Undersampled dynamic X-ray tomography with dimension reduction Kalman filter

TL;DR

This paper introduces a computationally efficient and robust dimension reduction Kalman filter for undersampled dynamic X-ray tomography, enabling accurate reconstructions with limited angular data.

Contribution

It proposes a prior-based low-dimensional basis approach that simplifies computations and enhances robustness in undersampled dynamic X-ray tomography.

Findings

Accurate reconstructions with very limited angular data

Method is computationally lightweight and explicit

Effective on both real and simulated data

Abstract

In this paper, we consider prior-based dimension reduction Kalman filter for undersampled dynamic X-ray tomography. With this method, the X-ray reconstructions are parameterized by a low-dimensional basis. Thus, the proposed method is a) computationally very light; and b) extremely robust as all the computations can be done explicitly. With real and simulated measurement data, we show that the method provides accurate reconstructions even with very limited number of angular directions.