Rapid mono and biexponential 3D-T 1ρ 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 ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T maps obtained by deep learning-based variationa...
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| Veröffentlicht in: | Scientific reports 2020-11, Vol.10 (1), p.19144 |
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| creator | Zibetti, Marcelo V W Johnson, Patricia M Sharafi, Azadeh Hammernik, Kerstin Knoll, Florian Regatte, Ravinder R |
| description | 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
) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T
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
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
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
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%. |
| format | Article |
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) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T
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
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
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
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) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T
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
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
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
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) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T
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
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
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
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><cop>England</cop><pmid>33154515</pmid><orcidid>https://orcid.org/0000-0001-5357-8656</orcidid><orcidid>https://orcid.org/0000-0001-9572-4922</orcidid><orcidid>https://orcid.org/0000-0003-1547-9969</orcidid><orcidid>https://orcid.org/0000-0002-4607-7682</orcidid><orcidid>https://orcid.org/0000-0002-2734-1409</orcidid><orcidid>https://orcid.org/0000-0003-2856-3625</orcidid></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Springer Nature OA Free Journals; Nature Free; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Cartilage, Articular - diagnostic imaging Female Humans Image Processing, Computer-Assisted - methods Imaging, Three-Dimensional - methods Knee Joint - diagnostic imaging Magnetic Resonance Imaging - methods Male Retrospective Studies Young Adult |
| title | Rapid mono and biexponential 3D-T 1ρ mapping of knee cartilage using variational networks |
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