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Intersection-based slice motion estimation for fetal brain imaging.

Chloe Mercier¹, Sylvain Faisan², Alexandre Pron³

¹IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France.

Computers in Biology and Medicine

|March 20, 2025

Summary

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This study introduces a new method to fix motion artifacts in fetal MRI scans without needing to reconstruct the image. The technique uses slice intersections and machine learning to accurately correct fetal movement, improving diagnostic quality.

Area of Science:

Medical Imaging
Radiology
Biomedical Engineering

Background:

Fetal MRI is crucial for studying fetal development and early diagnosis.
Motion artifacts from maternal and fetal movement degrade fetal MRI quality.
Current methods often involve complex registration and reconstruction, leading to data loss.

Purpose of the Study:

To develop a novel, reconstruction-independent method for correcting inter-slice motion artifacts in fetal MRI.
To improve the quality of 3D fetal MRI volumes by addressing motion-induced artifacts.
To offer an alternative to existing motion correction techniques that may reduce data quality.

Main Methods:

Utilized the intersections of orthogonal MRI slices to estimate motion.
Developed a machine learning classifier to identify misaligned slices.

Keywords:

Fetal brain Magnetic resonance imaging Registration

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Employed a multistart optimization approach for parameter correction of misaligned slices.

Main Results:

Demonstrated very low registration errors on simulated datasets.
Qualitative analysis on real fetal MRI data showed superior performance compared to existing methods.
The proposed method effectively corrects motion artifacts without compromising data integrity.

Conclusions:

The novel reconstruction-independent method significantly improves fetal MRI quality by correcting motion artifacts.
This approach offers a more robust and effective solution for motion correction in fetal imaging.
The machine learning-based slice identification enhances the accuracy and reliability of the correction process.