Work

PSF model-based reconstruction with sparsity constraint: Algorithm and application to real-time cardiac MRI

Year: 2010

Type: conference-paper

Abstract: The partially separable function (PSF) model has been successfully used to reconstruct cardiac MR images with high spatiotemporal resolution from sparsely sampled (k,t)-space data. However, the underlying model fitting problem is often ill-conditioned due to temporal undersampling, and image artifacts can result if reconstruction is based solely on the data consistency constraints. This paper proposes a new method to regularize the inverse problem using sparsity constraints. The method enables both partial separability (or low-rankness) and sparsity constraints to be used simultaneously for high-quality image reconstruction from undersampled (k,t)-space data. The proposed method is described and reconstruction results with cardiac imaging data are presented to illustrate its performance.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/iembs.2010.5627934

Authors: Bo Zhao, Justin P Haldar, Zhi-Pei Liang

Institution: University of Illinois Urbana-Champaign

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, National Center for Research Resources