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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Quantum optimization algorithms for CT image segmentation from X-ray data.

Kyungtaek Jun^1,2, Hyunju Lee³

¹Quantum Research Center, QTomo Inc., Cheongju, Chungcheongbuk-do, 28535, South Korea. ktfriends@gmail.com.

Scientific Reports

|July 2, 2025

Summary

This study introduces a quantum optimization algorithm for simultaneous computed tomography (CT) image reconstruction and segmentation. The novel approach shows comparable results to classical methods, advancing medical imaging potential.

Keywords:

CT image segmentation QUBO model Quantum optimization CT algorithm Quantum optimization algorithms Quantum tomographic image segmentation Quantum tomographic reconstruction

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Area of Science:

Medical Imaging
Quantum Computing
Computational Science

Background:

Computed tomography (CT) is vital for internal body imaging.
Traditional CT requires separate reconstruction and segmentation, risking errors.
Existing methods are susceptible to errors from both reconstruction and segmentation algorithms.

Purpose of the Study:

To introduce a novel quantum optimization algorithm for simultaneous CT image reconstruction and segmentation.
To overcome limitations of classical CT methods by integrating reconstruction and segmentation.
To validate the efficacy of the quantum approach using experimental data.

Main Methods:

Utilized quadratic unconstrained binary optimization (QUBO), a quantum algorithm, for CT image segmentation.
Performed simultaneous reconstruction and segmentation by minimizing sinogram differences using quantum states and Radon transform.
Employed X-ray mass attenuation coefficients to optimize qubit requirements and D-Wave's hybrid solver.

Main Results:

The quantum CT algorithm achieved segmentation results comparable to classical methods after post-processing.
Identified minor discrepancies in pixel segmentation at boundaries compared to classical approaches.
Demonstrated the feasibility of integrating quantum optimization for medical image analysis.

Conclusions:

The developed quantum optimization CT algorithm offers a simultaneous reconstruction and segmentation approach.
This method holds promise for advancing medical imaging by potentially reducing errors and improving efficiency.
Further development could lead to significant breakthroughs in diagnostic imaging technologies.