Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans

Magnetic Resonance Imaging (MRI) is a vital non-invasive tool for high-resolution brain imaging, yet its long acquisition time frequently results in patient movement leading to motion artifacts, particularly among non-compliant patients like newborns. While Deep Learning (DL) models have emerged as powerful retrospective solutions for motion mitigation, their performance is expected to degrade when applied to data from different scanners, protocols, or patient populations. This thesis investigates the generalizability of DL-based motion correction across heterogeneous adult and neonatal brain MRI domains. The research is conducted through three primary aims. First, the study evaluates the domain generalization of a 3D U-Net model across multi-center adult datasets - IXI, Calgary-Campinas, and OASIS-3, comprising 11 imaging centers with diverse scanner vendors and field strengths. Results indicate that models generalize effectively to unseen adult datasets from various centers, while superior performance is achieved when training incorporates broader intensity distributions. Second, the thesis explores cross-domain transfer learning by applying adult-trained models to the neonatal domain, utilizing the dHCP dataset, which contains real motion from neonatal subjects. Results indicate that adult-pretrained models learn domain-agnostic structural priors, allowing them to reconstruct neonatal brain anatomy despite significant shifts in contrast, size, and motion characteristics. Crucially, fine-tuning with as few as three neonatal scans was shown to substantially improve the mitigation of neonatal-specific artifacts, demonstrating an efficient adaptation strategy for data-scarce domains. Third, the study critically assesses the reliability of common image quality metrics (IQMs). The analysis reveals that traditional metrics are often insensitive to subtle motion and can even yield paradoxical results, where motion-corrupted images appear higher in quality than corrected ones. Overall, this work highlights the potential for deploying robust motion-mitigation frameworks in diverse clinical settings. By leveraging transfer learning, this thesis provides a foundation for more reliable and scalable deep learning–based motion correction in pediatric and adult neuroimaging.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Engineering – Biomedical

Grantor

Schulich School of Engineering

Year dc:date.issued

2026

Author dc:creator

• Ashraf, Saad Bin

Advisor dc:contributor.advisor

• Bento, Mariana

Committee members dc:contributor.committeemember

• Bray, Signe
• Wilms, Matthias
• Abbasi, Zahra

• Brain MRI
• Motion Mitigation
• Deep Learning
• Heterogeneous Multi-center Datasets
• Domain Generalization
• Motion Artifacts
• Quantitative Assessment
• Qualitative Assessment
• Healthy controls

Statement dc:rights

• Unless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

en

DOI

https://dx.doi.org/10.11575/PRISM/51192

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/124367