mcLARO : Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping
© 2023 International Society for Magnetic Resonance in Medicine..
PURPOSE: To develop a method for rapid sub-millimeter T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO).
METHODS: A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method with R = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard.
RESULTS: The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal.
CONCLUSION: mcLARO enabled fast sub-millimeter T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 2023 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:91 |
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| Enthalten in: |
Magnetic resonance in medicine - 91(2023), 1 vom: 01. Jan., Seite 344-356 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Jinwei [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 15.11.2023 Date Revised 22.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/mrm.29854 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 100 | 1 | |a Zhang, Jinwei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a mcLARO |b Multi-contrast learned acquisition and reconstruction optimization for simultaneous quantitative multi-parametric mapping |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Completed 15.11.2023 | ||
| 500 | |a Date Revised 22.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 International Society for Magnetic Resonance in Medicine. | ||
| 520 | |a PURPOSE: To develop a method for rapid sub-millimeter T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan using multi-contrast learned acquisition and reconstruction optimization (mcLARO) | ||
| 520 | |a METHODS: A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and magnetic susceptibility. The proposed mcLARO optimized both the multi-contrast k-space under-sampling pattern and image reconstruction based on image feature fusion using a deep learning framework. The proposed mcLARO method with R = 8 $$ R=8 $$ under-sampling was validated in a retrospective ablation study and compared with other deep learning reconstruction methods, including MoDL and Wave-MoDL, using fully sampled data as reference. Various under-sampling ratios in mcLARO were investigated. mcLARO was also evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard | ||
| 520 | |a RESULTS: The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without the multi-contrast sampling pattern optimization or image feature fusion module. The under-sampling ratio experiment showed that higher under-sampling ratios resulted in blurrier images and lower precision of quantitative values. The prospective study showed that small or negligible bias and narrow 95% limits of agreement on regional T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). mcLARO outperformed MoDL and Wave-MoDL in terms of image sharpness, noise suppression, and artifact removal | ||
| 520 | |a CONCLUSION: mcLARO enabled fast sub-millimeter T1 , T2 , T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and QSM mapping in a single scan | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a learned acquisition and reconstruction optimization | |
| 650 | 4 | |a multi-contrast pulse sequence | |
| 650 | 4 | |a quantitative multi-parametric mapping | |
| 700 | 1 | |a Nguyen, Thanh D |e verfasserin |4 aut | |
| 700 | 1 | |a Solomon, Eddy |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qihao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jiahao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hang |e verfasserin |4 aut | |
| 700 | 1 | |a Spincemaille, Pascal |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yi |e verfasserin |4 aut | |
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| 773 | 1 | 8 | |g volume:91 |g year:2023 |g number:1 |g day:01 |g month:01 |g pages:344-356 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1002/mrm.29854 |3 Volltext |
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