Browsing by Author "Badea, Cristian T"
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Item Open Access A comparative analysis of EGFR-targeting antibodies for gold nanoparticle CT imaging of lung cancer.(PloS one, 2018-01) Ashton, Jeffrey R; Gottlin, Elizabeth B; Patz, Edward F; West, Jennifer L; Badea, Cristian TComputed tomography (CT) is the standard imaging test used for the screening and assessment of suspected lung cancer, but distinguishing malignant from benign nodules by CT is an ongoing challenge. Consequently, a large number of avoidable invasive procedures are performed on patients with benign nodules in order to exclude malignancy. Improving cancer discrimination by non-invasive imaging could reduce the need for invasive diagnostics. In this work we focus on developing a gold nanoparticle contrast agent that targets the epidermal growth factor receptor (EGFR), which is expressed on the cell surface of most lung adenocarcinomas. Three different contrast agents were compared for their tumor targeting effectiveness: non-targeted nanoparticles, nanoparticles conjugated with full-sized anti-EGFR antibodies (cetuximab), and nanoparticles conjugated with a single-domain llama-derived anti-EGFR antibody, which is smaller than the cetuximab, but has a lower binding affinity. Nanoparticle targeting effectiveness was evaluated in vitro by EGFR-binding assays and in cell culture with A431 cells, which highly express EGFR. In vivo CT imaging performance was evaluated in both C57BL/6 mice and in nude mice with A431 subcutaneous tumors. The cetuximab nanoparticles had a significantly shorter blood residence time than either the non-targeted or the single-domain antibody nanoparticles. All of the nanoparticle contrast agents demonstrated tumor accumulation; however, the cetuximab-targeted group had significantly higher tumor gold accumulation than the other two groups, which were statistically indistinguishable from one another. In this study we found that the relative binding affinity of the targeting ligands had more of an effect on tumor accumulation than the circulation half life of the nanoparticles. This study provides useful insight into targeted nanoparticle design and demonstrates that nanoparticle contrast agents can be used to detect tumor receptor overexpression. Combining receptor status data with traditional imaging characteristics has the potential for better differentiation of malignant lung tumors from benign lesions.Item Open Access A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.(Tomography (Ann Arbor, Mich.), 2023-07) Nadkarni, Rohan; Clark, Darin P; Allphin, Alex J; Badea, Cristian TPhoton-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU's potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research.Item Open Access A LabVIEW Platform for Preclinical Imaging Using Digital Subtraction Angiography and Micro-CT.(J Med Eng, 2013) Badea, Cristian T; Hedlund, Laurence W; Johnson, G AllanCT and digital subtraction angiography (DSA) are ubiquitous in the clinic. Their preclinical equivalents are valuable imaging methods for studying disease models and treatment. We have developed a dual source/detector X-ray imaging system that we have used for both micro-CT and DSA studies in rodents. The control of such a complex imaging system requires substantial software development for which we use the graphical language LabVIEW (National Instruments, Austin, TX, USA). This paper focuses on a LabVIEW platform that we have developed to enable anatomical and functional imaging with micro-CT and DSA. Our LabVIEW applications integrate and control all the elements of our system including a dual source/detector X-ray system, a mechanical ventilator, a physiological monitor, and a power microinjector for the vascular delivery of X-ray contrast agents. Various applications allow cardiac- and respiratory-gated acquisitions for both DSA and micro-CT studies. Our results illustrate the application of DSA for cardiopulmonary studies and vascular imaging of the liver and coronary arteries. We also show how DSA can be used for functional imaging of the kidney. Finally, the power of 4D micro-CT imaging using both prospective and retrospective gating is shown for cardiac imaging.Item Open Access A neural network-based method for spectral distortion correction in photon counting x-ray CT.