Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework

Yi, Zheyuan1,2,3; Liu, Yilong1,2; Zhao, Yujiao1,2; Xiao, Linfang1,2; Leong, Alex T. L.1,2; Feng, Yanqiu4; Chen, Fei3; Wu, Ed X.1,2

2021-06-01

10.1002/mrm.28674

MAGNETIC RESONANCE IN MEDICINE   影响因子和分区

0740-3194

1522-2594

["Purpose To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework.","Methods A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T-1-weighted, T-2-weighted, fluid-attenuated inversion recovery, and T-1-weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes.","Results The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallel-imaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling strategy where the phase-encoding direction among different contrasts is orthogonally alternated.","Conclusion The proposed MC-HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil-sensitivity calibration."]

Hankel tensor completion joint calibrationless reconstruction low rank multicontrast magnetic resonance imaging (MRI)

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 Key Technologies for Alzheimer's Disease Diagnosis and Treatment[2018B030336001] ; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Fund[2019008]

Radiology, Nuclear Medicine & Medical Imaging

Radiology, Nuclear Medicine & Medical Imaging

WOS:000614025900001

WILEY

20210609883663

Approximation theory ; Iterative methods ; Learning to rank ; Recovery ; Singular value decomposition ; Tensors

Mathematics:921

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, Guangdong, Peoples R China

Yi, Zheyuan,Liu, Yilong,Zhao, Yujiao,et al. Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework[J]. MAGNETIC RESONANCE IN MEDICINE,2021,85(6):3256-3271.

Yi, Zheyuan.,Liu, Yilong.,Zhao, Yujiao.,Xiao, Linfang.,Leong, Alex T. L..,...&Wu, Ed X..(2021).Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework.MAGNETIC RESONANCE IN MEDICINE,85(6),3256-3271.

Yi, Zheyuan,et al."Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low-rank Hankel tensor completion framework".MAGNETIC RESONANCE IN MEDICINE 85.6(2021):3256-3271.