Open AccessA Tomographic Reconstruction Method using Coordinate-based Neural Network with Spatial Regularization
Author(s) -
Jakeoung Koo,
Elise Otterlei Brenne,
Anders Bjorholm Dahl,
Vedrana Andersen Dahl
Publication year - 2021
Publication title -
proceedings of the northern lights deep learning workshop
Language(s) - English
Resource type - Journals
ISSN - 2703-6928
DOI - 10.7557/18.5676
Subject(s) - regularization (linguistics) , tomographic reconstruction , computer science , iterative reconstruction , coordinate system , grid , attenuation , artificial intelligence , tomography , computer vision , artificial neural network , algorithm , mathematics , geometry , optics , physics
Tomographic reconstruction is concerned with computing the cross-sections of an object from a finite number of projections. Many conventional methods represent the cross-sections as images on a regular grid. In this paper, we study a recent coordinate-based neural network for tomographic reconstruction, where the network inputs a spatial coordinate and outputs the attenuation coefficient on the coordinate. This coordinate-based network allows the continuous representation of an object. Based on this network, we propose a spatial regularization term, to obtain a high-quality reconstruction. Experimental results on synthetic data show that the regularization term improves the reconstruction quality significantly, compared to the baseline. We also provide an ablation study for different architecture configurations and hyper-parameters.