(Physics in medicine and biology, 2016-08) Touch, Mengheng; Clark, Darin P; Barber, William; Badea, Cristian TSpectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables both 4 energy bins acquisition, as well as full-spectrum mode in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical effects in the detector and can be very noisy due to photon starvation in narrow energy bins. To address spectral distortions, we propose and demonstrate a novel artificial neural network (ANN)-based spectral distortion correction mechanism, which learns to undo the distortion in spectral CT, resulting in improved material decomposition accuracy. To address noise, post-reconstruction denoising based on bilateral filtration, which jointly enforces intensity gradient sparsity between spectral samples, is used to further improve the robustness of ANN training and material decomposition accuracy. Our ANN-based distortion correction method is calibrated using 3D-printed phantoms and a model of our spectral CT system. To enable realistic simulations and validation of our method, we first modeled the spectral distortions using experimental data acquired from (109)Cd and (133)Ba radioactive sources measured with our PCXD. Next, we trained an ANN to learn the relationship between the distorted spectral CT projections and the ideal, distortion-free projections in a calibration step. This required knowledge of the ground truth, distortion-free spectral CT projections, which were obtained by simulating a spectral CT scan of the digital version of a 3D-printed phantom. Once the training was completed, the trained ANN was used to perform distortion correction on any subsequent scans of the same system with the same parameters. We used joint bilateral filtration to perform noise reduction by jointly enforcing intensity gradient sparsity between the reconstructed images for each energy bin. Following reconstruction and denoising, the CT data was spectrally decomposed using the photoelectric effect, Compton scattering, and a K-edge material (i.e. iodine). The ANN-based distortion correction approach was tested using both simulations and experimental data acquired in phantoms and a mouse with our PCXD-based micro-CT system for 4 bins and full-spectrum acquisition modes. The iodine detectability and decomposition accuracy were assessed using the contrast-to-noise ratio and relative error in iodine concentration estimation metrics in images with and without distortion correction. In simulation, the material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with 50% and 20% reductions in material concentration measurement error in full-spectrum and 4 energy bins cases, respectively. Overall, experimental data confirms that full-spectrum mode provides superior results to 4-energy mode when the distortion corrections are applied. The material decomposition accuracy in the reconstructed data was vastly improved following distortion correction and denoising, with as much as a 41% reduction in material concentration measurement error for full-spectrum mode, while also bringing the iodine detectability to 4-6 mg ml(-1). Distortion correction also improved the 4 bins mode data, but to a lesser extent. The results demonstrate the experimental feasibility and potential advantages of ANN-based distortion correction and joint bilateral filtration-based denoising for accurate K-edge imaging with a PCXD. Given the computational efficiency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.Item Open Access A Plasmonic Gold Nanostar Theranostic Probe for In Vivo Tumor Imaging and Photothermal Therapy.(Theranostics, 2015) Liu, Yang; Ashton, Jeffrey R; Moding, Everett J; Yuan, Hsiangkuo; Register, Janna K; Fales, Andrew M; Choi, Jaeyeon; Whitley, Melodi J; Zhao, Xiaoguang; Qi, Yi; Ma, Yan; Vaidyanathan, Ganesan; Zalutsky, Michael R; Kirsch, David G; Badea, Cristian T; Vo-Dinh, TuanNanomedicine has attracted increasing attention in recent years, because it offers great promise to provide personalized diagnostics and therapy with improved treatment efficacy and specificity. In this study, we developed a gold nanostar (GNS) probe for multi-modality theranostics including surface-enhanced Raman scattering (SERS) detection, x-ray computed tomography (CT), two-photon luminescence (TPL) imaging, and photothermal therapy (PTT). We performed