Deep learning-based super-resolution (SR) techniques offer a powerful tool to enhance the spatial resolution of magnetic resonance imaging (MRI) without increasing scan time. However, most supervised SR methods rely on high-resolution, high-signal-to-noise ratio (SNR) reference data for training, which are difficult to...
Mu-Ye Zhang, Hong-Jia Yang, Zi-Han Li et al.· Imaging neuroscience· 0 citations
A clinically feasible SANDI model is developed that reduces data requirements and model complexity while preserving sensitivity to cellular‐level microstructural features.
Hansol Lee, Kwok-Shing Chan, Yixin Ma et al.· Magnetic Resonance in Medici...· 0 citations
This review summarizes the most recent developments in advanced MRI for MS, highlighting biomarkers that were newly added in the 2024 revisions of the McDonald criteria, describing the ongoing importance of established imaging measures, and reviewing the applications of artificial intelligence in clinical practice.
N. Michaelson, Sittaya Buathong, C. Treaba et al.· Radiology· 0 citations
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