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

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

Imaging Studies IV: Magnetic Resonance Imaging

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

You might also read

Related Articles

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

Sort by

Same author

Prognostic associations of steroid use in patients with glioblastoma: A population-based study.

Neuro-oncology practice·2026

Same author

Spinal high-grade astrocytoma with piloid features presenting with progressive encephalopathy, hypoglycorrhachia, hydrocephalus, and leptomeningeal dissemination.

Journal of neuropathology and experimental neurology·2026

Same author

Distinct molecular subgroups in pediatric and young-onset meningiomas require age-adapted risk stratification.

Nature communications·2026

Same author

Neurosurgeons' perspective on surgical treatment of glioblastoma: Practices, beliefs and willingness to randomize in surgical trials.

Neuro-oncology advances·2026

Same author

Temozolomide Versus Radiotherapy as First-Line Therapy for Low-Grade Glioma: Mature Results of a Randomized Phase III Trial (EORTC 22033-26033/NCIC-CTG/TROG/MRC-CTU).

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Same author

IDH1-mutant vaccine in newly diagnosed astrocytoma: final analysis of the multicenter, single-arm, open-label, first-in-human phase 1 NOA16 trial.

Nature cancer·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

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

Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.

Mustafa Ahmed Mahmutoglu¹, Chandrakanth Jayachandran Preetha¹, Hagen Meredig¹

¹From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.).

Radiology. Artificial Intelligence

|January 2, 2024

Summary

A new convolutional neural network (CNN) accurately identifies nine MRI sequence types in brain scans. This deep learning tool enhances efficiency for clinical and research neuroradiology workflows.

Keywords:

Brain/Brain Stem CNS Computer Applications-General (Informatics)Convolutional Neural Network (CNN)Deep Learning Algorithms MR-Imaging Machine Learning Algorithms Neural Networks

More Related Videos

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Jun 17, 2026

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

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging Informatics
Artificial Intelligence in Radiology
Machine Learning for Neuroscience

Background:

Accurate labeling of Magnetic Resonance Imaging (MRI) sequences is crucial for clinical diagnosis and research.
Manual labeling is time-consuming and prone to errors, especially with heterogeneous, unstructured data.
Automated methods are needed to improve the throughput and reliability of MRI data analysis.

Purpose of the Study:

To develop a fully automated, device- and sequence-independent convolutional neural network (CNN).
To enable high-throughput and reliable labeling of diverse MRI data.
To differentiate between nine common MRI sequence types in brain imaging.

Main Methods:

A retrospective, multicentric dataset of 63,327 MRI sequences from 2,179 glioblastoma patients was utilized.
A ResNet-18 based CNN was trained and validated on 2D-midsection images, with 80% for training/validation and 20% for testing.
Model performance was evaluated across nine MRI sequence types, including T1-weighted, T2-weighted, FLAIR, DWI, and SWI.

Main Results:

The CNN achieved an overall accuracy of 97.9% across all sequence types on the test set.
Individual sequence accuracies ranged from 84.2% (SWI) to 99.8% (T2-weighted).
The ResNet-18 model demonstrated superior accuracy compared to ResNet-50 and was unaffected by tumor presence.

Conclusions:

The developed CNN reliably differentiates nine types of MRI sequences in large-scale, multicenter neuroimaging data.
The model is device- and sequence-independent, enhancing its generalizability.
This automated approach can significantly improve the speed, accuracy, and efficiency of neuroradiologic workflows.