Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

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...

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Implementation of Polish guidelines on futile therapy protocols in pediatric intensive care units: a multicenter retrospective review.

Scientific reports·2026

Same author

Role of Post-operative Thyroglobulin in Predicting Disease-recurrence in Differentiated Thyroid Cancer.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

Same author

Fluoroquinolone consumption and resistance after an Antibiotic Stewardship Team intervention-An interventional study in a single hospital in Southern Poland from 2018 to 2023.

American journal of infection control·2025

Same author

Understanding the risk of ionizing radiation in breast imaging: Concepts and quantities, clinical importance, and future directions.

European journal of radiology·2024

Same author

COVID-19 and antibiotic consumption in the intensive care units of the Polish tertiary hospital.

The Journal of hospital infection·2024

Same author

Assessment of Risk for Ventricular Tachycardia based on Extensive Electrophysiology Simulations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

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

Can color-coded parametric maps improve dynamic enhancement pattern analysis in MR mammography?

P A Baltzer¹, M Dietzel, T Vag

¹Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-University Jena, Erlanger Allee 101, 07740 Jena, Germany. pascal.baltzer@med.uni-jena.de

Rofo : Fortschritte Auf Dem Gebiete Der Rontgenstrahlen Und Der Nuklearmedizin

|October 29, 2009

Summary

Color-coded parametric maps (CCPMs) improve diagnostic accuracy in MR mammography (MRM) by automatically assessing lesion enhancement. This method offers higher sensitivity than traditional time/signal intensity curves (TSIC) for evaluating post-contrast enhancement characteristics (PEC).

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

Area of Science:

Magnetic Resonance Imaging
Medical Diagnostics
Radiology

Background:

Post-contrast enhancement characteristics (PEC) are crucial for MR mammography (MRM) differential diagnosis.
Manual region of interest (ROI) placement for time/signal intensity curves (TSIC) is the standard but labor-intensive method.
Automated analysis of dynamic enhancement data offers a potential improvement.

Purpose of the Study:

To compare the diagnostic accuracy (DA) of color-coded parametric maps (CCPMs) with traditional TSIC for assessing PEC in MRM.
To evaluate the feasibility of automated voxel-wise TSIC calculation and its integration into CCPMs.

Main Methods:

329 patients with 469 histologically verified lesions underwent MRM.
Manual TSIC calculation from ROIs and automated CCPM generation using dedicated software were performed.
Receiver operating characteristic (ROC) analysis was used to compare the DA of both methods, with two blinded observers reaching consensus.

Main Results:

CCPMs demonstrated a significantly higher area under the curve (AUC) (0.829) compared to TSIC (0.749) (p=0.026).
Sensitivity was higher with CCPMs (88.5%) versus TSIC (82.8%).
Specificity levels were comparable between CCPMs (63.7%) and TSIC (63.0%).

Conclusions:

CCPMs offer significantly higher diagnostic accuracy in MRM compared to TSIC.
The CCPM method enhances sensitivity for PEC assessment.
CCPMs represent a feasible approach for analyzing dynamic MRM data, condensing multiple series into a single map.