Parallel imaging and compressed sensing combined framework for accelerating high-resolution diffusion tensor imaging using inter-image correlation.
OBJECTIVE
Increasing acquisition efficiency is always a challenge in high-resolution diffusion tensor imaging (DTI), which has low signal-to-noise ratio and is sensitive to reconstruction artifacts. In this study, a parallel imaging (PI) and compressed sensing (CS) combined framework is proposed, which features motion error correction, PI calibration, and sparsity model using inter-image correlation tailored for high-resolution DTI.


METHODS
The proposed method, named anisotropic sparsity SPIRiT, consists of three steps: (i) motion-induced phase error estimation, (ii) initial CS reconstruction and PI kernel calibration, and (iii) final reconstruction combining PI and CS. Inter-image correlation of diffusion-weighted images are used through anisotropic signals for improved sparsity. A specific implementation based on multishot variable density spiral DTI is used to demonstrate the method.


RESULTS
The proposed reconstruction method was compared with CG-SENSE, CS-based joint reconstruction, and PI and CS combined methods with L1 and joint sparsity regularization, in brain DTI experiments at acceleration factors of 3 to 5. Both qualitative and quantitative results demonstrated that the proposed method resulted in better preserved image quality and more accurate DTI parameters than other methods.


CONCLUSIONS
The proposed method can accelerate high-resolution DTI acquisition effectively by using the sharable information among different diffusion encoding directions.
Department of Electrical Engineering, Stanford University, Stanford, California, USA; Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
Xinwei
X
Shi
Xiaodong
X
Ma
Wenchuan
W
Wu
Feng
F
Huang
Chun
C
Yuan
Hua
H
Guo
D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D013223:Statistics as Topic
anisotropic sparsity; compressed sensing; diffusion tensor imaging; parallel imaging; variable density spiral