Prospective motion correction for functional MRI using sparsity and Kalman filtering

Daniel S. Weller; Douglas C. Noll; Jeffrey A. Fessler

doi:10.1117/12.2023074

26 September 2013 Prospective motion correction for functional MRI using sparsity and Kalman filtering

Daniel S. Weller, Douglas C. Noll, Jeffrey A. Fessler

Proceedings Volume 8858, Wavelets and Sparsity XV; 885823 (2013) https://doi.org/10.1117/12.2023074
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

We propose a novel algorithm to adaptively correct head motion during functional magnetic resonance imaging scans. Our method combines a Kalman-filter-like motion tracker and a registration cost function based on a sparse residual image model. Using simulated data, we compare a time series correlation analysis of our prospectively corrected reconstruction against the same analysis using post-scan motion correction provided by standard software. Our experiments demonstrate our prospective correction method is capable of mitigating motion effects and improving the sensitivity and specificity of the correlation analysis, without relying on costly external tracking hardware or separate navigational data that would take extra time to acquire during each time frame.

Citation Download Citation

Daniel S. Weller, Douglas C. Noll, and Jeffrey A. Fessler "Prospective motion correction for functional MRI using sparsity and Kalman filtering", Proc. SPIE 8858, Wavelets and Sparsity XV, 885823 (26 September 2013); https://doi.org/10.1117/12.2023074

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