Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks

• Published in: Scientific reports Vol. 10; no. 1; p. 19144
• Main Authors: Zibetti, Marcelo V. W., Johnson, Patricia M., Sharafi, Azadeh, Hammernik, Kerstin, Knoll, Florian, Regatte, Ravinder R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05-11-2020; Nature Publishing Group
• Subjects: 692/700/1421/1628; 692/700/1421/2025; Cartilage; Coefficient of variation; Humanities and Social Sciences; Knee; Magnetic resonance imaging; Mapping; multidisciplinary; Science; Science (multidisciplinary); Variance analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-76126-x

In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating frame (T
1ρ
) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T
1ρ
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 T
1ρ
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 T
1ρ
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-T
1ρ
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%.