Quantitative Imaging and Analysis Lab

Badea, Cristian. “Principles of Micro X-ray Computed Tomography.” In Molecular Imaging Principles and Practice, edited by Brian Ross and Sanjiv Sam Gambir, 1:47–64. Academic Press, 2021. https://doi.org/10.1016/B978-0-12-816386-3.00006-5.

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 

Examples of high-resolution, ex vivo vascular imaging using micro-CT and BriteVu as a vascular contrast agent. We illustrate mouse vasculature in the kidney (A), the head (B), and the thorax (C).

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: https://gitlab.oit.duke.edu/dpc18/duke-ct-spectral-extrapolation. It includes code for both of our DE extrapolation papers:

(1) Clark, D. P., Schwartz, F. R., Marin, D., Ramirez‐Giraldo, J. C., & Badea, C. T. (2020). Deep learning based spectral extrapolation for dual‐source, dual‐energy x‐ray computed tomography. Medical Physics, 47(9), 4150-4163.

(2) Schwartz, F. R., Clark, D. P., Ding, Y., Ramirez-Giraldo, J. C., Badea, C. T., & Marin, (2021). Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT – a retrospective pilot study. European Journal of Radiology, 109734.

Our QIAL papers presented at the SPIE Medical Imaging 2021:

1. 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 
2. Holbrook MD, Clark DP, Badea CT. Deep learning based spectral distortion correction and decomposition for photon counting CT using calibration provided by an energy integrated detector. SPIE Medical Imaging. 2021; 1159520. https://doi.org/10.1117/12.2581124
3. Holbrook MD, Clark DP, Patel R, Qi Y, Mowery YM, Badea CT. Towards deep learning segmentation of lung nodules using micro-CT data. SPIE Medical Imaging. 2021; 116000I. https://doi.org/10.1117/12.2581120

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 CT’s role as a powerful anatomical imaging modality. However, the utility of PCDs suffers due to distorted spectral measurements, affecting the accuracy of material decomposition. We attempt to improve material decomposition accuracy using our novel hybrid dual-source micro-CT system which combines a PCD and an energy integrating detector.  https://doi.org/10.1088/1361-6560/aba8b2…