Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease

Alzheimer's disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN's selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.

Leveraging 3D Bioprinting and Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs

3D bioprinting is revolutionizing the fields of personalized and precision medicine by enabling the manufacturing of bioartificial implants that recapitulate the structural and functional characteristics of native tissues. However, the lack of quantitative and noninvasive techniques to longitudinally track the function of implants has hampered clinical applications of bioprinted scaffolds. In this study, multimaterial 3D bioprinting, engineered nanoparticles (NPs), and spectral photon-counting computed tomography (PCCT) technologies are integrated for the aim of developing a new precision medicine approach to custom-engineer scaffolds with traceability. Multiple CT-visible hydrogel-based bioinks, containing distinct molecular (iodine and gadolinium) and NP (iodine-loaded liposome, gold, methacrylated gold (AuMA), and Gd2 O3 ) contrast agents, are used to bioprint scaffolds with varying geometries at adequate fidelity levels. In vitro release studies, together with printing fidelity, mechanical, and biocompatibility tests identified AuMA and Gd2 O3 NPs as optimal reagents to track bioprinted constructs. Spectral PCCT imaging of scaffolds in vitro and subcutaneous implants in mice enabled noninvasive material discrimination and contrast agent quantification. Together, these results establish a novel theranostic platform with high precision, tunability, throughput, and reproducibility and open new prospects for a broad range of applications in the field of precision and personalized regenerative medicine.

Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models

BACKGROUND

Cardiovascular disease (CVD) is associated with the apolipoprotein E (APOE) gene and lipid metabolism. This study aimed to develop an imaging-based pipeline to comprehensively assess cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT).

METHODS

123 mice grouped based on APOE genotype (APOE2, APOE3, APOE4, APOE knockout (KO)), gender, human NOS2 factor, and diet (control or high fat) were used in this study. The pipeline included PCCT imaging on a custom-built system with contrast-enhanced in vivo imaging and intrinsic cardiac gating, spectral and temporal iterative reconstruction, spectral decomposition, and deep learning cardiac segmentation. Statistical analysis evaluated genotype, diet, sex, and body weight effects on cardiac measurements.

RESULTS

Our results showed that PCCT offered high quality imaging with reduced noise. Material decomposition enabled separation of calcified plaques from iodine enhanced blood in APOE KO mice. Deep learning-based segmentation showed good performance with Dice scores of 0.91 for CT-based segmentation and 0.89 for iodine map-based segmentation. Genotype-specific differences were observed in left ventricular volumes, heart rate, stroke volume, ejection fraction, and cardiac index. Statistically significant differences were found between control and high fat diets for APOE2 and APOE4 genotypes in heart rate and stroke volume. Sex and weight were also significant predictors of cardiac measurements. The inclusion of the human NOS2 gene modulated these effects.

CONCLUSIONS

This study demonstrates the potential of PCCT in assessing cardiac structure and function in mouse models of CVD which can help in understanding the interplay between genetic factors, diet, and cardiovascular health.

Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

OBJECTIVES

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.

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data

BACKGROUND

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains.

PURPOSE

We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public).

METHODS

The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times.

RESULTS

Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems.

CONCLUSIONS

The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images

Photon-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.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging

Preclinical 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.

An Online Repository for Pre-Clinical Imaging Protocols (PIPs)

Providing 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.

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

The 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.

Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma

This study aims 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). We generated a high mutational load primary mouse model of STS by intramuscular injection of adenovirus expressing Cas9 and guide RNA targeting Trp53 and intramuscular injection of 3-methylcholanthrene (MCA) into the gastrocnemius muscle of wild-type mice (p53/MCA model). We randomized tumor-bearing mice to receive isotype control or anti-PD-1 antibody with or without radiotherapy (20 Gy), followed by hind limb amputation. We used micro-CT to detect lung metastases with high spatial resolution, which was confirmed by histology. We investigated whether sarcoma metastasis was regulated by immunosurveillance by lymphocytes or tumor cell-intrinsic mechanisms. Compared with surgery with isotype control antibody, the combination of anti-PD-1, radiotherapy, and surgery improved local recurrence-free survival (P = 0.035) and disease-free survival (P = 0.005), but not metastasis-free survival. Mice treated with radiotherapy, but not anti-PD-1, showed significantly improved local recurrence-free survival and metastasis-free survival over surgery alone (P = 0.043 and P = 0.007, respectively). The overall metastasis rate was low (∼12%) in the p53/MCA sarcoma model, which limited the power to detect further improvement in metastasis-free survival with addition of anti-PD-1 therapy. Tail vein injections of sarcoma cells into immunocompetent mice suggested that impaired metastasis was due to inability of sarcoma cells to grow in the lungs rather than a consequence of immunosurveillance. In conclusion, neoadjuvant radiotherapy improves metastasis-free survival after surgery in a primary model of STS.

Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images

Magnetic 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.

