Highly accurate quantum optimization algorithm for CT image reconstructions based on sinogram patterns

TL;DR

This paper introduces a novel quantum algorithm for CT image reconstruction that leverages sinogram patterns and quantum optimization, promising improved accuracy and applicability to various CT modalities.

Contribution

The paper presents a new quantum algorithm for CT image reconstruction that utilizes sinogram patterns and can be implemented on gate-model quantum computers or quantum annealers.

Findings

Enhanced image reconstruction accuracy using quantum optimization.

Applicable to cone-beam CT and other imaging modalities.

Potential for integration with existing quantum hardware.

Abstract

Computed tomography (CT) has been developed as a non-destructive technique for observing minute internal images of samples. It has been difficult to obtain photo-realistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with limitations of back projection algorithms. Recently, an iterative optimization algorithm has been developed that uses the entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Suppose we have an experimental sinogram produced by a Radon transform. To find the CT image of this sinogram, we express the CT image as a combination of qubits. After the Radon transform of the undetermined CT image, we find the combination of the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstructions and will be of great help in the field of medical imaging.