radiolabeling, as well as CT and optical imaging, to investigate the GNS probe's biodistribution and intratumoral uptake at both macroscopic and microscopic scales. We also characterized the performance of the GNS nanoprobe for in vitro photothermal heating and in vivo photothermal ablation of primary sarcomas in mice. The results showed that 30-nm GNS have higher tumor uptake, as well as deeper penetration into tumor interstitial space compared to 60-nm GNS. In addition, we found that a higher injection dose of GNS can increase the percentage of tumor uptake. We also demonstrated the GNS probe's superior photothermal conversion efficiency with a highly concentrated heating effect due to a tip-enhanced plasmonic effect. In vivo photothermal therapy with a near-infrared (NIR) laser under the maximum permissible exposure (MPE) led to ablation of aggressive tumors containing GNS, but had no effect in the absence of GNS. This multifunctional GNS probe has the potential to be used for in vivo biosensing, preoperative CT imaging, intraoperative detection with optical methods (SERS and TPL), as well as image-guided photothermal therapy.Item Open Access An Integrated Framework for Spectral and Temporal X-ray CT(2015) Clark, Darin PX-ray CT is widely used, both clinically and preclinically, for fast, high-resolution, anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. Common implementations of spectral and temporal (spectro-temporal) CT discretely sample the time points and energies to be reconstructed, proportionally increasing acquisition time and ionizing radiation dose with data dimensionality. Here, we propose and develop an integrated framework for spectro-temporal CT data acquisition, reconstruction, and analysis which drastically reduces the sampling time and radiation dose associated with spectro-temporal CT imaging. Specifically, we exploit the latent, gradient sparse and low rank structure of spectro-temporal CT data sets to recover their full dimensionality from highly undersampled projection measurements. We achieve reliable, high fidelity results through a novel combination of hierarchical projection sampling, the split Bregman optimization method, and piecewise-constant kernel regression. The integrated framework generalizes to arbitrary spectral and temporal CT reconstruction problems, while maintaining or even improving upon the sampling time and radiation dose associated with anatomic imaging protocols. We believe that this integrated framework will serve as the basis for a new generation of routine, functional CT imaging protocols.
Item Open Access An Online Repository for Pre-Clinical Imaging Protocols (PIPs).(Tomography (Ann Arbor, Mich.), 2023-03) Gammon, Seth T; Cohen, Allison S; Lehnert, Adrienne L; Sullivan, Daniel C; Malyarenko, Dariya; Manning, Henry Charles; Hormuth, David A; Daldrup-Link, Heike E; An, Hongyu; Quirk, James D; Shoghi, Kooresh; Pagel, Mark David; Kinahan, Paul E; Miyaoka, Robert S; Houghton, A McGarry; Lewis, Michael T; Larson, Peder; Sriram, Renuka; Blocker, Stephanie J; Pickup, Stephen; Badea, Alexandra; Badea, Cristian T; Yankeelov, Thomas E; Chenevert, Thomas LProviding method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.Item Open Access Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials(Tomography) Peehl, Donna M; Badea, Cristian T; Chenevert, Thomas L; Daldrup-Link, Heike E; Ding, Li; Dobrolecki, Lacey E; Houghton, A McGarry; Kinahan, Paul E; Kurhanewicz, John; Lewis, Michael T; Li, Shunqiang; Luker, Gary D; Ma, Cynthia X; Manning, H Charles; Mowery, Yvonne M; O'Dwyer, Peter J; Pautler, Robia G; Rosen, Mark A; Roudi, Raheleh; Ross, Brian D; Shoghi, Kooresh I; Sriram, Renuka; Talpaz, Moshe; Wahl, Richard L; Zhou, RongThe availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients’ tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.Item Open Access Assessing cardiac injury in mice with dual energy-microCT, 4D-microCT, and microSPECT imaging after partial heart irradiation.