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Allphin AJ, Mowery YM, Lafata KJ, Clark DP, Bassil AM, Castillo R, Odhiambo D, Holbrook MD, Ghaghada KB, Badea CT. Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden. Tomography 2022; 8:740-753. [PMID: 35314638 PMCID: PMC8938796 DOI: 10.3390/tomography8020061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/04/2022] [Accepted: 03/08/2022] [Indexed: 01/13/2023] Open
Abstract
The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/− (TLs present) and Rag2−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2+/− and Rag2−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.
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Affiliation(s)
- Alex J. Allphin
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 277101, USA; (D.P.C.); (M.D.H.)
- Correspondence: (A.J.A.); (C.T.B.)
| | - Yvonne M. Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA; (Y.M.M.); (K.J.L.); (A.M.B.); (R.C.); (D.O.)
- Department of Head and Neck Surgery & Communication Sciences, Duke University Medical Center, Durham, NC 27710, USA
| | - Kyle J. Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA; (Y.M.M.); (K.J.L.); (A.M.B.); (R.C.); (D.O.)
- Department of Radiology, Duke University, Durham, NC 27710, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27710, USA
| | - Darin P. Clark
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 277101, USA; (D.P.C.); (M.D.H.)
| | - Alex M. Bassil
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA; (Y.M.M.); (K.J.L.); (A.M.B.); (R.C.); (D.O.)
| | - Rico Castillo
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA; (Y.M.M.); (K.J.L.); (A.M.B.); (R.C.); (D.O.)
| | - Diana Odhiambo
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA; (Y.M.M.); (K.J.L.); (A.M.B.); (R.C.); (D.O.)
| | - Matthew D. Holbrook
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 277101, USA; (D.P.C.); (M.D.H.)
| | - Ketan B. Ghaghada
- E.B. Singleton Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA;
- Department of Radiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Cristian T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 277101, USA; (D.P.C.); (M.D.H.)
- Correspondence: (A.J.A.); (C.T.B.)
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Clark D, Badea C. Advances in micro-CT imaging of small animals. Phys Med 2021; 88:175-192. [PMID: 34284331 PMCID: PMC8447222 DOI: 10.1016/j.ejmp.2021.07.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/23/2021] [Accepted: 07/05/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research. METHODS Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field. RESULTS Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT. CONCLUSIONS All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.
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Affiliation(s)
- D.P. Clark
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710
| | - C.T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710
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Holbrook MD, Clark DP, Badea CT. Dual source hybrid spectral micro-CT using an energy-integrating and a photon-counting detector. Phys Med Biol 2020; 65:205012. [PMID: 32702686 DOI: 10.1088/1361-6560/aba8b2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
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. Comparisons are made between PCD-only and hybrid CT results, both reconstructed with our iterative, multi-channel algorithm based on the split Bregman method and regularized with rank-sparse kernel regression. Multi-material decomposition is performed post-reconstruction for separation of iodine (I), gold (Au), gadolinium (Gd), and calcium (Ca). System performance is evaluated first in simulations, then in micro-CT phantoms, and finally in an in vivo experiment with a genetically modified p53fl/fl mouse cancer model with Au, Gd, and I nanoparticle (NP)-based contrasts agents. Our results show that the PCD-only and hybrid CT reconstructions offered very similar spatial resolution at 10% MTF (PCD: 3.50 lp mm-1; hybrid: 3.47 lp mm-1) and noise characteristics given by the noise power spectrum. For material decomposition we note successful separation of the four basis materials. We found that hybrid reconstruction reduces RMSE by an average of 37% across all material maps when compared to PCD-only of similar dose but does not provide much difference in terms of concentration accuracy. The in vivo results show separation of targeted Au and accumulated Gd NPs in the tumor from intravascular iodine NPs and bone. Hybrid spectral micro-CT can benefit nanotechnology and cancer research by providing quantitative imaging to test and optimize various NPs for diagnostic and therapeutic applications.
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Affiliation(s)
- M D Holbrook
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States of America
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