Student Posters Repository

Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause artefacts that severely degrade image quality. In the clinic, if an image with motion artefacts is acquired, it will often be reacquired. This provides a source from which a large number of motion-degraded images, along with their respective re-scans, could be collected. These pairs of images could be used to train a neural network to identify the mapping relationship between an image with motion artefacts and a high quality, artefact free image. Inspired by previous work demonstrating MR image reconstruction with machine learning,[1,2] our objective is to train a neural network to perform motion corrected image reconstruction on image data with simulated motion artefacts. We simulate motion in previously acquired brain images and use the image pairs (corrupted + original) to train a deep neural network (DNN). The DNN was developed and trained using the TensorFlow library [4]. We show that the  images predicted by the DNN, from motion-corrupted k-space, have improved image quality compared to the motion-corrupted images. 

References: [1] Zhu, Bo. et al., Image reconstruction by domain transform manifold learning, 2017 [2] Hammernik K, et al., Learning a Variational Network for Reconstruction of Accelerated MRI Data, 2017 [3] Forstmann BU, et al., Multi-modal ultra-high resolution structural 7-Tesla MRI data repository. 2014 [4] Abadi M. et al., TensorFlow, 2015.