Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

• Published in: NeuroImage (Orlando, Fla.) Vol. 263; p. 119638
• Main Authors: Nunez-Gonzalez, L., Nagtegaal, M.A., Poot, D.H.J., de Bresser, J., van Osch, M.J.P., Hernandez-Tamames, J.A., Vos, F.M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2022; Elsevier Limited; Elsevier
• Subjects: Accuracy; Algorithms; Brain; Brain - diagnostic imaging; Cerebral Cortex; Cerebrospinal fluid; Dictionaries; Fingerprinting; Gray Matter - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Methods; Multi-component MR Fingerprinting; Myelin; Parameter estimation; Phantoms, Imaging; Quantitative MRI; Repeatability; Segmentation; Simulation; Software; Sparsity; Substantia alba; Substantia grisea; White Matter - diagnostic imaging; Repeatability; Brain; Accuracy; Segmentation; Quantitative MRI; Multi-component MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facili...