Cristian T. Badea
Affiliations: | Biomedical Engineering | Duke University, Durham, NC |
Area:
Radiology, Biomedical Engineering, Computer ScienceGoogle:
"Cristian Badea"
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Publications
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| Devkota L, Bhavane R, Badea CT, et al. (2025) Nanoparticle Contrast Agents for Photon-Counting Computed Tomography: Recent Developments and Future Opportunities. Wiley Interdisciplinary Reviews. Nanomedicine and Nanobiotechnology. 17: e70004 |
| Nadkarni R, Clark DP, Allphin AJ, et al. (2024) Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images. Physics in Medicine and Biology |
| Blocker SJ, Mowery YM, Everitt JI, et al. (2024) MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma. Frontiers in Oncology. 14: 1287479 |
| Nadkarni R, Han ZY, Anderson RJ, et al. (2024) High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry. Plos One. 19: e0303288 |
| Moon HS, Mahzarnia A, Stout J, et al. (2023) Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease. Biorxiv : the Preprint Server For Biology |
| Allphin AJ, Mahzarnia A, Clark DP, et al. (2023) Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models. Plos One. 18: e0291733 |
| Gil CJ, Evans CJ, Li L, et al. (2023) Leveraging 3d Bioprinting And Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs. Advanced Healthcare Materials. e2302271 |
| Nadkarni R, Clark DP, Allphin AJ, et al. (2023) A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images. Tomography (Ann Arbor, Mich.). 9: 1286-1302 |
| Clark DP, Badea CT. (2023) MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Medical Physics |
| Moore SM, Quirk JD, Lassiter AW, et al. (2023) Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging. Tomography (Ann Arbor, Mich.). 9: 995-1009 |