Extreme Few-view CT Reconstruction using Deep InferenceDownload PDF

Published: 21 Oct 2019, Last Modified: 05 May 2023NeurIPS 2019 Deep Inverse Workshop PosterReaders: Everyone
TL;DR: We present a CNN inference-based reconstruction algorithm to address extremely few-view CT.
Keywords: Computed Tomography, Few-View CT, CNN
Abstract: Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem. It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT. Existing analytic or iterative algorithms generally produce poorly reconstructed images, severely deteriorated by artifacts and noise, especially when the number of x-ray projections is considerably low. This paper presents a deep network-driven approach to address extreme few-view CT by incorporating convolutional neural network-based inference into state-of-the-art iterative reconstruction. The proposed method interprets few-view sinogram data using attention-based deep networks to infer the reconstructed image. The predicted image is then used as prior knowledge in the iterative algorithm for final reconstruction. We demonstrate effectiveness of the proposed approach by performing reconstruction experiments on a chest CT dataset.
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