Model-Based Reconstruction for Accelerated Magnetic Resonance Imaging
Speaker: Miao He
Host Faculty: Professor Dariusz Czarkowski
Compared to other medical imaging methods, magnetic resonance imaging (MRI) is differentiated by excellent soft tissue contrast and use of non-ionizing electromagnetic radiation. By accelerating MR image acquisition, the throughput, and hence, the affordability and comfort to patients can be increased, and the temporal resolution of dynamic and functional MRI can be improved. In this talk, I describe how my research into model-based reconstruction facilitates accelerating MRI.
To begin, I explore using sparsity to improve the GRAPPA method for accelerated MRI reconstruction with parallel receiver coils. I describe a post-processing method that effectively denoises the reconstruction, while preserving the acquired data. Another approach regularizes calibration of the GRAPPA method to promote sparsity in the output, improving reconstruction quality when insufficient calibration data is available. I tie these methods together using an estimation framework and effectively combine the calibration and reconstruction into a single optimization problem.
Next, I outline my current research exploiting sparsity to prospectively correct for head motion in functional MRI. Such motion affects the accuracy of time series correlations common in functional MRI analysis. I register frames as they are acquired and adjust the scan prescription prospectively for the detected motion. Through these examples, I demonstrate the great benefits model-based reconstruction holds for magnetic resonance imaging and other imaging modalities.
About the Speaker
Daniel Weller received his B.S. in Electrical and Computer Engineering with honors from Carnegie Mellon University in 2006, and his S.M. and Ph.D. in Electrical Engineering from MIT in 2008 and 2012. Daniel is currently a postdoctoral research fellow at the University of Michigan, supported by an NIH NRSA postdoctoral fellowship. He previously received a National Defense Science and Engineering Graduate (NDSEG) fellowship and an NSF graduate research fellowship. Daniel was a finalist in the student paper competition at the 2011 IEEE International Symposium on Biomedical Imaging. His research interests include magnetic resonance imaging, signal processing and estimation theory, nonideal sampling and reconstruction.