ArticleRadiology advances2025
Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI.
Article in Radiology advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Memory-efficient image reconstruction using diffusion models for accelerated 3D non-Cartesian UTE imaging.Magma (New York, N.Y.) · 2026Article
- 3D Brachial Plexus Neurography With Variable-Rate Selective Excitation RF Pulses.Journal of magnetic resonance imaging : JMRI · 2026Article
- MR Imaging of Wallenberg Syndrome Using Accelerated 3D-FLAIR with T2 Preparation and Magnetization Transfer Contrast: A Case Report.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Deep learning-accelerated 3D flair for white matter lesion detection in multiple sclerosis: a feasibility study.Neuroradiology · 2026Article
- Physics-Guided Deep Learning for Interpretable Biomedical Image Reconstruction and Pattern Recognition in Diagnostic Frameworks.Bioengineering (Basel, Switzerland) · 2026Article
- Optimized TMagnetic resonance in medicine · 2026Article
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10 authors.
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Abstract
Background: Physics-informed deep learning (DL) reconstructions show promise in accelerating MRI yet have not been extensively validated, particularly for 3D fluid-attenuated inversion recovery (FLAIR) sequence. Purpose: To evaluate the diagnostic quality and interchangeability of DL-based 3D FLAIR with a state-of-the-art acceleration technique (wave-controlled aliasing in parallel imaging [Wave-CAIPI] FLAIR) in a clinical setting with 3 T brain MRI. Materials and Methods: Participants undergoing evaluation for demyelinating disease between October and December of 2023 were prospectively enrolled at a single center. For each participant, state-of-the-art Wave-CAIPI FLAIR and a resolution-matched 6-fold-under-sampled Cartesian FLAIR acquisition with DL reconstruction were performed at 3-T system (MAGNETOM Vida, Siemens Healthineers, Erlangen, Germany). Four neuroradiologists evaluated overall image quality, anatomic conspicuity, lesion conspicuity, and imaging artifacts. Lesion count, volume, and regional brain volume were compared between imaging methods. Inter-reader agreement was assessed using quadratic weighted Cohen's kappa and Kendall's correlation coefficient. Agreement of continuous metrics was evaluated using intraclass correlation coefficients (ICCs), linear regression, and Bland-Altman analysis. Interchangeability regarding the quantitative metrics was evaluated with individual equivalence index (IEI). Results: Totally, 88 participants (61 women [69%], 47 ± 13 years) were evaluated. DL-FLAIR reduced scan time (1:53 vs. 2:50) and showed higher overall image quality, anatomic conspicuity, lesion conspicuity, and imaging artifacts compared with state-of-the-art technique (all Conclusion: Deep learning reconstruction of 3D-FLAIR provides higher image quality compared to a state-of-the-art technique with 30% less scan time while maintaining excellent agreement and interchangeability in quantitative evaluation.
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