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Abstract #3407

ICA based filtering of IVIM-DWI data to improve fidelity of parametric diffusion maps in breast cancer patients

Dattesh D Shanbhag1, Tetsuya Wakayama2, Reem Bedair3, Andrew J Patterson4, Fiona J Gilbert3, Rakesh Mullick1, and Martin J Graves3,4

1GE Global Research, Bangalore, India, 2GE Healthcare, Hino, Japan, 3Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom, 4Department of Radiology, Addenbrookes Hospital, Cambridge, United Kingdom

IVIM-DWI data can be potentially corrupted by eddy currents, susceptibility artifacts, motion and image reconstruction methods. We hypothesized that artifacts in IVIM imaging could be separated from true diffusion decay using an independent component analysis (ICA) methodology. In this work , we demonstrate that with ICA based filtering of raw IVIM data, transients in IVIM data are removed, with consequent improvement in IVIM model fit quality and reduction in saturated values in pseudo-diffusion D*maps. This should therefore improve confidence in interpreting IVIM parametric maps in clinical practice.

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