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Abstract #2279

Automated Principal Component Analysis (PCA) Filtering for Denoising DCE-MRI Data

Balvay Daniel1, Nadjia Kachenoura2, Isabelle Thomassin1, Rokhaya Thiam1, Laure Fournier1,3, Yves Rozenholc4, Charles Andr Cuenod1,3

1LRI EA 4062, Universit Paris Descartes, Paris, France; 2UMR_S 678, Universit Pierre et Marie Curie, Paris, France; 3Service de Radiologie, Hpital Europen Georges Pompidou, Paris, France; 4MAP5 - UMR CNRS 8145, Universit Paris Descartes, Paris, France

Dynamic Contrast Enhanced MRI is impaired, for small or heterogeneous lesion, by low signal to noise ratio (SNR), providing poor drawing of region of interest, of noisy microcirculatory parametric maps. To improve SNR without limiting the spatial or the time resolution by low pass filter, a Principal Component Analysis time filtering (PCA-TF) was performed. The number of PCA-TF factors was automatically identified by using the Fraction of Residual Information. The tests on DCE-MRI series showed an obvious denoising efficiency of the adapted PCA-TF with a minimal loss of information.