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Related Experiment Video

Updated: Aug 21, 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

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency.

Junshen Xu¹, Sayeri Lala¹, Borjan Gagoski²

¹Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|November 16, 2022

Summary

This study introduces a semi-supervised deep learning method to automatically detect motion artifacts in fetal brain MRI scans, improving diagnostic accuracy and reducing assessment time.

Keywords:

Convolutional neural network (CNN)Fetal magnetic resonance imaging (MRI)Image quality assessment Semi-supervised learning

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Last Updated: Aug 21, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Fetal brain MRI is crucial for diagnosing abnormalities but is significantly hampered by fetal motion, leading to image artifacts.
Manual assessment of fetal MR image quality is time-consuming and subjective.

Purpose of the Study:

To develop a semi-supervised deep learning method for automated detection of motion-corrupted slices in fetal brain MRI.
To improve the efficiency and accuracy of fetal MR image quality assessment.

Main Methods:

A semi-supervised deep learning approach utilizing a mean teacher model with ROI consistency loss.
The model enforces consistency between student and teacher networks, focusing on the brain region to detect artifacts.

Main Results:

The method achieved approximately 6% higher accuracy compared to supervised learning methods.
It outperformed existing state-of-the-art semi-supervised learning techniques on a large dataset.
Online evaluation on an MR scanner demonstrated feasibility for real-time quality assessment.

Conclusions:

The proposed semi-supervised deep learning method effectively detects motion artifacts in fetal brain MRI.
This approach enhances diagnostic accuracy and offers a feasible solution for online image quality control during scans.