A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography

TL;DR

This paper introduces a self-supervised, physics-informed variational autoencoder that reconstructs 3D objects from sparse X-ray projections, reducing radiation dose and enabling imaging of fragile samples.

Contribution

It presents a novel self-supervised deep learning method that reconstructs 3D structures from sparse measurements without high-dose training data.

Findings

Successfully reconstructs 3D objects from sparse projections

Reduces x-ray dose needed for imaging

Enables visualization of fragile samples

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 .