Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks. Sci Rep 2020 Nov 05;10(1):19144
Date
11/07/2020Pubmed ID
33154515Pubmed Central ID
PMC7645759DOI
10.1038/s41598-020-76126-xScopus ID
2-s2.0-85095422024 (requires institutional sign-in at Scopus site) 11 CitationsAbstract
In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin-lattice relaxation time in the rotating frame (T1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1ρ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T1ρ mapping, with MNAD around 5% for AF = 2, which increases almost linearly with the AF to an MNAD of 13% for AF = 8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF = 2 and reaching MNAD of 13.1% for AF = 8. The VN was able to produce 3D-T1ρ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.
Author List
Zibetti MVW, Johnson PM, Sharafi A, Hammernik K, Knoll F, Regatte RRAuthor
Azadeh Sharafi PhD Assistant Professor in the Radiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AdultCartilage, Articular
Female
Humans
Image Processing, Computer-Assisted
Imaging, Three-Dimensional
Knee Joint
Magnetic Resonance Imaging
Male
Retrospective Studies
Young Adult