Material decomposition from photon-counting CT using a convolutional neural network and energy-integrating CT training labels

Objective.Photon-counting CT (PCCT) has better dose efficiency and spectral resolution than energy-integrating CT, which is advantageous for material decomposition. Unfortunately, the accuracy of PCCT-based material decomposition is limited due to spectral distortions in the photon-counting detector (PCD).Approach.In this work, we demonstrate a deep learning (DL) approach that compensates for spectral distortions in the PCD and improves accuracy in material decomposition by using decomposition maps provided by high-dose multi-energy-integrating detector (EID) data as training labels. We use a 3D U-net architecture and compare networks with PCD filtered back projection (FBP) reconstruction (FBP2Decomp), PCD iterative reconstruction (Iter2Decomp), and PCD decomposition (Decomp2Decomp) as the input.Main results.We found that our Iter2Decomp approach performs best, but DL outperforms matrix inversion decomposition regardless of the input. Compared to PCD matrix inversion decomposition, Iter2Decomp gives 27.50% lower root mean squared error (RMSE) in the iodine (I) map and 59.87% lower RMSE in the photoelectric effect (PE) map. In addition, it increases the structural similarity (SSIM) by 1.92%, 6.05%, and 9.33% in the I, Compton scattering (CS), and PE maps, respectively. When taking measurements from iodine and calcium vials, Iter2Decomp provides excellent agreement with multi-EID decomposition. One limitation is some blurring caused by our DL approach, with a decrease from 1.98 line pairs/mm at 50% modulation transfer function (MTF) with PCD matrix inversion decomposition to 1.75 line pairs/mm at 50% MTF when using Iter2Decomp.Significance.Overall, this work demonstrates that our DL approach with high-dose multi-EID derived decomposition labels is effective at generating more accurate material maps from PCD data. More accurate preclinical spectral PCCT imaging such as this could serve for developing nanoparticles that show promise in the field of theranostics (therapy and diagnostics).

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden

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.

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

The 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.

Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas

Purpose 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.

Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study

PURPOSE

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.

Dual source hybrid spectral micro-CT using an energy-integrating and a photon-counting detector

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 p53^fl/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.

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography

PURPOSE

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.

Vascular Endothelial Growth Factor Receptor 3 Regulates Endothelial Function Through β-Arrestin 1

BACKGROUND

Receptor signaling is central to vascular endothelial function and is dysregulated in vascular diseases such as atherosclerosis and pulmonary arterial hypertension (PAH). Signaling pathways involved in endothelial function include vascular endothelial growth factor receptors (VEGFRs) and G protein-coupled receptors, which classically activate distinct intracellular signaling pathways and responses. The mechanisms that regulate these signaling pathways have not been fully elucidated and it is unclear what nodes for cross talk exist between these diverse signaling pathways. For example, multifunctional β-arrestin (ARRB) adapter proteins are best known as regulators of G protein-coupled receptor signaling, but their role at other receptors and their physiological importance in the setting of vascular disease are unclear.

METHODS

We used a combination of human samples from PAH, human microvascular endothelial cells from lung, and Arrb knockout mice to determine the role of ARRB1 in endothelial VEGFR3 signaling. In addition, a number of biochemical analyses were performed to determine the interaction between ARRB1 and VEGFR3, signaling mediators downstream of VEGFR3, and the internalization of VEGFR3.

RESULTS

Expression of ARRB1 and VEGFR3 was reduced in human PAH, and the deletion of Arrb1 in mice exposed to hypoxia led to worse PAH with a loss of VEGFR3 signaling. Knockdown of ARRB1 inhibited VEGF-C-induced endothelial cell proliferation, migration, and tube formation, along with reduced VEGFR3, Akt, and endothelial nitric oxide synthase phosphorylation. This regulation was mediated by direct ARRB1 binding to the VEGFR3 kinase domain and resulted in decreased VEGFR3 internalization.

CONCLUSIONS

Our results demonstrate a novel role for ARRB1 in VEGFR regulation and suggest a mechanism for cross talk between G protein-coupled receptors and VEGFRs in PAH. These findings also suggest that strategies to promote ARRB1-mediated VEGFR3 signaling could be useful in the treatment of pulmonary hypertension and other vascular disease.

Sensitization of Vascular Endothelial Cells to Ionizing Radiation Promotes the Development of Delayed Intestinal Injury in Mice

Exposure of the gastrointestinal (GI) tract to ionizing radiation can cause acute and delayed injury. However, critical cellular targets that regulate the development of radiation-induced GI injury remain incompletely understood. Here, we investigated the role of vascular endothelial cells in controlling acute and delayed GI injury after total-abdominal irradiation (TAI). To address this, we used genetically engineered mice in which endothelial cells are sensitized to radiation due to the deletion of the tumor suppressor p53. Remarkably, we found that VE-cadherin-Cre; p53^FL/FL mice, in which both alleles of p53 are deleted in endothelial cells, were not sensitized to the acute GI radiation syndrome, but these mice were highly susceptible to delayed radiation enteropathy. Histological examination indicated that VE-cadherin-Cre; p53^FL/FL mice that developed delayed radiation enteropathy had severe vascular injury in the small intestine, which was manifested by hemorrhage, loss of microvessels and tissue hypoxia. In addition, using dual-energy CT imaging, we showed that VE-cadherin-Cre; p53^FL/FL mice had a significant increase in vascular permeability of the small intestine in vivo 28 days after TAI. Together, these findings demonstrate that while sensitization of endothelial cells to radiation does not exacerbate the acute GI radiation syndrome, it is sufficient to promote the development of late radiation enteropathy.

