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

Imaging Studies I: CT and MRI

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...

You might also read

Related Articles

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

Sort by

Same author

Myelin damage in radiologically isolated syndrome is associated with decreased processing speed.

Multiple sclerosis journal - experimental, translational and clinical·2026

Same author

Medical Assistance in Dying in ALS: Frequency, Survival, and Associations With Patient Characteristics in a Canadian Clinic.

Neurology·2026

Same author

DeepRelaxo: Fast Mono-Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self-Supervised Deep Learning.

Magnetic resonance in medicine·2026

Same author

Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification.

Magnetic resonance in medicine·2026

Same author

Exploring the Value of Brain T2* Weighted and FLAIR Imaging for Diagnosing Amyotrophic Lateral Sclerosis.

The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques·2026

Same author

Repeatability of Susceptibility Source Separation Methods in Human Brain: A Single-Site Study at 3 T.

NMR in biomedicine·2026

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets.

Rafsanjany Kushol¹, Alan H Wilman², Sanjay Kalra^1,3

¹Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada.

Diagnostics (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

A new Domain Shift analyzer for MRI (DSMRI) framework quantifies variability in multi-center MRI data. This tool aids in developing better domain adaptation and harmonization techniques for improved medical research.

Keywords:

MRI UMAP domain shift quality control t-SNE texture analysis

More Related Videos

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Jul 15, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Medical imaging analysis
Neuroimaging
Machine learning in healthcare

Background:

Multi-center MRI datasets are crucial for medical research and clinical applications.
Variability between centers (domain shift) challenges data analysis quality and reliability.
Lack of adequate domain shift analysis tools impedes domain adaptation and harmonization.

Purpose of the Study:

To introduce a novel framework, Domain Shift analyzer for MRI (DSMRI), for analyzing domain shift in multi-center MRI data.
To provide tools for assessing and quantifying domain shift to improve MRI data analysis.

Main Methods:

DSMRI assesses domain shift using MRI-quality metrics from spatial, frequency, and wavelet domains.
Incorporates texture features for enhanced analysis robustness.
Utilizes t-SNE and UMAP for data visualization and quantitative analysis of domain shift distance and feature significance.

Main Results:

The DSMRI framework effectively quantifies domain shift across various MRI data characteristics.
Visualization techniques confirm clear separation of similar and dissimilar data clusters.
Quantitative analysis provides measurable insights into domain shift distance and feature importance.

Conclusions:

DSMRI offers a robust solution for domain shift analysis in multi-center MRI datasets.
The framework supports the development of more reliable domain adaptation and harmonization techniques.
Validated on seven large-scale neuroimaging datasets, demonstrating its effectiveness.