Abstract #3843

Comparison of Compressed Sensing Reconstruction for 3D Echo Planar Spectroscopic Imaging data using Total Variation and Statistically Optimized Perona-Malik Non-linear Diffusion

Andres Saucedo¹, Ajin Joy², Eric S. Daar³, Mario Guerrero³, Joseph Suresh Paul², Manoj K. Sarma¹, and M. Albert Thomas¹

¹Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Medical Image Computing and Signal Processing Laboratory, Indian Institute of Information Technology and Management - Kerala, Trivandrum, India, ³Los Angeles Biomedical Research Institute, Torrance, CA, United States

Conventional magnetic resonance spectroscopic imaging requires long acquisition times. Echo planar spectroscopic imaging (EPSI) significantly reduces the scan time but is limited by conventional phase encoding. Non-uniform sampling and compressed sensing (CS) reconstruction can further accelerated 3D EPSI. We applied a Perona-Malik (PM) non-linear diffusion algorithm for CS reconstruction of 3D EPSI data in both retrospectively and prospectively undersampled phantom and in-vivo data sets, and compared results with those using Total Variation (TV). Our pilot findings demonstrate that PM produces improved reconstruction results compared to TV. Furthermore, PM eliminates the need for parameter tuning, giving it a great advantage over TV.

This abstract and the presentation materials are available to members only; a login is required.

Join Here