Functional imaging of tumor vasculature using iodine and gadolinium-based nanoparticle contrast agents: a comparison of spectral micro-CT using energy integrating and photon counting detectors

Advances in computed tomography (CT) hardware have propelled the development of novel CT contrast agents. In particular, the spectral capabilities of x-ray CT can facilitate simultaneous imaging of multiple contrast agents. This approach is particularly useful for functional imaging of solid tumors by simultaneous visualization of multiple targets or architectural features that govern cancer development and progression. Nanoparticles are a promising platform for contrast agent development. While several novel imaging moieties based on high atomic number elements are being explored, iodine (I) and gadolinium (Gd) are particularly attractive because of their existing approval for clinical use. In this work, we investigate the in vivo discrimination of I and Gd nanoparticle contrast agents using both dual energy micro-CT with energy integrating detectors (DE-EID) and photon counting detector (PCD)-based spectral micro-CT. Simulations and phantom experiments were performed using varying concentrations of I and Gd to determine the imaging performance with optimized acquisition parameters. Quantitative spectral micro-CT imaging using liposomal-iodine (Lip-I) and liposomal-Gd (Lip-Gd) nanoparticle contrast agents was performed in sarcoma bearing mice for anatomical and functional imaging of tumor vasculature. Iterative reconstruction provided high sensitivity to detect and discriminate relatively low I and Gd concentrations. According to the Rose criterion applied to the experimental results, the detectability limits for I and Gd were approximately 2.5 mg ml^-1 for both DE-EID CT and PCD micro-CT, even if the radiation dose was approximately 3.8 times lower with PCD micro-CT. The material concentration maps confirmed expected biodistributions of contrast agents in the blood, liver, spleen and kidneys. The PCD provided lower background signal and better simultaneous visualization of tumor vasculature and intratumoral distribution patterns of nanoparticle contrast agent compared to DE-EID decompositions. Preclinical spectral CT systems such as this could be useful for functional characterization of solid tumors, simultaneous quantitative imaging of multiple targets and for identifying clinically-relevant applications that benefit from the use of spectral imaging. Additionally, it could aid in the development nanoparticles that show promise in the developing field of cancer theranostics (therapy and diagnostics) by measuring vascular tumor biomarkers such as fractional blood volume and the delivery of liposomal chemotherapeutics.

A comparative analysis of EGFR-targeting antibodies for gold nanoparticle CT imaging of lung cancer

Computed 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.

Overcoming detector limitations of x-ray photon counting for preclinical microcomputed tomography

Spectral computed tomography (CT) using photon counting detectors (PCDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCDs are especially suited for K-edge imaging, revealing the spatial distribution of select imaging probes through quantitative material decomposition. We report on a prototype spectral micro-CT system with a CZT-based PCD (DxRay, Inc.) that has 16 × 16    pixels of 0.5 × 0.5    mm 2 , a thickness of 3 mm, and four energy thresholds. Due to the PCD's limited size ( 8 × 8    mm 2 ), our system uses a translate-rotate projection acquisition strategy to cover a field of view relevant for preclinical imaging ( ∼ 4.5    cm ). Projection corrections were implemented to minimize artifacts associated with dead pixels and projection stitching. A sophisticated iterative algorithm was used to reconstruct both phantom and ex vivo mouse data. To achieve preclinically relevant spatial resolution, we trained a convolutional neural network to perform pan-sharpening between low-resolution PCD data ( 247 - μ m voxels) and high-resolution energy-integrating detector data ( 82 - μ m voxels), recovering a high-resolution estimate of the spectral contrast suitable for material decomposition. Long-term, preclinical spectral CT systems such as ours could serve in the developing field of theranostics (therapy and diagnostics) for cancer research.

Dual-Energy CT Imaging of Tumor Liposome Delivery After Gold Nanoparticle-Augmented Radiation Therapy

Gold nanoparticles (AuNPs) are emerging as promising agents for both cancer therapy and computed tomography (CT) imaging. AuNPs absorb x-rays and subsequently release low-energy, short-range photoelectrons during external beam radiation therapy (RT), increasing the local radiation dose. When AuNPs are near tumor vasculature, the additional radiation dose can lead to increased vascular permeability. This work focuses on understanding how tumor vascular permeability is influenced by AuNP-augmented RT, and how this effect can be used to improve the delivery of nanoparticle chemotherapeutics.

