Neural Computed Tomography
- URL: http://arxiv.org/abs/2201.06574v1
- Date: Mon, 17 Jan 2022 18:50:58 GMT
- Title: Neural Computed Tomography
- Authors: Kunal Gupta, Brendan Colvert and Francisco Contijoch
- Abstract summary: Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions.
We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts.
- Score: 1.7188280334580197
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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
underlying motion. Instead, boundaries are represented using a signed distance
metric and neural implicit framework. We utilize `analysis-by-synthesis' to
identify a solution consistent with the acquired sinogram as well as spatial
and temporal consistency constraints. We illustrate the utility of NeuralCT in
three progressively more complex scenarios: translation of a small circle,
heartbeat-like change in an ellipse's diameter, and complex topological
deformation. Without hyperparameter tuning or change to the architecture,
NeuralCT provides high quality image reconstruction for all three motions, as
compared to filtered backprojection, using mean-square-error and Dice metrics.
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