High resolution image reconstruction with constrained, total-variation minimization

TL;DR

This paper presents a novel iterative image reconstruction method for high-resolution low-intensity X-ray CT that stabilizes the model by constraining high-frequency components, improving efficiency and image quality.

Contribution

It introduces a new constrained total-variation minimization approach that sets high spatial frequencies to zero, enhancing stability and efficiency in high-resolution CT reconstruction.

Findings

Effective stabilization of the imaging model.

Improved image quality with reduced artifacts.

Demonstrated on real CT data.

Abstract

This work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling rate. Such systems pose a challenge for iterative image reconstruction, because a very fine image grid is needed to realize the resolution inherent in such scanners. These image arrays lead to under-determined imaging models whose inversion is unstable and can result in undesirable artifacts and noise patterns. There are many possibilities to stabilize the imaging model, and this work proposes a method which may have an advantage in terms of algorithm efficiency. The proposed method introduces additional constraints in the optimization problem; these constraints set to zero high spatial frequency components which are beyond the sensing capability of the detector. The method is demonstrated with an actual CT data set and compared with another method based on projection up-sampling.