Methods: Dual-energy CT was used to quantify the accumulation of both liposomal iodine and AuNPs in tumors following AuNP-augmented RT in a mouse model of primary soft tissue sarcoma. Mice were injected with non-targeted AuNPs, RGD-functionalized AuNPs (vascular targeting), or no AuNPs, after which they were treated with varying doses of RT. The mice were injected with either liposomal iodine (for the imaging study) or liposomal doxorubicin (for the treatment study) 24 hours after RT. Increased tumor liposome accumulation was assessed by dual-energy CT (iodine) or by tracking tumor treatment response (doxorubicin).

Results: A significant increase in vascular permeability was observed for all groups after 20 Gy RT, for the targeted and non-targeted AuNP groups after 10 Gy RT, and for the vascular-targeted AuNP group after 5 Gy RT. Combining targeted AuNPs with 5 Gy RT and liposomal doxorubicin led to a significant tumor growth delay (tumor doubling time ~ 8 days) compared to AuNP-augmented RT or chemotherapy alone (tumor doubling time ~3-4 days).

Conclusions: The addition of vascular-targeted AuNPs significantly improved the treatment effect of liposomal doxorubicin after RT, consistent with the increased liposome accumulation observed in tumors in the imaging study. Using this approach with a liposomal drug delivery system can increase specific tumor delivery of chemotherapeutics, which has the potential to significantly improve tumor response and reduce the side effects of both RT and chemotherapy.

Low-dose 4D cardiac imaging in small animals using dual source micro-CT

Micro-CT is widely used in preclinical studies, generating substantial interest in extending its capabilities in functional imaging applications such as blood perfusion and cardiac function. However, imaging cardiac structure and function in mice is challenging due to their small size and rapid heart rate. To overcome these challenges, we propose and compare improvements on two strategies for cardiac gating in dual-source, preclinical micro-CT: fast prospective gating (PG) and uncorrelated retrospective gating (RG). These sampling strategies combined with a sophisticated iterative image reconstruction algorithm provide faster acquisitions and high image quality in low-dose 4D (i.e. 3D  +  Time) cardiac micro-CT. Fast PG is performed under continuous subject rotation which results in interleaved projection angles between cardiac phases. Thus, fast PG provides a well-sampled temporal average image for use as a prior in iterative reconstruction. Uncorrelated RG incorporates random delays during sampling to prevent correlations between heart rate and sampling rate. We have performed both simulations and animal studies to validate these new sampling protocols. Sampling times for 1000 projections using fast PG and RG were 2 and 3 min, respectively, and the total dose was 170 mGy each. Reconstructions were performed using a 4D iterative reconstruction technique based on the split Bregman method. To examine undersampling robustness, subsets of 500 and 250 projections were also used for reconstruction. Both sampling strategies in conjunction with our iterative reconstruction method are capable of resolving cardiac phases and provide high image quality. In general, for equal numbers of projections, fast PG shows fewer errors than RG and is more robust to undersampling. Our results indicate that only 1000-projection based reconstruction with fast PG satisfies a 5% error criterion in left ventricular volume estimation. These methods promise low-dose imaging with a wide range of preclinical applications in cardiac imaging.

Hybrid spectral CT reconstruction

Current photon counting x-ray detector (PCD) technology faces limitations associated with spectral fidelity and photon starvation. One strategy for addressing these limitations is to supplement PCD data with high-resolution, low-noise data acquired with an energy-integrating detector (EID). In this work, we propose an iterative, hybrid reconstruction technique which combines the spectral properties of PCD data with the resolution and signal-to-noise characteristics of EID data. Our hybrid reconstruction technique is based on an algebraic model of data fidelity which substitutes the EID data into the data fidelity term associated with the PCD reconstruction, resulting in a joint reconstruction problem. Within the split Bregman framework, these data fidelity constraints are minimized subject to additional constraints on spectral rank and on joint intensity-gradient sparsity measured between the reconstructions of the EID and PCD data. Following a derivation of the proposed technique, we apply it to the reconstruction of a digital phantom which contains realistic concentrations of iodine, barium, and calcium encountered in small-animal micro-CT. The results of this experiment suggest reliable separation and detection of iodine at concentrations ≥ 5 mg/ml and barium at concentrations ≥ 10 mg/ml in 2-mm features for EID and PCD data reconstructed with inherent spatial resolutions of 176 μm and 254 μm, respectively (point spread function, FWHM). Furthermore, hybrid reconstruction is demonstrated to enhance spatial resolution within material decomposition results and to improve low-contrast detectability by as much as 2.6 times relative to reconstruction with PCD data only. The parameters of the simulation experiment are based on an in vivo micro-CT experiment conducted in a mouse model of soft-tissue sarcoma. Material decomposition results produced from this in vivo data demonstrate the feasibility of distinguishing two K-edge contrast agents with a spectral separation on the order of the energy resolution of the PCD hardware.

