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

Search research articles

Related Experiment Video

Updated: Oct 22, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Deep learning-based parameter estimation in fetal diffusion-weighted MRI.

Davood Karimi¹, Camilo Jaimes¹, Fedel Machado-Rivas¹

¹Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.

|August 29, 2021

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis.

Scientific data·2026

Same author

Deep learning reconstruction improves appendix visualization on pediatric magnetic resonance imaging (MRI): a single-center experience.

Pediatric radiology·2026

Same author

Deep medullary vein thrombosis in neonates: a retrospective study.

Journal of perinatology : official journal of the California Perinatal Association·2026

Same author

From Velasco to NAUTILUS: an evolution of centromedian nucleus targeting in epilepsy.

Journal of neurosurgery·2026

Same author

ACR Appropriateness Criteria® Hydrocephalus-Child.

Journal of the American College of Radiology : JACR·2026

Same author

Strategies for radiology faculty recruitment and retention in a competitive market: implications for pediatric radiology.

Pediatric radiology·2026

Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026

Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026

Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026

Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026

Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026

Same journal

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026

See all related articles

This study introduces a new method using generated fetal brain MRI data to train deep learning models. This approach significantly improves the accuracy and precision of diffusion-weighted MRI parameter estimation in fetuses.

Area of Science:

Medical Imaging
Neuroscience
Artificial Intelligence

Background:

Fetal brain diffusion-weighted MRI (DW-MRI) faces challenges due to motion and low signal-to-noise ratio.
Accurate parameter estimation in fetal DW-MRI is difficult, with limited deep learning applications due to data acquisition challenges.

Purpose of the Study:

To develop a novel methodology for robust fetal brain DW-MRI parameter estimation.
To generate realistic synthetic fetal DW-MRI data for training deep learning models.

Main Methods:

Utilized scans from fetuses and prematurely-born infants to create high-quality newborn scans.
Generated synthetic DW-MRI data mimicking fetal acquisition parameters and noise characteristics.
Trained a convolutional neural network (CNN) on generated data to estimate color fractional anisotropy (CFA).

Keywords:

Fetal MRI color fractional anisotropy deep learning diffusion weighted imaging

More Related Videos

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Diffusion Imaging in the Rat Cervical Spinal Cord

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Related Experiment Videos

Last Updated: Oct 22, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Diffusion Imaging in the Rat Cervical Spinal Cord

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Main Results:

The CNN trained on synthetic data demonstrated significantly lower reconstruction error (p<0.001) and higher precision (p<0.001) compared to standard methods.
Expert assessments confirmed superior overall reconstruction quality (p<0.001) and accuracy in 11 regions of interest (p<0.001).

Conclusions:

The proposed data generation pipeline effectively enables deep learning for fetal brain DW-MRI analysis.
This machine learning approach offers a significant advancement in fetal neuroimaging parameter estimation.