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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

A motion assessment method for reference stack selection in fetal brain MRI reconstruction based on tensor rank

Haoan Xu¹, Wen Shi^1,2, Jiwei Sun¹

¹Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.

NMR in Biomedicine

|September 4, 2024

Summary

This summary is machine-generated.

A novel method using CANDECOMP/PARAFAC (CP) decomposition accurately assesses fetal brain motion in 3D stacks. This approach improves 3D volume reconstruction by identifying the least-motion reference stack for registration.

Keywords:

fetal brain MRI motion assessment singular value decomposition slice‐to‐volume registration tensor decomposition

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

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Published on: January 7, 2021

Area of Science:

Medical Imaging
Computational Neuroscience
Biomedical Engineering

Background:

3D fetal brain volume reconstruction relies on slice-to-volume registration and super-resolution.
Accurate motion assessment (MA) is critical for selecting the reference stack with minimal motion.
Current MA methods using 2D singular value decomposition (SVD) lose spatial information.

Purpose of the Study:

To introduce a novel MA method based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition.
To compare the performance of the CP-based MA method against the existing 2D SVD-based method.
To evaluate the impact of the CP-based MA method on 3D fetal brain volume reconstruction.

Main Methods:

Utilized 3D low-rank approximation via CP decomposition to analyze motion in 2D slice stacks.
Defined the difference between the original stack and its low-rank approximation as the motion indicator.
Compared CP-based MA with SVD-based MA on simulated and real fetal brain data.

Main Results:

The CP-based method demonstrated higher sensitivity to small motions and lower baseline bias than SVD.
CP achieved 95.45% accuracy in identifying the minimum motion stack, compared to 58.18% for SVD.
Integrating CP into the SRR-SVR pipeline significantly enhanced 3D volume reconstruction quality.

Conclusions:

The proposed CP-based MA method offers superior performance over SVD methods for fetal brain imaging.
CP provides higher sensitivity, accuracy, and lower bias in motion assessment.
This method serves as an effective prior step for improving 3D fetal brain reconstruction.