Spectrotemporal CT data acquisition and reconstruction at low dose

PURPOSE

X-ray computed tomography (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. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D+dual energy+time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols.

METHODS

The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer.

RESULTS

Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol.

CONCLUSIONS

Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time.

A neural network-based method for spectral distortion correction in photon counting x-ray CT

Spectral 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.

In vivo small animal micro-CT using nanoparticle contrast agents

Computed tomography (CT) is one of the most valuable modalities for in vivo imaging because it is fast, high-resolution, cost-effective, and non-invasive. Moreover, CT is heavily used not only in the clinic (for both diagnostics and treatment planning) but also in preclinical research as micro-CT. Although CT is inherently effective for lung and bone imaging, soft tissue imaging requires the use of contrast agents. For small animal micro-CT, nanoparticle contrast agents are used in order to avoid rapid renal clearance. A variety of nanoparticles have been used for micro-CT imaging, but the majority of research has focused on the use of iodine-containing nanoparticles and gold nanoparticles. Both nanoparticle types can act as highly effective blood pool contrast agents or can be targeted using a wide variety of targeting mechanisms. CT imaging can be further enhanced by adding spectral capabilities to separate multiple co-injected nanoparticles in vivo. Spectral CT, using both energy-integrating and energy-resolving detectors, has been used with multiple contrast agents to enable functional and molecular imaging. This review focuses on new developments for in vivo small animal micro-CT using novel nanoparticle probes applied in preclinical research.

A Plasmonic Gold Nanostar Theranostic Probe for In Vivo Tumor Imaging and Photothermal Therapy

Nanomedicine 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.

Anatomical and functional imaging of myocardial infarction in mice using micro-CT and eXIA 160 contrast agent

Noninvasive small animal imaging techniques are essential for evaluation of cardiac disease and potential therapeutics. A novel preclinical iodinated contrast agent called eXIA 160 has recently been developed, which has been evaluated for micro-CT cardiac imaging. eXIA 160 creates strong contrast between blood and tissue immediately after its injection and is subsequently taken up by the myocardium and other metabolically active tissues over time. We focus on these properties of eXIA and show its use in imaging myocardial infarction in mice. Five C57BL/6 mice were imaged ~2 weeks after left anterior descending coronary artery ligation. Six C57BL/6 mice were used as controls. Immediately after injection of eXIA 160, an enhancement difference between blood and myocardium of ~340 HU enabled cardiac function estimation via 4D micro-CT scanning with retrospective gating. Four hours post-injection, the healthy perfused myocardium had a contrast difference of ~140 HU relative to blood while the infarcted myocardium showed no enhancement. These differences allowed quantification of infarct size via dual-energy micro-CT. In vivo micro-SPECT imaging and ex vivo triphenyl tetrazolium chloride (TTC) staining provided validation for the micro-CT findings. Root mean squared error of infarct measurements was 2.7% between micro-CT and SPECT, and 4.7% between micro-CT and TTC. Thus, micro-CT with eXIA 160 can be used to provide both morphological and functional data for preclinical studies evaluating myocardial infarction and potential therapies. Further studies are warranted to study the potential use of eXIA 160 as a CT molecular imaging tool for other metabolically active tissues in the mouse.

Spectral diffusion: an algorithm for robust material decomposition of spectral CT data

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piecewise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg mL(-1)), gold (0.9 mg mL(-1)), and gadolinium (2.9 mg mL(-1)) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen.

Micro-CT of rodents: state-of-the-art and future perspectives

Micron-scale computed tomography (micro-CT) is an essential tool for phenotyping and for elucidating diseases and their therapies. This work is focused on preclinical micro-CT imaging, reviewing relevant principles, technologies, and applications. Commonly, micro-CT provides high-resolution anatomic information, either on its own or in conjunction with lower-resolution functional imaging modalities such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). More recently, however, advanced applications of micro-CT produce functional information by translating clinical applications to model systems (e.g., measuring cardiac functional metrics) and by pioneering new ones (e.g. measuring tumor vascular permeability with nanoparticle contrast agents). The primary limitations of micro-CT imaging are the associated radiation dose and relatively poor soft tissue contrast. We review several image reconstruction strategies based on iterative, statistical, and gradient sparsity regularization, demonstrating that high image quality is achievable with low radiation dose given ever more powerful computational resources. We also review two contrast mechanisms under intense development. The first is spectral contrast for quantitative material discrimination in combination with passive or actively targeted nanoparticle contrast agents. The second is phase contrast which measures refraction in biological tissues for improved contrast and potentially reduced radiation dose relative to standard absorption imaging. These technological advancements promise to develop micro-CT into a commonplace, functional and even molecular imaging modality.

Assessing cardiac injury in mice with dual energy-microCT, 4D-microCT, and microSPECT imaging after partial heart irradiation

PURPOSE

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.

