Category Archives: spectral CT

Using photon-counting CT and deep learning, we show that voluntary exercise significantly mitigates high-fat diet–induced cardiac dysfunction in adult APOE-targeted mice, with the strongest benefits observed in APOE4 and humanized innate immune (HN) backgrounds. Exercise mitigates high-fat diet–induced cardiac dysfunction … Continue reading →

How do genetics, sex, and immune signaling shape bone health as mice age?Our study shows that photon-counting CT  in APOE mouse models reveals strong sex- and immune-dependent bone loss, with female HN mice showing the most pronounced decline. Nadkarni, R.; … Continue reading →

We developed a data-adaptive, self-supervised deep learning method combining sparse coding (SC) +  visual transformer (ViT) for denoising pediatric cardiac photon-counting CT. Preserves anatomy, adapts to noise, generalizes well—even to preclinical data. Clark, D. P., Cao, J. Y., & Badea, … Continue reading →

Our team developed a CT-visible bioprinted vascular patch using dual-contrast bioinks. Photon-counting CT enabled precise tracking of structure and perfusion, highlighting the role of imaging in cardiac repair. Gil, C. J., Allphin, A. J., Jin, L., Salar Amoli, M., Rezapourdamanab, … Continue reading →

This study introduces a benchtop photon-counting micro-CT system for dynamic small animal perfusion imaging. Deep learning methods enable rapid 5D  (3D + Time + Energy) imaging and quantitative blood flow analysis, supporting high-throughput preclinical vascular research. Allphin AJ, Nadkarni R, … Continue reading →

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 … Continue reading →

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. … Continue reading →

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, … Continue reading →

Our new study develops an advanced imaging pipeline using photon-counting CT to assess cardiac structure & function in mouse models of apolipoprotein E gene variations. Results highlight genotype-specific cardiovascular differences and the impact of diet. Allphin, A. J., Mahzarnia, A., … Continue reading →

The purpose of this study was to evaluate a novel algorithm for noise reduction in obese patients for dual-source dual-energy (DE) CT abdominal imaging. We demonstrated that multi-channel denoising methods (RSKR, multi-energy (ME)-NLM) could reduce image noise and improve both … Continue reading →