Titlebook: Machine Learning for Medical Image Reconstruction; First International Florian Knoll,Andreas Maier,Daniel Rueckert Conference proceedings

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Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networksstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dic

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Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Imagem undersampled .-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicti

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Bayesian Deep Learning for Accelerated MR Image Reconstruction been focussing on . the behaviour of deep networks, such as investigating when the networks may fail to reconstruct. In this work, we explore the applicability of Bayesian DL techniques to model the uncertainty associated with DL-based reconstructions. In particular, we apply MC-dropout and heteros

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Sparse-View CT Reconstruction Using Wasserstein GANsersarial networks (wGAN). Our wGAN optimizes the 2D CT image reconstruction by utilizing an adversarial loss to improve the perceived image quality as well as an . content loss to enforce structural similarity to the target image. We evaluate our wGANs using different weight factors between the two

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Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Kneeso severe artifacts in reconstructions. In knee imaging, a state-of-the-art approach to compensate for patient motion uses fiducial markers attached to the skin. However, marker placement is a tedious and time consuming procedure for both, the physician and the patient. In this manuscript we investig

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