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Updated: Oct 31, 2025

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

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

Published on: December 15, 2014

Normalization of breast MRIs using cycle-consistent generative adversarial networks.

Gourav Modanwal¹, Adithya Vellal², Maciej A Mazurowski¹

¹Department of Radiology, Duke University, Durham, NC, USA.

Computer Methods and Programs in Biomedicine

|July 1, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for normalizing breast MRI images from different scanners, improving cancer detection. The new approach preserves anatomical details, enabling algorithms to work across various MRI machines.

Keywords:

CycleGAN Deep learning MRI intensity normalization Medical image translation Vendor normalization

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Last Updated: Oct 31, 2025

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is crucial for breast cancer detection.
Variations in MRI scanner output (e.g., GE Healthcare, Siemens) hinder algorithm generalization.
Image intensity and noise differences across scanners pose a significant challenge.

Purpose of the Study:

To develop a method for normalizing breast MRI images between different vendors.
To enable algorithms trained on one scanner's data to generalize to others.
To improve the reliability of AI-driven breast cancer diagnosis.

Main Methods:

Utilized a cycle-consistent generative adversarial network (CycleGAN) for bidirectional MRI normalization.
Incorporated mutual information loss to preserve anatomical breast shape.
Modified the discriminator architecture with a smaller field-of-view to maintain tissue structure details.

Main Results:

The proposed method successfully normalized intensity and noise distributions between GE Healthcare and Siemens scanners.
Evaluations confirmed the preservation of breast shape and intricate tissue structures.
The innovations addressed limitations of traditional CycleGAN in medical image processing.

Conclusions:

The developed model effectively performs bidirectional normalization for multi-vendor breast MRIs.
This normalization technique has the potential to enhance breast cancer diagnosis and detection accuracy.
Publicly available data was used, promoting reproducibility and further research.