Dual-energy micro-CT functional imaging of primary lung cancer in mice using gold and iodine nanoparticle contrast agents: a validation study

Purpose

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 (R² = 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.

Assessing the radiation response of lung cancer with different gene mutations using genetically engineered mice

Purpose: 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.

In vivo characterization of tumor vasculature using iodine and gold nanoparticles and dual energy micro-CT

Tumor blood volume and vascular permeability are well established indicators of tumor angiogenesis and important predictors in cancer diagnosis, planning and treatment. In this work, we establish a novel preclinical imaging protocol which allows quantitative measurement of both metrics simultaneously. First, gold nanoparticles are injected and allowed to extravasate into the tumor, and then liposomal iodine nanoparticles are injected. Combining a previously optimized dual energy micro-CT scan using high-flux polychromatic x-ray sources (energies: 40 kVp, 80 kVp) with a novel post-reconstruction spectral filtration scheme, we are able to decompose the results into 3D iodine and gold maps, allowing simultaneous measurement of extravasated gold and intravascular iodine concentrations. Using a digital resolution phantom, the mean limits of detectability (mean CNR = 5) for each element are determined to be 2.3 mg mL(-1) (18 mM) for iodine and 1.0 mg mL(-1) (5.1 mM) for gold, well within the observed in vivo concentrations of each element (I: 0-24 mg mL(-1), Au: 0-9 mg mL(-1)) and a factor of 10 improvement over the limits without post-reconstruction spectral filtration. Using a calibration phantom, these limits are validated and an optimal sensitivity matrix for performing decomposition using our micro-CT system is derived. Finally, using a primary mouse model of soft-tissue sarcoma, we demonstrate the in vivo application of the protocol to measure fractional blood volume and vascular permeability over the course of five days of active tumor growth.

Data analysis: evaluation of nanoscale contrast agent enhanced CT scan to differentiate between benign and malignant lung cancer in mouse model

Proposed is a method for statistical analysis for a small sample size, repeated measure experiment with nesting factors. In the original experiment the Student t-test was used for analysis. Using the same data, we modeled the experiment into two groups of mice with benign and malignant primary lung tumors. 4 tumor nodules were selected from each mouse (N= 36). The dependent variables are the volume, diameter, and signal attenuation measured using computed tomography (CT). The measurements are made before injecting the contrast and at 0, 72, and 168 hours after injection. The contrast agent enhances tumor nodule volume and volume differences between benign and malignant tumor nodules measured across time (p < 0.05). The signal attenuation measured across time differentiates between benign and malignant groups (p < 0.05). There is significant correlation between rate of change of volume and diameter of tumor. The advantages of this statistical method are discussed.

Temporal and spectral imaging with micro-CT

PURPOSE

Micro-CT is widely used for small animal imaging in preclinical studies of cardiopulmonary disease, but further development is needed to improve spatial resolution, temporal resolution, and material contrast. We present a technique for visualizing the changing distribution of iodine in the cardiac cycle with dual source micro-CT.

METHODS

The approach entails a retrospectively gated dual energy scan with optimized filters and voltages, and a series of computational operations to reconstruct the data. Projection interpolation and five-dimensional bilateral filtration (three spatial dimensions + time + energy) are used to reduce noise and artifacts associated with retrospective gating. We reconstruct separate volumes corresponding to different cardiac phases and apply a linear transformation to decompose these volumes into components representing concentrations of water and iodine. Since the resulting material images are still compromised by noise, we improve their quality in an iterative process that minimizes the discrepancy between the original acquired projections and the projections predicted by the reconstructed volumes. The values in the voxels of each of the reconstructed volumes represent the coefficients of linear combinations of basis functions over time and energy. We have implemented the reconstruction algorithm on a graphics processing unit (GPU) with CUDA. We tested the utility of the technique in simulations and applied the technique in an in vivo scan of a C57BL∕6 mouse injected with blood pool contrast agent at a dose of 0.01 ml∕g body weight. Postreconstruction, at each cardiac phase in the iodine images, we segmented the left ventricle and computed its volume. Using the maximum and minimum volumes in the left ventricle, we calculated the stroke volume, the ejection fraction, and the cardiac output.

RESULTS

Our proposed method produces five-dimensional volumetric images that distinguish different materials at different points in time, and can be used to segment regions containing iodinated blood and compute measures of cardiac function.

CONCLUSIONS

We believe this combined spectral and temporal imaging technique will be useful for future studies of cardiopulmonary disease in small animals.

