Parallel imaging and compressed sensing combined framework for accelerating high-resolution diffusion tensor imaging using inter-image correlation

• Published in: Magnetic resonance in medicine Vol. 73; no. 5; pp. 1775 - 1785
• Main Authors: Shi, Xinwei, Ma, Xiaodong, Wu, Wenchuan, Huang, Feng, Yuan, Chun, Guo, Hua
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.05.2015; Wiley Subscription Services, Inc
• Subjects: anisotropic sparsity; Brain - pathology; compressed sensing; Diffusion Magnetic Resonance Imaging - methods; diffusion tensor imaging; Humans; Image Enhancement - methods; Image Processing, Computer-Assisted - methods; parallel imaging; Statistics as Topic; variable density spiral; anisotropic sparsity; parallel imaging; diffusion tensor imaging; variable density spiral; compressed sensing
• DOI: 10.1002/mrm.25290
• ISSN: 0740-3194

Purpose 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. Theory and 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. Conclusion The proposed method can accelerate high‐resolution DTI acquisition effectively by using the sharable information among different diffusion encoding directions. Magn Reson Med 73:1775–1785, 2015. © 2014 Wiley Periodicals, Inc.