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Abstract #2445

Automatic Detection of Nyquist Ghosts in Whole-Body Diffusion Weighted MRI Using Deep Learning

Alistair Lamb1, Anna Barnes2, Stuart A Taylor2, and Hui Zhang3
1Department of Medical Phyics and Biomedical Engineering, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom, 3Centre for Medical Image Computing, University College London, London, United Kingdom

Despite its potential as an imaging biomarker in assessing tumor response to therapy, use of apparent diffusion coefficient (ADC) as a quantitative endpoint is not routine in clinical practice. One factor that limits the usefulness of ADC is the presence of artifacts in the constituent diffusion-weighted imaging (DWI) data. In this study, we propose a supervised deep-learning approach to detect the presence of Nyquist ghosts in axial DWI slices of the abdomen, achieving a test accuracy of 81.5%. The detection and removal of these artifacts could help improve the reproducibility of quantitative ADC measurements.

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