(Int J Radiat Oncol Biol Phys, 2014-03-01) Lee, Chang-Lung; Min, Hooney; Befera, Nicholas; Clark, Darin; Qi, Yi; Das, Shiva; Johnson, G Allan; Badea, Cristian T; Kirsch, David GPURPOSE: To develop a mouse model of cardiac injury after partial heart irradiation (PHI) and to test whether dual energy (DE)-microCT and 4-dimensional (4D)-microCT can be used to assess cardiac injury after PHI to complement myocardial perfusion imaging using micro-single photon emission computed tomography (SPECT). METHODS AND MATERIALS: To study cardiac injury from tangent field irradiation in mice, we used a small-field biological irradiator to deliver a single dose of 12 Gy x-rays to approximately one-third of the left ventricle (LV) of Tie2Cre; p53(FL/+) and Tie2Cre; p53(FL/-) mice, where 1 or both alleles of p53 are deleted in endothelial cells. Four and 8 weeks after irradiation, mice were injected with gold and iodinated nanoparticle-based contrast agents, and imaged with DE-microCT and 4D-microCT to evaluate myocardial vascular permeability and cardiac function, respectively. Additionally, the same mice were imaged with microSPECT to assess myocardial perfusion. RESULTS: After PHI with tangent fields, DE-microCT scans showed a time-dependent increase in accumulation of gold nanoparticles (AuNp) in the myocardium of Tie2Cre; p53(FL/-) mice. In Tie2Cre; p53(FL/-) mice, extravasation of AuNp was observed within the irradiated LV, whereas in the myocardium of Tie2Cre; p53(FL/+) mice, AuNp were restricted to blood vessels. In addition, data from DE-microCT and microSPECT showed a linear correlation (R(2) = 0.97) between the fraction of the LV that accumulated AuNp and the fraction of LV with a perfusion defect. Furthermore, 4D-microCT scans demonstrated that PHI caused a markedly decreased ejection fraction, and higher end-diastolic and end-systolic volumes, to develop in Tie2Cre; p53(FL/-) mice, which were associated with compensatory cardiac hypertrophy of the heart that was not irradiated. CONCLUSIONS: Our results show that DE-microCT and 4D-microCT with nanoparticle-based contrast agents are novel imaging approaches complementary to microSPECT for noninvasive assessment of the change in myocardial vascular permeability and cardiac function of mice in whom myocardial injury develops after PHI.Item Open Access Assessing the radiation response of lung cancer with different gene mutations using genetically engineered mice.(Front Oncol, 2013) Perez, Bradford A; Ghafoori, A Paiman; Lee, Chang-Lung; Johnston, Samuel M; Li, Yifan; Moroshek, Jacob G; Ma, Yan; Mukherjee, Sayan; Kim, Yongbaek; Badea, Cristian T; Kirsch, David GPURPOSE: Non-small cell lung cancers (NSCLC) are a heterogeneous group of carcinomas harboring a variety of different gene mutations. We have utilized two distinct genetically engineered mouse models of human NSCLC (adenocarcinoma) to investigate how genetic factors within tumor parenchymal cells influence the in vivo tumor growth delay after one or two fractions of radiation therapy (RT). MATERIALS AND METHODS: Primary lung adenocarcinomas were generated in vivo in mice by intranasal delivery of an adenovirus expressing Cre-recombinase. Lung cancers expressed oncogenic Kras(G12D) and were also deficient in one of two tumor suppressor genes: p53 or Ink4a/ARF. Mice received no radiation treatment or whole lung irradiation in a single fraction (11.6 Gy) or in two 7.3 Gy fractions (14.6 Gy total) separated by 24 h. In each case, the biologically effective dose (BED) equaled 25 Gy10. Response to RT was assessed by micro-CT 2 weeks after treatment. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunohistochemical staining were performed to assess the integrity of the p53 pathway, the G1 cell-cycle checkpoint, and apoptosis. RESULTS: Tumor growth rates prior to RT were similar for the two genetic variants of lung adenocarcinoma. Lung cancers with wild-type (WT) p53 (LSL-Kras; Ink4a/ARF(FL/FL) mice) responded better to two daily fractions of 7.3 Gy compared to a single fraction of 11.6 Gy (P = 0.002). There was no statistically significant difference in the response of lung cancers deficient in p53 (LSL-Kras; p53(FL/FL) mice) to a single fraction (11.6 Gy) compared to 7.3 Gy × 2 (P = 0.23). Expression of the p53 target genes p21 and PUMA were higher and bromodeoxyuridine uptake was lower after RT in tumors with WT p53. CONCLUSION: Using an in vivo model of malignant lung cancer in mice, we demonstrate that the response of primary lung cancers to one or two fractions of RT can be influenced by specific gene mutations.Item Open Access Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images.