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Presenter: Jon Andre Ottesen, PhD student at CRAI, Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
Abstract: MRI data acquisition is an inherently slow process due to fundamental physical constraints that limit the signal (k-space) acquisition rate. This can lead to prolonged MRI sequences, during which the patient must remain still to achieve diagnostic-quality images. Traditionally, acceleration methods have entailed k-space/signal subsampling, followed by image reconstruction to reduce aliasing artifacts. In recent years, deep learning and convolutional neural networks have shown great promise as an alternative framework for MRI reconstruction to further accelerate scans beyond that of more traditional reconstruction methods.
Deep learning-based MRI reconstruction aims to learn to map a subsampled MR image to its fully sampled counterpart. One of the methods is a so-called cascading network that alternates between a U-Net-like architecture and data consistency. Here we aim to investigate how to improve MRI reconstruction frameworks through alterations to the cascading network formula, and show how it can be used on prospective data.
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