An optimized registration workflow and standard geometric space for small animal brain imaging

Area of Science:

• Biomedical Imaging
• Neuroscience
• Medical Physics

Background:

• Reproducibility and transparency in scientific data processing are crucial for reliable results.
• Comparability of imaging data, especially magnetic resonance imaging (MRI), across subjects and studies depends on accurate registration to a standard reference space.
• Current small animal MRI processing workflows are often adapted from human data, leading to suboptimal registration and reduced data quality.

Purpose of the Study:

• To develop and validate a generic, reproducible workflow optimized for mouse brain MRI registration.
• To establish a standard reference space for harmonizing small animal MRI data.
• To introduce automated quality control (QC) metrics and visualization tools for improved operator inspection.

Main Methods:

• Development of a novel, open-source processing workflow specifically for mouse brain MRI.
• Creation of a standard reference space tailored for small animal imaging.
• Implementation of four automated quality control (QC) metrics and a visualization method for data inspection.

Main Results:

• The new workflow demonstrates more consistent performance compared to legacy practices.
• It better preserves inter-subject variance while minimizing intra-session variance.
• Root-mean-square error (RMSE) for volume and smoothness conservation was improved approximately two-fold.

Conclusions:

• The proposed open-source workflow and QC metrics offer a new standard for small animal MRI registration.
• This approach ensures workflow robustness, enhances data comparability, and validates region assignment.
• These improvements are essential for ensuring the comparability of scientific results across diverse experiments and research centers.