI的层间分辨率。
我们的策略包括对CNN架构进行有监督的微调，以促进感知质量以及重建准确性的内容损失函数为指导，以恢复高级图像特征。
对PwMS的MRI数据进行的广泛评估表明，我们的SR策略比竞争方法能带来更准确的MRI重建。此外，它还改善了低分辨率MRI上的病变分割，接近高分辨率图像可实现的性能。
结果证明了我们的SR框架在促进使用来自实际临床环境的低分辨率回顾性MRI来研究基于定量图像的MS生物标志物方面的潜力。
