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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