Co-Clinical Imaging

Our group contributed to these new papers from the co-clinical imaging research program (CIRP) :

1.Moore, S.M.; Quirk, J.D.; Lassiter, A.W.; Laforest, R.; Ayers, G.D.; Badea, C.T.; Fedorov, A.Y.; Kinahan, P.E.; Holbrook, M.; Larson, P.E.Z.; Sriram, R.; Chenevert, T.L.; Malyarenko, D.; Kurhanewicz, J.; Houghton, A.M.; Ross, B.D.; Pickup, S.; Gee, J.C.; Zhou, R.; Gammon, S.T.; Manning, H.C.; Roudi, R.; Daldrup-Link, H.E.; Lewis, M.T.; Rubin, D.L.; Yankeelov, T.E.; Shoghi, K.I. Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical ImagingTomography 20239, 995-1009. https://doi.org/10.3390/tomography9030081

2.Gammon, S.T.; Cohen, A.S.; Lehnert, A.L.; Sullivan, D.C.; Malyarenko, D.; Manning, H.C.; Hormuth, D.A.; Daldrup-Link, H.E.; An, H.; Quirk, J.D.; Shoghi, K.; Pagel, M.D.; Kinahan, P.E.; Miyaoka, R.S.; Houghton, A.M.; Lewis, M.T.; Larson, P.; Sriram, R.; Blocker, S.J.; Pickup, S.; Badea, A.; Badea, C.T.; Yankeelov, T.E.; Chenevert, T.L. An Online Repository for Pre-Clinical Imaging Protocols (PIPs)Tomography 20239, 750-758. https://doi.org/10.3390/tomography9020060

 

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QIAL papers at SPIE Medical Imaging 2023

1)  A Allphin, R Nadkarni, D Clark, C T Badea. Ex vivo high-resolution hybrid micro-CT imaging using photon counting and energy integrating detectors. Proc. of SPIE Vol 12468, 124680V-1, 2023

2) DP Clark, FR Schwartz, A Euler, V Mergen, H Alkadhi, D Marin, CT Badea  Unsupervised learning of robust models for cardiac and photon-counting x-ray CT denoising, SPIE Medical Imaging 2023: Physics of Medical Imaging 12463, 329-337

3) AJ Allphin, DP Clark, T Thuering, P Bhandari, KB Ghaghada, CT Badea Spectral micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors, SPIE Medical Imaging 2023: Physics of Medical Imaging 12463, 153-159

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Denoising dual-energy abdominal CT of obese patients

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 objective image quality metrics like contrast-to-noise ratio (CNR) and subjective metrics like reader satisfaction.Fides R. Schwartz, Darin P. Clark, Francesca Rigiroli, Kevin Kalisz, Benjamin Wildman-Tobriner, Sarah Thomas, Joshua Wilson, Cristian T. Badea & Daniele Marin . Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patientsEur Radiol (2023). https://doi.org/10.1007/s00330-023-09644-7 

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Spectral CT imaging using GaAs and CdTe photon counting detectors

In this study, we compared the performance of PC micro-CT imaging when using two types of PCDs. We have also demonstrated the ability of PCDs to separate a wide range of K-edge materials.

Alex Jeffrey Allphin, Darin P Clark, Thomas Thüring, Prajwal Bhandari, Ketan B Ghaghada, Cristian T Badea. Micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors. Physics in Medicine & Biology, 2023, DOI 10.1088/1361-6560/acc77e

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Group Photo

QIAL members and the Bass Connections team, March 2023

Artist and T-shirts Designer: Nariman (Ali) Mahzarnia

 

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Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials

This overview paper describes the ten co-clinical trials of investigators from eleven institutions who are supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group.

Examples of MRI and micro-CT images in the p53/MCA model and the schematics of the preclinical arm of the clinical trial organized at Duke.

Peehl, D.M.; Badea, C.T.; Chenevert, T.L.; Daldrup-Link, H.E.; Ding, L.; Dobrolecki, L.E.; Houghton, A.M.; Kinahan, P.E.; Kurhanewicz, J.; Lewis, M.T.; Li, S.; Luker, G.D.; Ma, C.X.; Manning, H.C.; Mowery, Y.M.; O’Dwyer, P.J.; Pautler, R.G.; Rosen, M.A.; Roudi, R.; Ross, B.D.; Shoghi, K.I.; Sriram, R.; Talpaz, M.; Wahl, R.L.; Zhou, R. Animal Models and Their Role in Imaging-Assisted Co-Clinical TrialsTomography 20239, 657-680. https://doi.org/10.3390/tomography9020053

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Identifying vulnerable brain networks associated with Alzheimer’s disease risk

Ali Mahzarnia, Jacques A Stout, Robert J Anderson, Hae Sol Moon, Zay Yar Han, Kate Beck, Jeffrey N Browndyke, David B Dunson, Kim G Johnson, Richard J O’Brien, Alexandra Badea, Identifying vulnerable brain networks associated with Alzheimer’s disease riskCerebral Cortex, 2022;, bhac419, https://doi.org/10.1093/cercor/bhac419

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QIAL papers @ CT Meeting 2022

1. C. Badea, D. Clark, A. Alphin, J. C. Ramirez-Giraldo, P. Bhandari, Y. Mowery, K. Ghaghada. Co-clinical photon counting CT research for multi-contrast imaging. Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123040G (2022)  https://doi.org/10.1117/12.2646399
2. Darin P. Clark, Alex J. Allphin, Yvonne M. Mowery, Cristian T. Badea. Photon-counting x-ray CT perfusion imaging in animal models of cancer . Proceedings Volume 7th International Conference on Image Formation in X-Ray Computed Tomography, 123041B (2022) https://doi.org/10.1117/12.2646403
3. Rohan Nadkarni, Alex Allphin, Darin Clark, Cristian Badea. Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels.  Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123041K (2022)) https://doi.org/10.1117/12.2646405
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Preclinical implementation of a clinical trial

This study aimed to investigate whether adding neoadjuvant radiotherapy (RT), anti-programmed cell death protein-1 (PD-1) antibody (anti-PD-1), or RT + anti-PD-1 to surgical resection improves disease-free survival for mice with soft tissue sarcomas (STS). The paper has been included in the highlights.

https://doi.org/10.1158/1535-7163.MCT-21-0991 

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Deep learning based material decomposition for photon counting CT

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 & BiologyVolume 67Number 15https://doi.org/10.1088/1361-6560/ac7d34

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