(Magnetic resonance imaging, 2022-10) Moon, Hae Sol; Heffron, Lindsay; Mahzarnia, Ali; Obeng-Gyasi, Barnabas; Holbrook, Matthew; Badea, Cristian T; Feng, Wuwei; Badea, AlexandraMagnetic resonance (MR) imaging (MRI) is commonly used to diagnose, assess and monitor stroke. Accurate and timely segmentation of stroke lesions provides the anatomico-structural information that can aid physicians in predicting prognosis, as well as in decision making and triaging for various rehabilitation strategies. To segment stroke lesions, MR protocols, including diffusion-weighted imaging (DWI) and T2-weighted fluid attenuated inversion recovery (FLAIR) are often utilized. These imaging sequences are usually acquired with different spatial resolutions due to time constraints. Within the same image, voxels may be anisotropic, with reduced resolution along slice direction for diffusion scans in particular. In this study, we evaluate the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions using single contrast (DWI) and dual contrast images (T2w FLAIR and DWI). The predicted segmentations correlate with post-stroke motor outcome measured by the National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer Upper Extremity (FM-UE) index based on the lesion loads overlapping the corticospinal tracts (CST), which is a neural substrate for motor movement and function. Although the four methods performed similarly, the 2D multimodal U-Net achieved the best results with a mean Dice of 0.737 (95% CI: 0.705, 0.769) and a relatively high correlation between the weighted lesion load and the NIHSS scores (both at baseline and at 90 days). A monotonically constrained quintic polynomial regression yielded R2 = 0.784 and 0.875 for weighted lesion load versus baseline and 90-Days NIHSS respectively, and better corrected Akaike information criterion (AICc) scores than those of the linear regression. In addition, using the quintic polynomial regression model to regress the weighted lesion load to the 90-Days FM-UE score results in an R2 of 0.570 with a better AICc score than that of the linear regression. Our results suggest that the multi-contrast information enhanced the accuracy of the segmentation and the prediction accuracy for upper extremity motor outcomes. Expanding the training dataset to include different types of stroke lesions and more data points will help add a temporal longitudinal aspect and increase the accuracy. Furthermore, adding patient-specific data may improve the inference about the relationship between imaging metrics and functional outcomes.Item Open Access Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.(Tomography (Ann Arbor, Mich.), 2023-05) Moore, Stephen M; Quirk, James D; Lassiter, Andrew W; Laforest, Richard; Ayers, Gregory D; Badea, Cristian T; Fedorov, Andriy Y; Kinahan, Paul E; Holbrook, Matthew; Larson, Peder EZ; Sriram, Renuka; Chenevert, Thomas L; Malyarenko, Dariya; Kurhanewicz, John; Houghton, A McGarry; Ross, Brian D; Pickup, Stephen; Gee, James C; Zhou, Rong; Gammon, Seth T; Manning, Henry Charles; Roudi, Raheleh; Daldrup-Link, Heike E; Lewis, Michael T; Rubin, Daniel L; Yankeelov, Thomas E; Shoghi, Kooresh IPreclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.Item Open Access Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.(Tomography (Ann Arbor, Mich.), 2020-09) Shoghi, Kooresh I; Badea, Cristian T; Blocker, Stephanie J; Chenevert, Thomas L; Laforest, Richard; Lewis, Michael T; Luker, Gary D; Manning, H Charles; Marcus, Daniel S; Mowery, Yvonne M; Pickup, Stephen; Richmond, Ann; Ross, Brian D; Vilgelm, Anna E; Yankeelov, Thomas E; Zhou, RongThe National Institutes of Health's (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.Item Open Access Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.(Medical physics, 2020-09) Clark, Darin P; Schwartz, Fides R; Marin, Daniele; Ramirez-Giraldo, Juan C; Badea, Cristian TPurpose
Data completion is commonly employed 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. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging.Methods
To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance.Results
Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation.Conclusions
Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.Item Open Access Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning(Tomography) Holbrook, Matthew D; Clark, Darin P; Patel, Rutulkumar; Qi, Yi; Bassil, Alex M; Mowery, Yvonne M; Badea, Cristian TWe are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-computed tomography (micro-CT) of mice to detect lung metastasis after treatment. This work explores deep learning (DL) as a fast approach for automated lung nodule detection. We used data from control mice both with and without primary lung tumors. To augment the number of training sets, we have simulated data using real augmented tumors inserted into micro-CT scans. We employed a convolutional neural network (CNN), trained with four competing types of training data: (1) simulated only, (2) real only, (3) simulated and real, and (4) pretraining on simulated followed with real data. We evaluated our model performance using precision and recall curves, as well as receiver operating curves (ROC) and their area under the curve (AUC). The AUC appears to be almost identical (0.76–0.77) for all four cases. However, the combination of real and synthetic data was shown to improve precision by 8%. Smaller tumors have lower rates of detection than larger ones, with networks trained on real data showing better performance. Our work suggests that DL is a promising approach for fast and relatively accurate detection of lung tumors in mice.Item Open Access Dual-energy micro-CT functional imaging of primary lung cancer in mice using gold and iodine nanoparticle contrast agents: a validation study.(PLoS One, 2014) Ashton, Jeffrey R; Clark, Darin P; Moding, Everett J; Ghaghada, Ketan; Kirsch, David G; West, Jennifer L; Badea, Cristian TPURPOSE: To provide additional functional information for tumor characterization, we investigated the use of dual-energy computed tomography for imaging murine lung tumors. Tumor blood volume and vascular permeability were quantified using gold and iodine nanoparticles. This approach was compared with a single contrast agent/single-energy CT method. Ex vivo validation studies were performed to demonstrate the accuracy of in vivo contrast agent quantification by CT. METHODS: Primary lung tumors were generated in LSL-Kras(G12D); p53(FL/FL) mice. Gold nanoparticles were injected, followed by iodine nanoparticles two days later. The gold accumulated in tumors, while the iodine provided intravascular contrast. Three dual-energy CT scans were performed-two for the single contrast agent method and one for the dual contrast agent method. Gold and iodine concentrations in each scan were calculated using a dual-energy decomposition. For each method, the tumor fractional blood volume was calculated based on iodine concentration, and tumor vascular permeability was estimated based on accumulated gold concentration. For validation, the CT-derived measurements were compared with histology and inductively-coupled plasma optical emission spectroscopy measurements of gold concentrations in tissues. RESULTS: Dual-energy CT enabled in vivo separation of gold and iodine contrast agents and showed uptake of gold nanoparticles in the spleen, liver, and tumors. The tumor fractional blood volume measurements determined from the two imaging methods were in agreement, and a high correlation (R(2) = 0.81) was found between measured fractional blood volume and histology-derived microvascular density. Vascular permeability measurements obtained from the two imaging methods agreed well with ex vivo measurements. CONCLUSIONS: Dual-energy CT using two types of nanoparticles is equivalent to the single nanoparticle method, but allows for measurement of fractional blood volume and permeability with a single scan. As confirmed by ex vivo methods, CT-derived nanoparticle concentrations are accurate. This method could play an important role in lung tumor characterization by CT.Item Open Access Enhancing in vivo preclinical studies with VivoVist™ and photon-counting micro-CT imaging(Medical Imaging 2024: Clinical and Biomedical Imaging, 2024-04-02) Badea, Cristian T; Rickard, Ashlyn; Allphin, Alex; Clark, Darin P; Ghaghada, Ketan B; Ridwan, S; Smilowitz, Henry M; Hainfeld, James; Mowery, Yvonne MItem Open Access Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.(European journal of radiology, 2021-06) Schwartz, Fides R; Clark, Darin P; Ding, Yuqin; Ramirez-Giraldo, Juan Carlos; Badea, Cristian T; Marin, DanielePurpose
Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from both tubes to evaluate renal lesions.Method
