Category Archives: spectral CT

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 →

R Nadkarni, A Allphin, DP Clark, CT Badea. Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels.  Physics in Medicine & Biology, Volume 67, Number 15 . https://doi.org/10.1088/1361-6560/ac7d34

We presented this talk  on spectral micro-CT and its applications at the NCI CIRP Annual Meeting,  June 22-23, 2022.