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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: May 14, 2026

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

Published on: December 15, 2014

A new user-friendly visual environment for breast MRI data analysis.

Danelakis Antonios1, Verganelakis A Dimitrios, Theoharis Theoharis

  • 1National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, University Campus, Ilissia, 15784 Athens, Greece. adanelakis@di.uoa.gr

Computer Methods and Programs in Biomedicine
|February 19, 2013
PubMed
Summary
This summary is machine-generated.

A new software, BreDAn, analyzes breast MRI data to detect high-risk areas. This automated system accurately processes images, potentially easing radiologists

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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
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Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

Published on: January 8, 2018

Area of Science:

  • Radiology and Medical Imaging
  • Biomedical Informatics
  • Oncology

Background:

  • Breast MRI is a crucial tool for detecting and characterizing breast lesions.
  • Accurate analysis of dynamic contrast-enhanced MRI (DCE-MRI) is essential for reliable diagnosis.
  • Current manual analysis can be time-consuming and prone to inter-observer variability.

Purpose of the Study:

  • To present a novel, user-friendly visual environment for automated breast MRI data analysis (BreDAn).
  • To validate BreDAn's capability in generating kinematic graphs and color maps of signal changes.
  • To assess BreDAn's accuracy and reliability in detecting high-risk breast areas.

Main Methods:

  • Development of a visual software environment (BreDAn) for breast MRI data analysis.
  • Input of planar MRI images acquired before and after IV contrast medium injection.
  • Generation of kinematic graphs, color maps of signal increase/decrease, and automated detection of high-risk areas.

Main Results:

  • BreDAn successfully generates kinematic graphs and color maps of signal changes.
  • The system accurately detects high-risk breast areas.
  • Validation and successful testing confirm the system's reliability.

Conclusions:

  • BreDAn offers an automated, accurate, and reliable solution for breast MRI data analysis.
  • The software has the potential to significantly reduce the workload for radiologists.
  • BreDAn facilitates the radiodiagnostic process, improving efficiency and consistency.