A DE extrapolation deep-learning (DEEDL) algorithm had been trained on DECT data of 50 patients using a DSCT with DE-FoV = 33 cm (Somatom Flash). Data from 128 patients with known renal lesions falling within DE-FoV was retrospectively collected (100/140 kVp; reference dataset 1). A smaller DE-FoV = 20 cm was simulated excluding the renal lesion of interest (dataset 2) and the DEEDL was applied to this dataset. Output from the DEEDL algorithm was evaluated using ReconCT v14.1 and Syngo.via. Mean attenuation values in lesions on mixed images (HU) were compared calculating the root-mean-squared-error (RMSE) between the datasets using MATLAB R2019a.Results
The DEEDL algorithm performed well reproducing the image data of the kidney lesions (Bosniak 1 and 2: 125, Bosniak 2F: 6, Bosniak 3: 1 and Bosniak 4/(partially) solid: 32) with RSME values of 10.59 HU, 15.7 HU for attenuation, virtual non-contrast, respectively. The measurements performed in dataset 1 and 2 showed strong correlation with linear regression (r2: attenuation = 0.89, VNC = 0.63, iodine = 0.75), lesions were classified as enhancing with an accuracy of 0.91.Conclusion
This DEEDL algorithm can be used to reconstruct a full dual-energy FoV from restricted data, enabling reliable HU value measurements in areas not covered by the smaller FoV and evaluation of renal lesions.Item Open Access Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients.(European radiology, 2023-04) Schwartz, Fides R; Clark, Darin P; Rigiroli, Francesca; Kalisz, Kevin; Wildman-Tobriner, Benjamin; Thomas, Sarah; Wilson, Joshua; Badea, Cristian T; Marin, DanieleObjectives
Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.Methods
Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.Results
Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.Conclusions
The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.Clinical relevance statement
Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.Key points
• Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.Item Open Access Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas.(Radiology. Imaging cancer, 2021-05) Blocker, Stephanie J; Cook, James; Mowery, Yvonne M; Everitt, Jeffrey I; Qi, Yi; Hornburg, Kathryn J; Cofer, Gary P; Zapata, Fernando; Bassil, Alex M; Badea, Cristian T; Kirsch, David G; Johnson, G AllanPurpose To establish a platform for quantitative tissue-based interpretation of cytoarchitecture features from tumor MRI measurements. Materials and Methods In a pilot preclinical study, multicontrast in vivo MRI of murine soft-tissue sarcomas in 10 mice, followed by ex vivo MRI of fixed tissues (termed MR histology), was performed. Paraffin-embedded limb cross-sections were stained with hematoxylin-eosin, digitized, and registered with MRI. Registration was assessed by using binarized tumor maps and Dice similarity coefficients (DSCs). Quantitative cytometric feature maps from histologic slides were derived by using nuclear segmentation and compared with registered MRI, including apparent diffusion coefficients and transverse relaxation times as affected by magnetic field heterogeneity (T2* maps). Cytometric features were compared with each MR image individually by using simple linear regression analysis to identify the features of interest, and the goodness of fit was assessed on the basis of R2 values. Results Registration of MR images to histopathologic slide images resulted in mean DSCs of 0.912 for ex vivo MR histology and 0.881 for in vivo MRI. Triplicate repeats showed high registration repeatability (mean DSC, >0.9). Whole-slide nuclear segmentations were automated to detect nuclei on histopathologic slides (DSC = 0.8), and feature maps were generated for correlative analysis with MR images. Notable trends were observed between cell density and in vivo apparent diffusion coefficients (best line fit: R2 = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R2 = 0.99, P < .001) and variance in nuclear circularity (best line fit: R2 = 0.98, P < .001). Conclusion An infrastructure for registering and quantitatively comparing in vivo tumor MRI with traditional histologic analysis was successfully implemented in a preclinical pilot study of soft-tissue sarcomas. Keywords: MRI, Pathology, Animal Studies, Tissue Characterization Supplemental material is available for this article. © RSNA, 2021.