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DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.

Mahdi Mobarak-Abadi¹, Ahmad Mahmoudi-Aznaveh², Hamed Dehghani³

¹Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran.

Frontiers in Psychiatry

|July 15, 2024

Summary

This summary is machine-generated.

DeepRetroMoCo, a novel deep learning method, significantly improves motion artifact correction in spinal cord fMRI. This approach enhances image quality and processing speed compared to existing methods.

Keywords:

deep learning fMRI motion correction spinal cord unsupervised

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Area of Science:

Neuroimaging
Medical Image Analysis
Machine Learning

Background:

Spinal cord fMRI preprocessing faces challenges with motion artifacts.
Existing motion correction algorithms for brain are not optimal for spinal cord.
Developing effective motion correction is crucial for spinal cord fMRI data quality.

Purpose of the Study:

To introduce DeepRetroMoCo, a deep learning model for motion artifact detection and correction in spinal cord fMRI.
To evaluate the performance of DeepRetroMoCo against established methods.

Main Methods:

A convolutional neural network (CNN) named DeepRetroMoCo was developed using unsupervised learning.
The model was trained and tested on axial T2*-weighted spinal cord fMRI data from 27 participants.
Performance was compared to the sct_fmri_moco method using tSNR and DVARS metrics.

Main Results:

DeepRetroMoCo achieved significantly higher average temporal signal-to-noise ratio (tSNR) in the cervical cord.
Lower Delta Variation Signal (DVARS) values were observed with DeepRetroMoCo, indicating better motion artifact reduction.
DeepRetroMoCo demonstrated a significantly faster processing time.

Conclusions:

DeepRetroMoCo offers a substantial improvement for motion correction in cervical spinal cord fMRI.
The deep learning approach provides enhanced performance and a promising solution for motion artifacts.
This method advances the quality of spinal cord fMRI analysis.