Abstract #0313

GPU Based Fast Inverse Gauss-Newton Motion Correction Method for High Throughput ofMRI

Zhongnan Fang¹, Jin Hyung Lee¹,²

¹Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, United States; ²Department of Neurology and Neurological Sciences, Department of Bioengineering, Stanford University, Stanford, CA, United States

A novel GPU based parallel motion correction method for high throughput ofMRI study is presented. With ultra-high processing speed, this method enables real-time motion correction while allowing future integration of computationally intense processing steps including iterative reconstruction and automatic segmentation for high-throughput interactive brain circuit analysis. The algorithm utilizes an iterative inverse update strategy, which dramatically reduces computational cost. GPU specific features such as texture caching and hard-wired interpolation are also utilized for the highest efficiency. Compared to currently available methods, the proposed algorithm shows the lowest RMS error rate and highest speed.