Registration-based segmentation of murine 4D cardiac micro-CT data using symmetric normalization

Micro-CT can play an important role in preclinical studies of cardiovascular disease because of its high spatial and temporal resolution. Quantitative analysis of 4D cardiac images requires segmentation of the cardiac chambers at each time point, an extremely time consuming process if done manually. To improve throughput this study proposes a pipeline for registration-based segmentation and functional analysis of 4D cardiac micro-CT data in the mouse. Following optimization and validation using simulations, the pipeline was applied to in vivo cardiac micro-CT data corresponding to ten cardiac phases acquired in C57BL/6 mice (n = 5). After edge-preserving smoothing with a novel adaptation of 4D bilateral filtration, one phase within each cardiac sequence was manually segmented. Deformable registration was used to propagate these labels to all other cardiac phases for segmentation. The volumes of each cardiac chamber were calculated and used to derive stroke volume, ejection fraction, cardiac output, and cardiac index. Dice coefficients and volume accuracies were used to compare manual segmentations of two additional phases with their corresponding propagated labels. Both measures were, on average, >0.90 for the left ventricle and >0.80 for the myocardium, the right ventricle, and the right atrium, consistent with trends in inter- and intra-segmenter variability. Segmentation of the left atrium was less reliable. On average, the functional metrics of interest were underestimated by 6.76% or more due to systematic label propagation errors around atrioventricular valves; however, execution of the pipeline was 80% faster than performing analogous manual segmentation of each phase.

4D micro-CT using fast prospective gating

Micro-CT is currently used in preclinical studies to provide anatomical information. But, there is also significant interest in using this technology to obtain functional information. We report here a new sampling strategy for 4D micro-CT for functional cardiac and pulmonary imaging. Rapid scanning of free-breathing mice is achieved with fast prospective gating (FPG) implemented on a field programmable gate array. The method entails on-the-fly computation of delays from the R peaks of the ECG signals or the peaks of the respiratory signals for the triggering pulses. Projection images are acquired for all cardiac or respiratory phases at each angle before rotating to the next angle. FPG can deliver the faster scan time of retrospective gating (RG) with the regular angular distribution of conventional prospective gating for cardiac or respiratory gating. Simultaneous cardio-respiratory gating is also possible with FPG in a hybrid retrospective/prospective approach. We have performed phantom experiments to validate the new sampling protocol and compared the results from FPG and RG in cardiac imaging of a mouse. Additionally, we have evaluated the utility of incorporating respiratory information in 4D cardiac micro-CT studies with FPG. A dual-source micro-CT system was used for image acquisition with pulsed x-ray exposures (80 kVp, 100 mA, 10 ms). The cardiac micro-CT protocol involves the use of a liposomal blood pool contrast agent containing 123 mg I ml(-1) delivered via a tail vein catheter in a dose of 0.01 ml g(-1) body weight. The phantom experiment demonstrates that FPG can distinguish the successive phases of phantom motion with minimal motion blur, and the animal study demonstrates that respiratory FPG can distinguish inspiration and expiration. 4D cardiac micro-CT imaging with FPG provides image quality superior to RG at an isotropic voxel size of 88 μm and 10 ms temporal resolution. The acquisition time for either sampling approach is less than 5 min. The radiation dose associated with the proposed method is in the range of a typical micro-CT dose (256 mGy for the cardiac study). Ignoring respiration does not significantly affect anatomic information in cardiac studies. FPG can deliver short scan times with low-dose 4D micro-CT imaging without sacrificing image quality. FPG can be applied in high-throughput longitudinal studies in a wide range of applications, including drug safety and cardiopulmonary phenotyping.

Computed tomography imaging of primary lung cancer in mice using a liposomal-iodinated contrast agent

Purpose

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.

Methods

Primary lung cancers with mutations in K-ras alone (Kras^LA1) or in combination with p53 (LSL-Kras^G12D;p53^FL/FL) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.

Results

A good correlation was seen between volume and diameter-based assessment of nodules (R²>0.8) for both lung cancer models. The LSL-Kras^G12D;p53^FL/FL model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras^LA1 mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras^G12D;p53^FL/FL mice compared to nodules in Kras^LA1 mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.

Conclusions

The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring.

Lung imaging in rodents using dual energy micro-CT

Dual energy CT imaging is expected to play a major role in the diagnostic arena as it provides material decomposition on an elemental basis. The purpose of this work is to investigate the use of dual energy micro-CT for the estimation of vascular, tissue, and air fractions in rodent lungs using a post-reconstruction three-material decomposition method. We have tested our method using both simulations and experimental work. Using simulations, we have estimated the accuracy limits of the decomposition for realistic micro-CT noise levels. Next, we performed experiments involving ex vivo lung imaging in which intact lungs were carefully removed from the thorax, were injected with an iodine-based contrast agent and inflated with air at different volume levels. Finally, we performed in vivo imaging studies in (n=5) C57BL/6 mice using fast prospective respiratory gating in end-inspiration and end-expiration for three different levels of positive end-expiratory pressure (PEEP). Prior to imaging, mice were injected with a liposomal blood pool contrast agent. The mean accuracy values were for Air (95.5%), Blood (96%), and Tissue (92.4%). The absolute accuracy in determining all fraction materials was 94.6%. The minimum difference that we could detect in material fractions was 15%. As expected, an increase in PEEP levels for the living mouse resulted in statistically significant increases in air fractions at end-expiration, but no significant changes in end-inspiration. Our method has applicability in preclinical pulmonary studies where various physiological changes can occur as a result of genetic changes, lung disease, or drug effects.

