Quality evaluation of MR images****

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Magnetic resonance imaging (MRI) is susceptible to severe artifacts that hinder image quality leading to reduced diagnostic value, repeated imaging, and increased procedure costs. Artifacts appear during MRI in part due to patient motion, inherent limitations of the scanner, and incorrect scanning parameters. Movement (or motion) artifacts are an inherent problem to MRI technology that degrades image quality and may lead to misinterpretation especially in MRI acquisitions with low signal-to-noise ratios. However, it may be possible to recognize common artifacts during acquisition or immediately after a scan has completed.****The goal of this project is to develop an "MR image quality assessment tool" to automatically detect MRI artifacts. Such a tool will add substantial clinical value through incorporation into Philips quality assessment products and MRI systems. We propose to apply artificial intelligence (AI) using a deep learning algorithm to identify and detect MRI artifacts. Some possible options from the literature are to use transfer learning using a pre-trained convolutional neural network (CNN). Another option is to develop a Generative Adversarial Network or GAN that has been shown to be robust in the presence of limited data. ****The developed model will be tested on both hospital and publicly available datasets with artificially induced artifacts. If successful, the deep learning solution can be extended to other imaging modalities in the future.******