Machine Learning for Detection of 3D Features using sparse X-ray data

TL;DR

This paper explores the use of convolutional neural networks to reconstruct 3D features of inertial confinement fusion implosions from sparse X-ray data, addressing the challenge of limited view angles in experimental settings.

Contribution

The study demonstrates the application of multiple CNN architectures with deep supervision for high-resolution 3D reconstruction from limited X-ray projections in ICF experiments.

Findings

Neural networks can effectively reconstruct 3D features from sparse X-ray data.

Deep supervision improves the resolution of 3D reconstructions.

Different CNN models provide diverse representations useful for feature tracking.

Abstract

In many inertial confinement fusion experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects which may be significant. Sources of these effects include defects in the shells and shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, X-rays are used to capture the internal structure of objects. Methods such as Computational Tomography use X-ray radiographs from hundreds of projections in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce and in many cases only consist of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. We utilize deep supervision to train a neural network to produce high resolution reconstructions. We use these representations to track 3D features of the capsules such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and X-ray radiography in general.