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...

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

Triply N-Confused Hexaphyrins(1.1.1.1.1.0) Synthesized From N-Confused Hexaphyrin(2.1.1.1.1.1): Aromaticity Modulation, Metal Coordination, and Photothermal Conversion.

Angewandte Chemie (International ed. in English)·2026

Same author

Cockayne syndrome mutation in XPG activate the integrated stress response.

Human genetics·2026

Same author

CAMERA-Net With Focal Pixel Loss for Accelerated MRI.

NMR in biomedicine·2025

Same author

Triphyrin(2.1.1) with Tunable Skeletons and Properties Based on the Confusion Approach.

Organic letters·2024

Same author

General loss of proteostasis links Huntington disease to Cockayne syndrome.

Neurobiology of disease·2024

Same author

Sanxiapeptin is an ideal preservative with a dual effect of controlling green mold and inducing systemic defense in postharvest citrus.

Food chemistry·2024

Search research articles

Related Experiment Video

Updated: Jun 24, 2025

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Published on: February 24, 2021

Deep learning-based automated scan plane positioning for brain magnetic resonance imaging.

Gaojie Zhu^1,2, Xiongjie Shen², Zhiguo Sun²

¹Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.

Quantitative Imaging in Medicine and Surgery

|June 7, 2024

Summary

This study introduces a deep learning framework for accurate, automated head MRI scan positioning, overcoming manual and traditional method limitations. The AI model achieves high precision and efficiency in clinical settings.

Keywords:

Deep learning automated scan plane positioning domain knowledge head scan magnetic resonance imaging (MRI)

More Related Videos

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Published on: July 19, 2019

Magnetic Resonance-Guided Stereotaxy for Infusions to the Pig Brain

Magnetic Resonance-Guided Stereotaxy for Infusions to the Pig Brain

Published on: March 31, 2023

Related Experiment Videos

Last Updated: Jun 24, 2025

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Published on: February 24, 2021

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Published on: July 19, 2019

Magnetic Resonance-Guided Stereotaxy for Infusions to the Pig Brain

Magnetic Resonance-Guided Stereotaxy for Infusions to the Pig Brain

Published on: March 31, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Manual scan planning in clinical MRI is inaccurate, inconsistent, and time-consuming.
Existing automated methods lack accuracy, stability, and computational efficiency for practical use.

Purpose of the Study:

Develop and evaluate a reliable, accurate deep learning framework for automatic head MRI scan plane positioning.
Incorporate prior physical knowledge into the AI model for improved performance.

Main Methods:

An end-to-end deep learning framework using a cascaded 3D convolutional neural network for landmark detection.
Multi-scale feature fusion and physically meaningful regression losses (PRL, DRL).
Data augmentation strategies simulating complex clinical scenarios.

Main Results:

Achieved high performance on 229 clinical head MRI scans.
Demonstrated low point-to-point absolute error (0.872 mm) and relative error (0.10%).
Reported average angular errors of 0.502°, 0.381°, and 0.675° for sagittal, transverse, and coronal planes.

Conclusions:

The proposed deep learning approach offers high efficiency, accuracy, and robustness.
Effective for diverse clinical head MRI scans, including variations in positioning, contrast, noise, and pathologies.