High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry.

Abstract

Background

Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT.

Methods

We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex.

Results

Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD.

Conclusions

This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1371/journal.pone.0303288

Publication Info

Nadkarni, Rohan, Zay Yar Han, Robert J Anderson, Alex J Allphin, Darin P Clark, Alexandra Badea and Cristian T Badea (2024). High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry. PloS one, 19(5). p. e0303288. 10.1371/journal.pone.0303288 Retrieved from https://hdl.handle.net/10161/31309.

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Scholars@Duke

Clark

Darin Clark

Assistant Professor in Radiology
Badea

Alexandra Badea

Associate Professor in Radiology

I have a joint appointment in Radiology and Neurology and my research focuses on neurological conditions like Alzheimer’s disease. I work on imaging and analysis to provide a comprehensive characterization of the brain. MRI is particularly suitable for brain imaging, and diffusion tensor imaging is an important tool for studying brain microstructure, and the connectivity amongst gray matter regions.  

I am interested in image segmentation, morphometry and shape analysis, as well as in integrating information from MRI with genetics, and behavior. Our approaches  target: 1) phenotyping the neuroanatomy using imaging; 2) uncovering the link between structural and functional changes, the genetic bases, and environmental factors. I am interested in generating methods and tools for comprehensive phenotyping.

We use high-performance cluster computing to accelerate our image analysis. We use compressed sensing image reconstruction, and process large image arrays using deformable registration, perform segmentation based on multiple image contrasts including diffusion tensor imaging, as well as voxel, and graph analysis for connectomics.

At BIAC  my efforts focus on developing multivariate biomarkers and identifying vulnerable networks based on genetic risk for Alzheimer's disease.

My enthusiasm comes from the possibility to extend from single to integrative multivariate and network based analyses to obtain a comprehensive picture of normal development and aging, stages of disease, and the effects of treatments.  I am working on multivariate image analysis and predictive modeling approaches to help better understand early biomarkers for human disease indirectly through mouse models, as well as directly in human studies. 

I am dedicated to supporting an increase in female presence in STEM fields, and love working with students. The Bass Connections teams involve undergraduate students in research, providing them the opportunity to do independent research studies and get involved with the community. These students have for example takes classes such as:

BME 394: Projects in Biomedical Engineering (GE)
BME 493: Projects in Biomedical Engineering (GE)
ECE 899: Special Readings in Electrical Engineering
NEUROSCI 493: Research Independent Study 1


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