Journal article
Direct diffusion tensor estimation using a model-based method with spatial and parametric constraints
Medical physics (Lancaster), Vol.44(2), pp.570-580
02/2017
DOI: 10.1002/mp.12054
PMID: 28138975
Abstract
To develop a new model-based method with spatial and parametric constraints (MB-SPC) aimed at accelerating diffusion tensor imaging (DTI) by directly estimating the diffusion tensor from highly undersampled k-space data.
The MB-SPC method effectively incorporates the prior information on the joint sparsity of different diffusion-weighted images using an L1-L2 norm and the smoothness of the diffusion tensor using a total variation seminorm. The undersampled k-space datasets were obtained from fully sampled DTI datasets of a simulated phantom and an ex-vivo experimental rat heart with acceleration factors ranging from 2 to 4. The diffusion tensor was directly reconstructed by solving a minimization problem with a nonlinear conjugate gradient descent algorithm. The reconstruction performance was quantitatively assessed using the normalized root mean square error (nRMSE) of the DTI indices.
The MB-SPC method achieves acceptable DTI measures at an acceleration factor up to 4. Experimental results demonstrate that the proposed method can estimate the diffusion tensor more accurately than most existing methods operating at higher net acceleration factors.
The proposed method can significantly reduce artifact, particularly at higher acceleration factors or lower SNRs. This method can easily be adapted to MR relaxometry parameter mapping and is thus useful in the characterization of biological tissue such as nerves, muscle, and heart tissue.
Details
- Title: Subtitle
- Direct diffusion tensor estimation using a model-based method with spatial and parametric constraints
- Creators
- Yanjie Zhu - Shenzhen Institutes of Advanced TechnologyXi Peng - Shenzhen Institutes of Advanced TechnologyYin Wu - Shenzhen Institutes of Advanced TechnologyEd X Wu - University of Hong KongLeslie Ying - State University of New YorkXin Liu - Shenzhen Institutes of Advanced TechnologyHairong Zheng - Shenzhen Institutes of Advanced TechnologyDong Liang - Shenzhen Institutes of Advanced Technology
- Resource Type
- Journal article
- Publication Details
- Medical physics (Lancaster), Vol.44(2), pp.570-580
- DOI
- 10.1002/mp.12054
- PMID
- 28138975
- ISSN
- 0094-2405
- eISSN
- 2473-4209
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 61471350, 11301508, 61401449, 61671441; DOI: 10.13039/100000001, name: National Science Foundation, award: CCF‐1514403; DOI: 10.13039/100000070, name: National Institute of Biomedical Imaging and Bioengineering, award: R21EB020861
- Language
- English
- Date published
- 02/2017
- Academic Unit
- Radiology
- Record Identifier
- 9984446406202771
Metrics
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