Abstract #1108

Locally low-rank denoising in transform domains.

Steen Moeller¹, Casey P. Johnson^1,2, Erick O. Buko^1,2, Ferenc Toth², Greg Metzger¹, Silvia Mangia¹, Shalom Michaeli¹, Sara Ponticorvo¹, Antonietta Canna¹, Kamil Ugurbil¹, and Mehmet Akcakaya^1,3

¹Radiology, University of Minnesota, Minneapolis, MN, United States, ²Veterinary Clinical Sciences, University of Minnesota, Minneapolis, MN, United States, ³Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States

Synopsis

Keywords: Quantitative Imaging, Data ProcessingThe concept of transform processing domain with locally low rank denoising is proposed as T-NORDIC and demonstrated for MSK and brain applications. The improvements on quantitative maps may be leveraged for faster acquisitions by relaxing the number of averages needed to obtain sufficient SNR for high resolution acquisitions and for application of low rank denoising to common clinical acquisitions.

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