Publications by Year: 2011

Conference Proceedings
A. G. Christodoulou, B. Zhao, H. Zhang, C. Ho, and Z. P. Liang, “Four-Dimensional MR Cardiovascular Imaging: Method and Applications,” 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 3732-3735, 2011. 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. Three-dimensional cardiovascular MRI in real-time, or 4D cardiovascular MRI without cardiac and respiratory gating or triggering, remains an important technological goal of the MR cardiovascular research community. In this paper, we present a novel technique to achieve 4D cardiovascular MR imaging in unprecedented spatiotemporal resolution. This breakthrough is made possible through a creative use of sparse sampling theory and parallel imaging with phased array coils and a novel implementation of data acquisition and image reconstruction. We have successfully used the technique to perform 4D cardiovascular imaging on rats, achieving 0.65 mm × 0.65 mm × 0.31 mm spatial resolution with a frame rate of 67 fps. This capability enables simultaneous imaging of cardiac motion, respiratory motion, and first-pass myocardial perfusion. This in turn allows multiple cardiac assessments including measurement of ejection fraction, cardiac output, and myocardial blood flow in a single experiment. We believe that the proposed technique can open up many important applications of cardiovascular imaging and have significant impact on the field.

B. Zhao, J. P. Haldar, A. G. Christodoulou, and Z. P. Liang, “Further Development of Image Reconstruction from Highly Undersampled (k, t)-Space Data with Joint Partial Separability and Sparsity Constraints,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI). pp. 1593-1596, 2011. Publisher's VersionAbstract

Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.