A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed
Tomography
- URL: http://arxiv.org/abs/2211.00002v1
- Date: Sun, 30 Oct 2022 02:33:45 GMT
- Title: A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed
Tomography
- Authors: Rey Mendoza, Minh Nguyen, Judith Weng Zhu, Vincent Dumont, Talita
Perciano, Juliane Mueller, Vidya Ganapati
- Abstract summary: This work develops and validates a self-supervised probabilistic deep learning technique, the physics-informed variational autoencoder.
Deep neural networks have been used to transform sparse 2-D projection measurements to a 3-D reconstruction by training on a dataset of known similar objects.
High-quality reconstructions cannot be generated without deep learning, and the deep neural network cannot be learned without the reconstructions.
- Score: 1.0806206850043696
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computed tomography has propelled scientific advances in fields from biology
to materials science. This technology allows for the elucidation of
3-dimensional internal structure by the attenuation of x-rays through an object
at different rotations relative to the beam. By imaging 2-dimensional
projections, a 3-dimensional object can be reconstructed through a
computational algorithm. Imaging at a greater number of rotation angles allows
for improved reconstruction. However, taking more measurements increases the
x-ray dose and may cause sample damage. Deep neural networks have been used to
transform sparse 2-D projection measurements to a 3-D reconstruction by
training on a dataset of known similar objects. However, obtaining high-quality
object reconstructions for the training dataset requires high x-ray dose
measurements that can destroy or alter the specimen before imaging is complete.
This becomes a chicken-and-egg problem: high-quality reconstructions cannot be
generated without deep learning, and the deep neural network cannot be learned
without the reconstructions. This work develops and validates a self-supervised
probabilistic deep learning technique, the physics-informed variational
autoencoder, to solve this problem. A dataset consisting solely of sparse
projection measurements from each object is used to jointly reconstruct all
objects of the set. This approach has the potential to allow visualization of
fragile samples with x-ray computed tomography. We release our code for
reproducing our results at: https://github.com/vganapati/CT_PVAE .
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