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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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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

Published on: September 11, 2011

X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.

Mingjie Gao^1,2, Jeffrey A Fessler^1,2, Heang-Ping Chan¹

¹Department of Radiology, University of Michigan, Ann Arbor, MI 48109, United States of America.

Physics in Medicine and Biology

|April 19, 2024

Summary

This study introduces a new method to reduce motion-induced blur in digital breast tomosynthesis (DBT) images. By modeling the x-ray source motion, the technique enhances image clarity for better breast cancer detection.

Keywords:

diffusion models digital breast tomosynthesis focal spot blur image deblurring image resolution system modeling x-ray source motion

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Published on: December 15, 2014

Area of Science:

Medical Imaging
Radiology
Computational Imaging

Background:

Digital breast tomosynthesis (DBT) offers improved breast cancer detection over 2D mammography.
Motion-induced blur in DBT, especially from continuous x-ray source motion, degrades image quality and lesion visibility.
Subtle findings like microcalcifications can be obscured by this blur, impacting diagnostic accuracy.

Purpose of the Study:

To address the challenge of motion-induced blur in digital breast tomosynthesis (DBT) imaging.
To develop and validate a post-processing deblurring technique for DBT images.
To improve the spatial resolution and visibility of lesions in DBT scans.

Main Methods:

Derived an analytical in-plane source blur kernel for DBT images based on imaging geometry.
Proposed a post-processing image deblurring method utilizing a generative diffusion model as an image prior.
Validated the blur kernel modeling through simulations and applied the deblurring method to reconstructed DBT images.

Main Results:

Source blur in DBT slices can be accurately approximated by a shift-invariant kernel.
The generative diffusion model demonstrated the capability to produce realistic DBT images.
The proposed deblurring method effectively enhanced spatial resolution in DBT images, even with detector blur and noise.

Conclusions:

Modeling imaging system components, such as x-ray source motion blur, is crucial for enhancing DBT image quality.
The developed deblurring technique shows significant potential for improving diagnostic performance in DBT.
This approach offers a pathway to overcome limitations in current DBT systems and improve breast cancer detection rates.