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

Bo Zhao¹, Justin P Haldar, Zhi-Pei Liang

¹Department of Electrical and Computer Engineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 1406 West Green Street, IL 61801, USA. bozhaol@illinois.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

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This study introduces a new method for reconstructing cardiac MR images from undersampled data. It combines partial separability and sparsity constraints for improved image quality and reduced artifacts.

Area of Science:

Medical Imaging
Biomedical Engineering
Image Reconstruction

Background:

Partially separable function (PSF) models enhance cardiac MR imaging resolution from undersampled data.
Temporal undersampling in (k,t)-space can lead to ill-posed problems and artifacts in image reconstruction.
Existing methods relying solely on data consistency may produce suboptimal results.

Purpose of the Study:

To develop a novel regularization method for cardiac MR image reconstruction.
To address the ill-conditioned nature of model fitting in sparsely sampled (k,t)-space data.
To improve the quality of cardiac MR images reconstructed from undersampled datasets.

Main Methods:

Proposed a new method to regularize the inverse problem using sparsity constraints.

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Enabled simultaneous application of partial separability (low-rankness) and sparsity constraints.

Applied the method to reconstruct cardiac MR images from undersampled (k,t)-space data.

Main Results:

Demonstrated high-quality image reconstruction from undersampled (k,t)-space data.
Successfully combined partial separability and sparsity for improved reconstruction.
Cardiac imaging data validated the performance of the proposed method.

Conclusions:

The proposed method effectively regularizes the inverse problem in cardiac MR image reconstruction.
Simultaneous use of partial separability and sparsity constraints leads to high-quality images.
The technique offers a significant advancement for reconstructing cardiac MR images from undersampled data.