Multiscale low-rank plus sparsity modeling in fast ultra-high-field cerebrovascular 4D Flow imaging
10.3760/cma.j.cn112149-20230627-00442
- VernacularTitle:多尺度低秩加稀疏模型在加速超高场脑部4D Flow成像中的应用
- Author:
Xueying ZHAO
1
;
Ruiyu CAO
;
Yinghua ZHU
;
Aiqi SUN
;
Jiabin SU
;
Wei NI
;
He WANG
Author Information
1. 复旦大学类脑人工智能科学与技术研究院，上海 200433
- Keywords:
Magnetic resonance imaging;
Ultra-high-field;
Cerebrovascular 4D Flow;
Compressed sensing;
Multi-scale low-rank model
- From:
Chinese Journal of Radiology
2023;57(11):1180-1186
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the application of multiscale low-rank plus sparsity (MLRS) modeling in fast ultra-high-field intracranial 4D Flow imaging.Methods:Ten healthy volunteers, 5 males and 5 females, aged 23-35 (29±4) years old, recruited from October 2022 to January 2023 at Huashan Hospital of Fudan University, were prospectively collected. A MLRS model acceleration algorithm was proposed according to the characteristics of 4D Flow data based on the multiscale low-rank (MLR) model. Firstly, full sampling brain 4D Flow scans were performed on healthy volunteers using 7.0 T MR, and the acquired data were under-sampled with Gaussian distributions at different acceleration rates (R of 4, 8, 12, and 16, respectively). The root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) of the compressed sensing algorithm (CS), low-rank plus sparse algorithm (L+S), MLR, and MLRS model were calculated at different acceleration rates, with fully sampled data as reference. And the comparison of models was performed using the paired-samples t-test or Wilcoxon signed rank test. Pearson′s test was used to assess the correlation between hemodynamic parameters of the 4 algorithms and the fully sampled reference values at different acceleration rates, and the correlation coefficients were compared using Wilcoxon signed rank test. Results:The RMSE under the same acceleration rates was MLRS, MLR, L+S, and CS models in ascending order, and the RMSE of the MLRS model was significantly lower than that of the MLR, L+S, and CS models ( P<0.05); the PSNR was MLRS, MLR, L+S, and CS models in descending order, and the PSNR of the MLRS model was significantly higher than that of the MLR, L+S, and CS model ( P<0.05). The correlation coefficients between the blood flow velocity measured by the MLRS model and the reference value were significantly higher than those of the MLR, L+S, and CS models for different acceleration rates ( P<0.05). Conclusion:The proposed MLRS algorithm is capable of accelerating ultra-high-field 4D Flow MR imaging of the brain while guaranteeing the image quality, and the MLRS model has higher reconstruction accuracy compared with conventional acceleration models at the same acceleration rate.
