Work

Spectral Quantification for High-Resolution MR Spectroscopic Imaging With Spatiospectral Constraints

Year: 2016

Type: article

Abstract: OBJECTIVE: To obtain reliable spectral estimation from magnetic resonance spectroscopic imaging (MRSI) data. METHODS: The proposed method takes advantage of prior knowledge: 1) along the spectral dimension in the form of spectral bases, and 2) along the spatial dimensions in the form of spatial regularizations (e.g., smoothness or transform sparsity) and jointly estimates parameters from all the voxels. RESULTS: Simulation and in vivo studies have been performed to demonstrate the performance of the proposed method. A Cramér-Rao-bound-based analysis is also provided. CONCLUSION: Incorporation of both spatial and spectral constraints can significantly improve spectral quantification of MRSI data. SIGNIFICANCE: The proposed method is expected to be useful for various quantitative MRSI studies.

Source: IEEE Transactions on Biomedical Engineering

DOI: 10.1109/tbme.2016.2594583

Authors: Qiang Ning, Chao Ma, Fan Lam, Zhi-Pei Liang

Institutions: University of Illinois Urbana-Champaign, Harvard University, Gordon Center for Medical Imaging, Massachusetts General Hospital

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering

Awards: Ultra-high Resolution 3D MR Spectroscopic Imaging, G2456547255, Faster Dynamic MRI with Sparse Sampling, G9000247401