Neural Computed Tomography
Kunal Gupta, Brendan Colvert, Francisco Contijoch

TL;DR
NeuralCT is a novel neural implicit reconstruction framework that produces motion-artifact-free, time-resolved CT images without explicitly estimating motion, demonstrated across simple to complex motion scenarios.
Contribution
NeuralCT introduces a motion-agnostic neural implicit approach for dynamic CT reconstruction, avoiding explicit motion estimation and handling complex patient-specific motions.
Findings
Outperforms filtered backprojection in quality metrics
Successfully reconstructs images with translation, heartbeat-like, and complex deformations
No hyperparameter tuning needed for different motion types
Abstract
Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions despite fast acquisition of individual views. In cases such as cardiac imaging, motion may be unavoidable and evaluating motion may be of clinical interest. Reconstructing images with reduced motion artifacts has typically been achieved by developing systems with faster gantry rotation or using algorithms which measure and/or estimate the displacements. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating/measuring non-rigid, temporally varying, and patient-specific motions. We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts. Our approaches utilizes a neural implicit approach and does not require estimation or modeling of the…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Medical Image Segmentation Techniques · Seismic Imaging and Inversion Techniques
