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...

You might also read

Related Articles

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

Sort by

Same author

Accuracy of Cytology Diagnosis for Well Differentiated Neuroendocrine Tumors: Assessment by the College of American Pathologists Non-Gynecologic Slide Program.

Archives of pathology & laboratory medicine·2026

Same author

Penile squamous cell carcinomas with the usual-type morphology can be HPV-associated - Routine p16/HPV testing should be performed in all penile squamous cell carcinomas regardless of morphology.

Virchows Archiv : an international journal of pathology·2026

Same author

NFE2L2-mutated urothelial carcinomas frequently show concomitant myxoid and squamous features and have high PD-L1 expression.

Histopathology·2026

Same author

Association of genomic driver mutation, histologic subtype, and mTORC1 biomarker expression in mTOR pathway-altered renal tumors.

Human pathology·2026

Same author

Comprehensive analysis of six MED15::TFE3 renal cell carcinomas including two novel solid MED15::TFE3 renal cell carcinomas, highlighting their morphologic, immunohistochemical and molecular differences from their cystic counterparts.

Virchows Archiv : an international journal of pathology·2026

Same author

Improved prostate diffusion imaging using deep learning denoising and phase correction with ultra-high-density coil array.

Radiology advances·2026

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue.

Francisco E Enríquez-Mier-Y-Terán^1,2, Aritrick Chatterjee^3,4, Tatjana Antic⁵

¹School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, 2008, Australia.

Scientific Reports

|October 1, 2023

Summary

This study introduces a flexible method to combine multiple models for predicting tissue microstructure using diffusion-relaxation MRI. This approach improves accuracy in complex tissues by avoiding a single, potentially suboptimal, model choice.

More Related Videos

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Related Experiment Videos

Last Updated: Jul 15, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Area of Science:

Biomedical Imaging
Quantitative MRI
Tissue Microstructure Analysis

Background:

Analyzing heterogeneous tissues with diffusion-relaxation MRI often requires selecting a single model, which can lead to inaccuracies.
The optimal model for tissue microstructure prediction can vary significantly within different tissue environments.

Purpose of the Study:

To propose a general, multi-model approach for combining predictions of tissue microstructure from diffusion-relaxation MRI data.
To overcome limitations of single-model approaches in heterogeneous tissues.
To enable flexible biological interpretation by separating signal characterization from biological assignment.

Main Methods:

A three-stage sequence for signal interpretation: 1. Applying multiple semi-phenomenological models to predict physical properties of water pools. 2. Using tissue microstructure models to predict relative volumes of tissue structures within each water pool. 3. Aggregating microstructure predictions weighted by model likelihood and water pool fractional volumes.
Exemplar application using in vivo diffusion-relaxation MRI data from human prostate.
Demonstration of combining multiple models to predict tissue microstructure.

Main Results:

The multi-model approach reduces prediction variance in regions where complex models are overparameterized and bias where models are underparameterized.
Separation of signal characterization and biological assignment allows for diverse interpretations of physical properties.
Successful application demonstrated on human prostate diffusion-relaxation MRI data.

Conclusions:

The proposed general method effectively combines multiple models for robust tissue microstructure prediction in heterogeneous tissues.
This flexible framework enhances the analysis of diffusion-relaxation MRI data, offering broader applicability.
The method has potential applications beyond prostate imaging, applicable to various domains where single models are insufficient.