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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: Jun 17, 2025

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Published on: May 17, 2018

DiffMAR: A Generalized Diffusion Model for Metal Artifact Reduction in CT Images.

Tianxiao Cai, Xiang Li, Chenglan Zhong

IEEE Journal of Biomedical and Health Informatics

|August 7, 2024

Summary

This summary is machine-generated.

A new generalized diffusion model, DiffMAR, effectively reduces metal artifacts in CT scans. It minimizes iterative errors and enhances anatomical structure generation for superior image quality and generalization.

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Last Updated: Jun 17, 2025

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

Metal artifacts in computed tomography (CT) images degrade image quality, particularly with metallic implants.
Existing metal artifact reduction (MAR) methods struggle with generalization and can produce lower-quality results due to cumulative errors in iterative approaches.

Purpose of the Study:

To develop a generalized diffusion model for effective metal artifact reduction (MAR) in CT images.
To address limitations in generalization and cumulative errors found in current MAR techniques.

Main Methods:

Introduced DiffMAR, a generalized diffusion model for MAR.
Simulated metal artifact formation using a linear degradation process.
Developed a Time-Latent Adjustment (TLA) module to minimize cumulative errors during iterative restoration.
Incorporated a Structure Information Extraction (SIE) module to guide anatomical structure generation using linear interpolation data.

Main Results:

DiffMAR demonstrated superior performance compared to state-of-the-art MAR methods.
The method achieved high-quality image generation with improved accuracy and robustness.
Validation on both synthesized and clinical data confirmed enhanced generalization capabilities.

Conclusions:

DiffMAR offers a significant advancement in metal artifact reduction for CT imaging.
The proposed model provides more accurate, robust, and generalized shadow-free image generation.
DiffMAR overcomes limitations of existing iterative and end-to-end MAR methods.