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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

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X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging

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Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study.

Su Min Ha, Hak Hee Kim, Eunhee Kang

Taehan Yongsang Uihakhoe Chi

|October 14, 2022

Summary

A new denoising convolutional neural network improves breast cancer diagnosis from low-dose mammography. This AI technique allows for substantial radiation dose reduction while maintaining diagnostic accuracy.

Keywords:

Artificial Intelligence Breast Neoplasm Deep Learning Mammography Radiation

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Low-dose mammography is crucial for breast cancer screening.
Image quality and lesion detectability can be compromised at reduced radiation doses.
Advanced image processing techniques are needed to enhance low-dose mammograms.

Purpose of the Study:

To develop and evaluate a denoising convolutional neural network (CNN) for breast cancer diagnosis.
To assess the efficacy of the CNN in improving image quality and lesion detection in low-dose mammography.
To investigate the potential for radiation dose reduction in mammography through AI-based image processing.

Main Methods:

A prospective study involving 6 breast radiologists evaluating low-dose mammograms.
Radiologists assessed lesion detection and image quality before and after applying a denoising CNN.
Comparison of low-dose, 40% reconstructed full-dose, and 100% full-dose images for diagnostic performance.

Main Results:

Denoising processing improved lesion perception on low-dose images compared to mastectomy specimens.
Full-dose images received higher ratings for resolution and diagnostic quality of calcifications, masses, and distortions than 40% reconstructed images.
40% reconstructed images demonstrated comparable overall quality, lesion visibility, and contrast to 100% full-dose images, indicating no significant differences.

Conclusions:

Denoising and image reconstruction techniques using CNNs can significantly reduce radiation dose in mammography.
AI-powered image processing holds promise for effective breast cancer diagnosis with lower radiation exposure.
This approach can maintain diagnostic accuracy while minimizing patient risk.