Journal
MAGNETIC RESONANCE IN MEDICINE
Volume 73, Issue 5, Pages 1775-1785Publisher
WILEY
DOI: 10.1002/mrm.25290
Keywords
diffusion tensor imaging; variable density spiral; anisotropic sparsity; compressed sensing; parallel imaging
Funding
- National Natural Science Foundation of China [61271132]
- National Key Technology R&D Program in the 12th Five-year Plan
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PurposeIncreasing 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 MethodsThe 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. ResultsThe 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. ConclusionThe 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. (c) 2014 Wiley Periodicals, Inc.
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