Yanjie Zhu1, Yuanyuan Liu1,2,3, Leslie Ying4, Xin Liu1, Hairong Zheng1, Dong Liang1. 1. Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 2. University of Chinese Academy of Sciences, Beijing, China. 3. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China. 4. Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York.
Abstract
PURPOSE: To develop and evaluate a fast imaging method based on signal-compensated low-rank plus sparse matrix decomposition to accelerate data acquisition for biexponential brain T1ρ mapping (Bio-SCOPE). METHODS: Two novel strategies were proposed to improve reconstruction performance. A variable-rate undersampling scheme was used with a varied acceleration factor for each k-space along the spin-lock time direction, and a modified nonlinear thresholding scheme combined with a feature descriptor was used for Bio-SCOPE reconstruction. In vivo brain T1ρ mappings were acquired from 4 volunteers. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled by net acceleration rates (R) of 4.6 and 6.1. Reference values were obtained from the fully sampled data. The agreement between the accelerated T1ρ measurements and reference values was assessed with Bland-Altman analyses. Prospectively undersampled data with R = 4.6 and R = 6.1 were acquired from 1 volunteer. RESULTS: T1ρ -weighted images were successfully reconstructed using Bio-SCOPE for R = 4.6 and 6.1 with signal-to-noise ratio variations <1 dB and normalized root mean square errors <4%. Accelerated and reference T1ρ measurements were in good agreement for R = 4.6 (T1ρ s : 18.6651 ± 1.7786 ms; T1ρ l : 88.9603 ± 1.7331 ms) and R = 6.1 (T1ρ s : 17.8403 ± 3.3302 ms; T1ρ l : 88.0275 ± 4.9606 ms) in the Bland-Altman analyses. T1ρ parameter maps from prospectively undersampled data also show reasonable image quality using the Bio-SCOPE method. CONCLUSION: Bio-SCOPE achieves a high net acceleration rate for biexponential T1ρ mapping and improves reconstruction quality by using a variable-rate undersampling data acquisition scheme and a modified soft-thresholding algorithm in image reconstruction.
PURPOSE: To develop and evaluate a fast imaging method based on signal-compensated low-rank plus sparse matrix decomposition to accelerate data acquisition for biexponential brain T1ρ mapping (Bio-SCOPE). METHODS: Two novel strategies were proposed to improve reconstruction performance. A variable-rate undersampling scheme was used with a varied acceleration factor for each k-space along the spin-lock time direction, and a modified nonlinear thresholding scheme combined with a feature descriptor was used for Bio-SCOPE reconstruction. In vivo brain T1ρ mappings were acquired from 4 volunteers. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled by net acceleration rates (R) of 4.6 and 6.1. Reference values were obtained from the fully sampled data. The agreement between the accelerated T1ρ measurements and reference values was assessed with Bland-Altman analyses. Prospectively undersampled data with R = 4.6 and R = 6.1 were acquired from 1 volunteer. RESULTS: T1ρ -weighted images were successfully reconstructed using Bio-SCOPE for R = 4.6 and 6.1 with signal-to-noise ratio variations <1 dB and normalized root mean square errors <4%. Accelerated and reference T1ρ measurements were in good agreement for R = 4.6 (T1ρ s : 18.6651 ± 1.7786 ms; T1ρ l : 88.9603 ± 1.7331 ms) and R = 6.1 (T1ρ s : 17.8403 ± 3.3302 ms; T1ρ l : 88.0275 ± 4.9606 ms) in the Bland-Altman analyses. T1ρ parameter maps from prospectively undersampled data also show reasonable image quality using the Bio-SCOPE method. CONCLUSION: Bio-SCOPE achieves a high net acceleration rate for biexponential T1ρ mapping and improves reconstruction quality by using a variable-rate undersampling data acquisition scheme and a modified soft-thresholding algorithm in image reconstruction.
Authors: Rajiv G Menon; Marcelo V W Zibetti; Rajan Jain; Yulin Ge; Ravinder R Regatte Journal: J Magn Reson Imaging Date: 2020-11-15 Impact factor: 4.813