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Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning Matthew D. Holbrook; Darin P. Clark; Rutulkumar Patel; Yi Qi; Alex M. Bassil; Yvonne M. Mowery; Cristian T. Badea Tomography 2021, Volume 7, Issue 3, 358-372

Our  new review paper on Micro-CT is now published : D.P.Clark, C.T.Badea, Advances in micro-CT imaging of small animals”: Physica Medica, Volume 88, August 2021, Pages 175-192 

New deep learning paper on Clinical CT  from our group:  Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT—A retrospective pilot study.  Eur J Radiol. 2021 Jun;139:109734. doi: 10.1016/j.ejrad.2021.109734. Epub 2021 The code is available at: … Continue reading →

Our QIAL papers presented at the SPIE Medical Imaging 2021: Clark DP, Badea CT. A constrained Bregman framework for unsupervised convolutional denoising of multi-channel x-ray CT data. SPIE Medical Imaging. 2021; 115950J. https://doi.org/10.1117/12.2581832  Holbrook MD, Clark DP, Badea CT. Deep … Continue reading →

Our keynote talk on Deep Learning Approaches in Spectral CT at the 2nd Annual Translational Imaging Conference AI and Machine Learning in Imaging.  

We combined MRI and micro-CT to show that lack of GIT1 results in skull shape abnormalities, brain atrophy, white matter and cortical layer deficiencies. Clustering of volume covariance adjacency matrices identified vulnerable brain networks. https://www.sciencedirect.com/science/article/pii/S0730725X20304537    

Preclinical micro-CT provides a hotbed in which to develop new imaging technologies, including spectral CT using photon counting detector (PCD) technology. Spectral imaging using PCDs promises to expand x-ray CT as a functional imaging modality, capable of molecular imaging, while maintaining … Continue reading →

Data completion is needed in dual‐source, dual‐energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Here we published a new Deep Learning approach for Spectral Extrapolation!