Our study links APOE gene, age, sex, and diet to brain network changes in Alzheimer’s disease.
Winter, S., Mahzarnia, A., Anderson, R. J., Han, Z. Y., Tremblay, J., Stout, J. A., Moon, H. S., Marcellino, D., Dunson, D. B., & Badea, A. (2024). Brain network fingerprints of Alzheimer’s disease risk factors in mouse models with humanized APOE alleles. Magnetic Resonance Imaging, 114, 110251. https://doi.org/10.1016/j.mri.2024.110251
Using deep learning denoising we achieve a 32x speedup over iterative methods for 5D cardiac PCCT in the mouse!
Rohan Nadkarni, Darin P. Clark, Alex Allphin, and Cristian T. Badea. Investigating Deep Learning Strategies for Fast Denoising of 5D Cardiac Photon-Counting Micro-CT Images.2024. Phys. Med. Biol. https://doi.org/10.1088/1361-6560/ad7fc6
Our new turn-table micro-CT scanner offers high-resolution, low-dose dynamic perfusion imaging in mice.
Allphin AJ, Nadkarni R, Clark DP, Gil CJ, Tomov M, Serpooshan V, Badea CT. Turn-table micro-CT scanner for dynamic perfusion imaging in mice: design, implementation, and evaluation. Phys Med Biol. 2024 Aug 13. doi: 10.1088/1361-6560/ad6edd. PMID: 39137802.
Our study on Alzheimer’s disease risk uses advanced AI models to estimate brain age in APOE/hNOS2 mouse models:
Hae Sol MoonAli MahzarniaJacques StoutRobert J AndersonZay Yar HanJessica T TremblayCristian T. BadeaAlexandra Badea; Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease. Imaging Neuroscience 2024; doi: https://doi.org/10.1162/imag_a_00245
Our team developed a high-res photon counting CT pipeline for mouse brain imaging. Faster than MRI, it reveals key differences in brain regions across APOE genotypes.
Nadkarni R, Han ZY, Anderson RJ, Allphin AJ, Clark DP, Badea A, et al. (2024) High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry. PLoS ONE 19(5): e0303288.
https://doi.org/10.1371/journal.pone.0303288
QIAL Contributions at SPIE Medical Imaging, San Diego, 2024
Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks
HS Moon, A Mahzarnia, J Stout, RJ Anderson, ZY Han, CT Badea, …
Medical Imaging 2024: Computer-Aided Diagnosis 12927, 268-275 2024
Volumetric brain region segmentation and morphometry in mouse models using high-resolution hybrid micro-CT imaging
R Nadkarni, ZY Han, RJ Anderson, A Allphin, DP Clark, A Badea, …
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 162-171 2024
Whole heart CNN-based segmentation for phenotypical analysis of APOE mouse models using photon counting cine cardiac micro-CT data
AJ Allphin, R Nadkarni, Z Han, DP Clark, A Badea, CT Badea
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 100-104 2024
Photon counting micro-CT imaging of Bi2WO6 nanoparticles
CT Badea, R Bhavane, A Allphin, R Nadkarni, DP Clark, AV Annapragada, …
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 311-319 2024
Enhancing in vivo preclinical studies with VivoVist™ and photon-counting micro-CT imaging
CT Badea, A Rickard, A Allphin, DP Clark, KB Ghaghada, S Ridwan, …
Medical Imaging 2024: Clinical and Biomedical Imaging 12930, 236-242 2024
Deep learning models for rapid denoising of 5D cardiac photon-counting micro-CT images
R Nadkarni, DP Clark, A Allphin, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 547-556 2024
Multi-channel reconstruction (MCR) toolkit v2. 0: open-source Python-based tools for x-ray CT reconstruction
DP Clark, A Allphin, R Nadkarni, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 454-459 2024
Denoising pediatric cardiac photon-counting CT data using volumetric vision transformers and unpaired training data
DP Clark, FR Schwartz, JY Cao, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 254-262 2024
Advancing preclinical micro-photon counting CT perfusion imaging: from phantom experiments to in vivo applications
AJ Allphin, DP Clark, CJ Gil, ML Tomov, V Serpooshan, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 72-79 2024
An acquisition-based approach for high-fidelity infilling of photon-counting x-ray detector pixel gaps
AJ Allphin, R Nadkarni, DP Clark, CT Badea
Medical Imaging 2024: Physics of Medical Imaging 12925, 536-541, 2024
Congratulations to runner-up Hae Sol Moon of the Robert F. Wagner All-Conference Best Student Paper Award, sponsored by SPIE and the Medical Imaging Perception Society! 
Our new study delves into Alzheimer’s disease, revealing molecular pathways tied to hallmark biomarkers & cognitive decline. Pathway analysis identified VEGF-RB as a common link, offering insights into disease mechanisms & potential treatment avenues.
Mahzarnia, Ali, Lutz, Michael W., and Badea, Alexandra. ‘A Continuous Extension of Gene Set Enrichment Analysis Using the Likelihood Ratio Test Statistics Identifies Vascular Endothelial Growth Factor as a Candidate Pathway for Alzheimer’s Disease via ITGA5’. 1 Jan. 2024 : 635 – 648. Journal of Alzheimer’s Disease, vol. 97, no. 2, pp. 635-648, 2024.DOI: 10.3233/JAD-230934
Exciting research on Age-related Macular Degeneration (AMD) and its impact on the brain! Using diffusion MRI, we observed significant brain changes in AMD patients, including reduced volume in key cognitive areas and altered brain connections.
Stout, J.A.; Mahzarnia, A.; Dai, R.; Anderson, R.J.; Cousins, S.; Zhuang, J.; Lad, E.M.; Whitaker, D.B.; Madden, D.J.; Potter, G.G.; et al. Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration. Biomedicines 2024, 12, 147. https://doi.org/10.3390/biomedicines12010147
In our study, we adopted an integrative approach that combined behavioral assessments, brain imaging, and blood transcriptomics to focus on the transition from middle to old age in APOE2 mice, leading to the identification of critical brain sub-networks and regions such as the cingulate cortex; we utilized Sparse Multiple Canonical Correlation Analysis (SMCCA) for modeling and graph neural network predictions, providing valuable insights into brain connectivity, aging, sex differences, and potential therapeutic targets.
Hae Sol Moon, Ali Mahzarnia, Jacques Stout, Robert J. Anderson, Madison Strain, Jessica T. Tremblay, Zay Yar Han, Andrei Niculescu, Anna MacFarlane, Jasmine King, Allison Ashley-Koch, Darin Clark, Michael W. Lutz & Alexandra Badea, Multivariate Investigation of Aging in Mouse Models with Alzheimer’s Protective APOE2 Allele: A Fusion of Cognitive Metrics, Brain Imaging, & Blood Transcriptomics, Brain Structure and Function, https://doi.org/10.1007/s00429-023-02731-x
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