Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion

Liu, Yilong1,2; Yi, Zheyuan1,2,3; Zhao, Yujiao1,2; Chen, Fei3; Feng, Yanqiu4; Guo, Hua5; Leong, Alex T. L.1,2; Wu, Ed X.1,2

2020-09-23

10.1002/mrm.28480

MAGNETIC RESONANCE IN MEDICINE   影响因子和分区

0740-3194

1522-2594

Purpose To provide joint calibrationless parallel imaging reconstruction of highly accelerated multislice 2D MR k-space data. Methods Adjacent image slices in multislice MR data have similar coil sensitivity maps, spatial support, and image content. Such similarities can be utilized to improve image quality by reconstructing multiple slices jointly with low-rank tensor completion. Specifically, the multichannel k-space data from multiple slices are constructed into a block-wise Hankel tensor and iteratively updated by promoting tensor low-rankness through higher-order SVD. This multislice block-wise Hankel tensor completion was implemented for 2D spiral and Cartesian k-space undersampling where sampling patterns vary between adjacent slices. The approach was evaluated with human brain MR data and compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. Results The proposed multislice block-wise Hankel tensor completion approach robustly reconstructed highly undersampled multislice 2D spiral and Cartesian data. It produced substantially lower level of artifacts compared to the traditional single-slice simultaneous autocalibrating and k-space estimation reconstruction. Quantitative evaluation using error maps and root mean square error demonstrated its significantly improved performance in terms of residual artifacts and root mean square error. Conclusion Our proposed multislice block-wise Hankel tensor completion method exploits the similar coil sensitivity and image content within multislice MR data through a tensor completion framework. It offers a new and effective approach to acquire and reconstruct highly undersampled multislice MR data in a calibrationless manner.

Hankel tensor completion low-rank multislice parallel imaging

SCI ; EI

英语

其他

Hong Kong Research Grant Council[R7003-19/C7048-16G/HKU17112120][HKU17103819/HKU17104020] ; Guangdong Key Technologies for Treatment of Brain Disorders[2018B030332001] ; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Fund[2019008] ; HKU Seed Fund for Basic Research[104005866]

Radiology, Nuclear Medicine & Medical Imaging

Radiology, Nuclear Medicine & Medical Imaging

WOS:000571971000001

WILEY

20203909233022

Iterative methods ; Image enhancement ; Errors ; Mean square error ; Image reconstruction

Algebra:921.1 ; Numerical Methods:921.6 ; Mathematical Statistics:922.2

CLINICAL MEDICINE

Web of Science

1.Univ Hong Kong, Lab Biomed Imaging & Signal Proc, Hong Kong, Peoples R China
2.Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
4.Southern Med Univ, Sch Biomed Engn, Guangzhou, Peoples R China
5.Tsinghua Univ, Dept Biomed Engn, Ctr Biomed Imaging Res, Beijing, Peoples R China

Liu, Yilong,Yi, Zheyuan,Zhao, Yujiao,et al. Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion[J]. MAGNETIC RESONANCE IN MEDICINE,2020,85:897-911.

Liu, Yilong.,Yi, Zheyuan.,Zhao, Yujiao.,Chen, Fei.,Feng, Yanqiu.,...&Wu, Ed X..(2020).Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion.MAGNETIC RESONANCE IN MEDICINE,85,897-911.

Liu, Yilong,et al."Calibrationless parallel imaging reconstruction for multislice MR data using low-rank tensor completion".MAGNETIC RESONANCE IN MEDICINE 85(2020):897-911.