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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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Related Experiment Video

Updated: Jan 8, 2026

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

MSDiff: multi-scale diffusion model for ultra-sparse view CT reconstruction.

Junyan Zhang¹, Mengxiao Geng¹, Pinhuang Tan¹

¹School of Information Engineering, Nanchang University, Nanchang 330031, People's Republic of China.

Physics in Medicine and Biology

|December 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel multi-scale diffusion model for ultra-sparse view computed tomography (CT) reconstruction. The method enhances image quality in low-angle CT scans by integrating global and local image information.

Keywords:

computed tomography multi-diffusion model sinogram domain ultra-sparse view reconstruction

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Last Updated: Jan 8, 2026

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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Area of Science:

Medical Imaging
Computational Imaging
Artificial Intelligence in Healthcare

Background:

Computed tomography (CT) imaging reduces radiation exposure via sparse sampling, but this technique challenges image reconstruction quality.
Limited projection angles in CT scans significantly degrade the fidelity of reconstructed images.

Purpose of the Study:

To develop an advanced ultra-sparse view CT reconstruction method using multi-scale diffusion models.
To enhance the quality of CT image reconstruction when projection angles are significantly reduced.

Main Methods:

Proposed an ultra-sparse view CT reconstruction method incorporating multi-scale diffusion models.
Combined comprehensive and selective sparse sampling techniques to capture both global and local image features.
Utilized an equidistant mask based on CT imaging principles to optimize model attention.

Main Results:

The multi-scale diffusion model significantly improved image reconstruction quality in ultra-sparse view CT.
The proposed method demonstrated robust generalization capabilities across diverse datasets.
The model effectively leveraged projection data correlations for enhanced image recovery.

Conclusions:

Multi-scale diffusion models offer a promising approach for high-quality CT reconstruction with minimal projection data.
This technique can improve diagnostic accuracy in low-dose or limited-angle CT applications.
The method shows potential for broader applications in medical imaging requiring efficient data acquisition.