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Abstract #2209

Reduction of ADC bias with deep learning-based acceleration in diffusion-weighted MRI: A phantom validation study

Teresa Nolte1, Masami Yoneyama2, Chiara Morsch1, Alexandra Barabasch1, Maximilian Schulze-Hagen1, Johannes M. Peeters3, Christiane Kuhl4, and Shuo Zhang5
1Diagnostic and Interventional Radiology, Uniklinik RWTH Aachen University, Aachen, Germany, 2Philips Japan, Tokyo, Japan, 3Philips Healthcare, Best, Netherlands, 4Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany, 5Philips GmbH Market DACH, Hamburg, Germany

Synopsis

Keywords: Image Reconstruction, Diffusion/other diffusion imaging techniques, NoiseFurther acceleration of diffusion MRI in clinical examinations is desired but challenging mainly due to low signal and associated potential bias in the quantitative apparent diffusion coefficient (ADC) values. Artificial intelligence-based denoising and image reconstruction may provide a solution to address this challenge. We investigate and compare different image reconstruction methods, including conventional parallel imaging, compressed sensing, and a deep learning-based technique, in ADC accuracy and precision using a diffusion phantom with illustration of the principle in numeric simulation.

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Keywords