Publications by Year: 2013

Journal Article
A. G. Christodoulou, H. Zhang, B. Zhao, T. K. Hitchens, C. Ho, and Z. P. Liang, “High-Resolution Cardiovascular MRI by Integrating Parallel Imaging with Low-Rank and Sparse Modeling,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 11, pp. 3083-3092, 2013. Publisher's VersionAbstract

Magnetic resonance imaging (MRI) has long been recognized as a powerful tool for cardiovascular imaging because of its unique potential to measure blood flow, cardiac wall motion, and tissue properties jointly. However, many clinical applications of cardiac MRI have been limited by low imaging speed. In this paper, we present a novel method to accelerate cardiovascular MRI through the integration of parallel imaging, low-rank modeling, and sparse modeling. This method consists of a novel image model and specialized data acquisition. Of particular novelty is the proposed low-rank model component, which is specially adapted to the particular low-rank structure of cardiovascular signals. Simulations and in vivo experiments were performed to evaluate the method, as well as an analysis of the low-rank structure of a numerical cardiovascular phantom. Cardiac imaging experiments were carried out on both human and rat subjects without the use of ECG or respiratory gating and without breath holds. The proposed method reconstructed 2-D human cardiac images up to 22 fps and 1.0 mm × 1.0 mm spatial resolution and 3-D rat cardiac images at 67 fps and 0.65 mm × 0.65 mm × 0.31 mm spatial resolution. These capabilities will enhance the practical utility of cardiovascular MRI.

Conference Proceedings
B. Zhao, F. Lam, W. Lu, and Z. P. Liang, “Model-Based MR Parameter Mapping with Sparsity Constraint,” International Symposium on Biomedical Imaging: From Nano to Macro (ISBI). pp. 1-4, 2013. Publisher's VersionAbstract

MR parameter mapping (e.g., T1 mapping, T2 mapping, or T*2 mapping) is a valuable tool for tissue characterization. However, its practical utility has been limited due to long data acquisition time. This paper addresses this problem with a new model-based parameter mapping method, which utilizes an explicit signal model and imposes a sparsity constraint on the parameter values. The proposed method enables direct estimation of the parameters of interest from highly undersampled, noisy k-space data. An algorithm is presented to solve the underlying parameter estimation problem. Its performance is analyzed using estimation-theoretic bounds. Some representative results from T2 brain mapping are also presented to illustrate the performance of the proposed method for accelerating parameter mapping.