Format

Send to

Choose Destination
Magn Reson Imaging. 2019 May 17. pii: S0730-725X(19)30119-5. doi: 10.1016/j.mri.2019.05.019. [Epub ahead of print]

Common information enhanced reconstruction for accelerated high-resolution multi-shot diffusion imaging.

Author information

1
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
2
Neusoft Medical System (Shanghai), Shanghai, China.
3
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. Electronic address: huaguo@tsinghua.edu.cn.

Abstract

PURPOSE:

Multi-shot technique can effectively achieve high-resolution diffusion weighted images, but the acquisition time of multi-shot technique is prolonged, especially for multiple direction diffusion encoding. Thus, increasing acquisition efficiency is highly desirable for high-resolution diffusion tensor imaging (DTI). In this study, based on the assumption that different diffusion directions share the common information, image ratio constrained reconstruction (IRCR) combined with iterative self-consistent parallel imaging reconstruction (SPIRiT) is proposed to improve data sampling efficiency and image reconstruction fidelity for high-resolution DTI.

THEORY AND METHODS:

The proposed reconstruction framework is named Common Information Enhanced Reconstruction (CIER). Inter-image correlation among different direction diffusion-weighted images is used through common information, which is an isotropic component and structure, for improving the performance of reconstruction. The framework consists of three steps. (i) Pre-processing: three intermediate multi-shot images, low-resolution composite image, high-resolution composite image and low-resolution diffusion weighted image, are generated based on the SPIRiT method. (ii) IRCR: the initial high-resolution diffusion weighted image is calculated from the images in step (i) based on that the ratio map between high-resolution images is approximated by the ratio map between the corresponding low-resolution images. (iii) Final SPIRiT reconstruction: the final image is generated with the image from IRCR as initialization by considering data consistency only in the SPIRiT calculation. A specific implementation based on multishot variable density spiral (VDS) DTI is used to demonstrate the method.

RESULTS:

The proposed CIER method was compared with the traditional reconstruction methods, conjugate gradient SENSE (CG-SENSE), L1-regularized SPIRiT (L1-SPIRiT), and anisotropic-sparsity SPIRiT (AS-SPIRiT) in brain DTI at acceleration factors of 3 to 7. CIER provided better diffusion image quality than other methods shown by both qualitative and quantitative results, especially at higher undersampling acceleration factors.

CONCLUSION:

CIER offers better diffusion image quality at higher undersampling acceleration factors for high-resolution DTI. Both qualitative and quantitative results prove that common information can be used to improve sampling efficiency and maintain the image quality of diffusion-weighted images.

KEYWORDS:

Common information; High-resolution DTI; Image ratio constrained reconstruction; SPIRiT; Variable density spiral (VDS)

PMID:
31108152
DOI:
10.1016/j.mri.2019.05.019

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center