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Abstract #1878

Quantitative imaging enhancement with deep learning based denoising: a validation study

Valentin H. Prevost1, Bei Zhang2, Clemence Bal3, and Wolter de Graaf2
1Canon Medical Systems Corporation, Tochigi, Japan, 2Canon Medical Systems Europe, Zoetermeer, Netherlands, 3Bordeaux University, Bordeaux, France

In MRI, signal noise ratio is the key point, determining the image quality and its medical relevance. Different ways exist to significantly increase it and then to access to high resolution imaging. Preliminary works introduced deep learning based denoising on several contexts and conclude to a significant signal noise ratio on qualitative images. However, it has not been tested yet on quantitative imaging sequences, questioning its feasibility and potential in this context. In this study, we investigated the DLR impact on calculated T1 and T2 relaxation times and diffusion imaging in healthy human brain areas.

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