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Abstract #0192

Model-free denoising of multi b-value diffusion-weighted MR images using principal component analysis: simulations and in vivo results

Oliver Jacob Gurney-Champion1, David J Collins2, Andreas Wetscherek1, Mihaela Rata2, Remy Klaassen3, Hanneke W M van Laarhoven3, Kevin J Harrington1, Uwe Oelfke1, and Matthew R Orton2

1The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, London, United Kingdom, 2Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, London, United Kingdom, 3Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands

We present a principal component analysis (PCA) toolkit for mode-free denoising of multi b-value diffusion-weighted images for clinical use. In simulations, PCA-denoising suppressed the random noise equally well (up to 55%) as synthetic MRI. Contrary to synthetic MRI (systematic error up to 29% of total signal intensity), PCA-denoising did not introduce any systematic errors (<2%). In volunteer and patient image data, PCA-denoising resulted in sharper and less noisy images than synthetic MRI, which resulted in sharper and clearer tumour boundaries. In conclusion, our PCA-denoising toolkit is promising for denoising b-value images for clinical use.

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