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

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Deep Learning Based Image Reconstruction for Diffuse Optical Tomography and affordable way. Image reconstruction is an ill-posed challenging task because knowledge of the exact analytic inverse transform does not exist a priori, especially in the presence of sensor non-idealities and noise. Standard reconstruction approaches involve approximating the inverse function a

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Towards Arbitrary Noise Augmentation—Deep Learning for Sampling from Arbitrary Probability Distributng techniques, the noise distribution of a novel sensor may be difficult to determine a priori. Therefore, we propose learning arbitrary noise distributions. To do so, this paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly k

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Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networkse a method to reconstruct the shape of the left atria during the electrophysiology procedure from a series of simple catheter maneuvers. We use left atria shapes generated from a statistical based physical model and approximate traversal locations of catheter maneuvers inside the left atria. These p

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