Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks
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 vari...
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| Published in: | Scientific reports Vol. 10; no. 1; p. 19144 |
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| Main Authors: | , , , , , |
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05-11-2020
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| Abstract | 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%. |
|---|---|
| AbstractList | 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%. Abstract 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%. 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%. |
| ArticleNumber | 19144 |
| Author | Knoll, Florian Sharafi, Azadeh Hammernik, Kerstin Johnson, Patricia M. Regatte, Ravinder R. Zibetti, Marcelo V. W. |
| Author_xml | – sequence: 1 givenname: Marcelo V. W. orcidid: 0000-0003-2856-3625 surname: Zibetti fullname: Zibetti, Marcelo V. W. email: Marcelo.WustZibetti@nyulangone.org organization: Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine – sequence: 2 givenname: Patricia M. orcidid: 0000-0003-1547-9969 surname: Johnson fullname: Johnson, Patricia M. organization: Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine – sequence: 3 givenname: Azadeh orcidid: 0000-0001-9572-4922 surname: Sharafi fullname: Sharafi, Azadeh organization: Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine – sequence: 4 givenname: Kerstin orcidid: 0000-0002-2734-1409 surname: Hammernik fullname: Hammernik, Kerstin organization: Department of Computing, Imperial College London – sequence: 5 givenname: Florian orcidid: 0000-0001-5357-8656 surname: Knoll fullname: Knoll, Florian organization: Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine – sequence: 6 givenname: Ravinder R. orcidid: 0000-0002-4607-7682 surname: Regatte fullname: Regatte, Ravinder R. organization: Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine |
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| Snippet | 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... Abstract In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating... In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating frame... In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin-lattice relaxation time in the rotating frame... |
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| SubjectTerms | 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 |
| Title | Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks |
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