Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning. Issue 1 (5th February 2021)

• Record Type: Journal Article
• Authors: Hermann, Ingo; Martínez‐Heras, Eloy; Rieger, Benedikt; Schmidt, Ralf; Golla, Alena‐Kathrin; Hong, Jia‐Sheng; Lee, Wei‐Kai; Yu‐Te, Wu; Nagtegaal, Martijn; Solana, Elisabeth; Llufriu, Sara; Gass, Achim; Schad, Lothar R.; Weingärtner, Sebastian; Zöllner, Frank G.
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 1(2021)
• Page Start: 471
• Page End: 486
• Publication Date: 2021-02-05
• Subjects: deep learning reconstruction -- magnetic resonance fingerprinting -- T1 mapping -- T2∗ mapping; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28688
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo‐planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of T 1 and T 2 ∗ in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF T 1 and T 2 ∗ parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the T 1 and T 2 ∗ parametric maps, and the WM and GM probability maps. Results: Deep learning‐based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for T 1 (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for T 2 ∗ (deviations 6.0%). Conclusions: MRF is a fast and robust tool for quantitative T 1 and T 2 ∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning. Abstract : Click here for author‐reader discussions