Image Reconstruction in Cone Beam Computed Tomography Using Controlled Gradient Sparsity

TL;DR

This paper introduces an efficient, fully automatic 3D cone-beam CT reconstruction method using controlled gradient sparsity and dynamic regularization parameter adjustment, significantly reducing computational load.

Contribution

It presents a novel minimization algorithm with control theory-based parameter tuning for TV regularization in 3D CBCT reconstruction.

Findings

Reconstruction runs in clinically acceptable time

Automatic parameter adjustment improves usability

Method effectively handles ill-posed imaging problems

Abstract

Total variation (TV) regularization is a popular reconstruction method for ill-posed imaging problems, and particularly useful for applications with piecewise constant targets. However, using TV for medical cone-beam computed X-ray tomography (CBCT) has been limited so far, mainly due to heavy computational loads at clinically relevant 3D resolutions and the difficulty in choosing the regularization parameter. Here an efficient minimization algorithm is presented, combined with a dynamic parameter adjustment based on control theory. The result is a fully automatic 3D reconstruction method running in clinically acceptable time. The input on top of projection data and system geometry is desired degree of sparsity of the reconstruction. This can be determined from an atlas of CT scans, or alternatively used as an easily adjustable parameter with straightforward interpretation.