Dual-energy micro-CT of the rodent lung

The purpose of this work is to investigate the use of dual-energy micro-computed tomography (CT) for the estimation of vascular, tissue, and air fractions in rodent lungs using a postreconstruction three material decomposition method. Using simulations, we have estimated the accuracy limits of the decomposition for realistic micro-CT noise levels. Next, we performed experiments involving ex vivo lung imaging in which intact rat lungs were carefully removed from the thorax, injected with an iodine-based contrast agent, and then inflated with different volumes of air (n = 2). Finally, we performed in vivo imaging studies in C57BL/6 mice (n = 5) using fast prospective respiratory gating in end inspiration and end expiration for three different levels of positive end expiratory pressure (PEEP). Before imaging, mice were injected with a liposomal blood pool contrast agent. The three-dimensional air, tissue, and blood fraction maps were computed and analyzed. The results indicate that separation and volume estimation of the three material components of the lungs are possible. The mean accuracy values for air, blood, and tissue were 93, 93, and 90%, respectively. The absolute accuracy in determining all fraction materials was 91.6%. The coefficient of variation was small (2.5%) indicating good repeatability. The minimum difference that we could detect in material fractions was 15%. As expected, an increase in PEEP levels for the living mouse resulted in statistically significant increases in air fractions at end expiration but no significant changes at end inspiration. Our method has applicability in preclinical pulmonary studies where changes in lung structure and gas volume as a result of lung injury, environmental exposures, or drug bioactivity would have important physiological implications.

Investigations on X-ray luminescence CT for small animal imaging

X-ray Luminescence CT (XLCT) is a hybrid imaging modality combining x-ray and optical imaging in which x-ray luminescent nanophosphors (NPs) are used as emissive imaging probes. NPs are easily excited using common CT energy x-ray beams, and the NP luminescence is efficiently collected using sensitive light based detection systems. XLCT can be recognized as a close analog to fluorescence diffuse optical tomography (FDOT). However, XLCT has remarkable advantages over FDOT due to the substantial excitation penetration depths provided by x-rays relative to laser light sources, long term photo-stability of NPs, and the ability to tune NP emission within the NIR spectral window. Since XCLT uses an x-ray pencil beam excitation, the emitted light can be measured and back-projected along the x-ray path during reconstruction, where the size of the X-ray pencil beam determines the resolution for XLCT. In addition, no background signal competes with NP luminescence (i.e., no auto fluorescence) in XLCT. Currently, no small animal XLCT system has been proposed or tested. This paper investigates an XLCT system built and integrated with a dual source micro-CT system. Two novel sampling paradigms that result in more efficient scanning are proposed and tested via simulations. Our preliminary experimental results in phantoms indicate that a basic CT-like reconstruction is able to recover a map of the NP locations and differences in NP concentrations. With the proposed dual source system and faster scanning approaches, XLCT has the potential to revolutionize molecular imaging in preclinical studies.

Denoising of 4D Cardiac Micro-CT Data Using Median-Centric Bilateral Filtration

Bilateral filtration has proven an effective tool for denoising CT data. The classic filter utilizes Gaussian domain and range weighting functions in 2D. More recently, other distributions have yielded more accurate results in specific applications, and the bilateral filtration framework has been extended to higher dimensions. In this study, brute-force optimization is employed to evaluate the use of several alternative distributions for both domain and range weighting: Andrew's Sine Wave, El Fallah Ford, Gaussian, Flat, Lorentzian, Huber's Minimax, Tukey's Bi-weight, and Cosine. Two variations on the classic bilateral filter which use median filtration to reduce bias in range weights are also investigated: median-centric and hybrid bilateral filtration. Using the 4D MOBY mouse phantom reconstructed with noise (stdev. ~ 65 HU), hybrid bilateral filtration, a combination of the classic and median-centric filters, with Flat domain and range weighting is shown to provide optimal denoising results (PSNRs: 31.69, classic; 31.58 median-centric; 32.25, hybrid). To validate these phantom studies, the optimal filters are also applied to in vivo, 4D cardiac micro-CT data acquired in the mouse. In a constant region of the left ventricle, hybrid bilateral filtration with Flat domain and range weighting is shown to provide optimal smoothing (stdev: original, 72.2 HU; classic, 20.3 HU; median-centric, 24.1 HU; hybrid, 15.9 HU). While the optimal results were obtained using 4D filtration, the 3D hybrid filter is ultimately recommended for denoising 4D cardiac micro-CT data because it is more computationally tractable and less prone to artifacts (MOBY PSNR: 32.05; left ventricle stdev: 20.5 HU).

In vivo imaging of rat coronary arteries using bi-plane digital subtraction angiography

INTRODUCTION

X-ray based digital subtraction angiography (DSA) is a common clinical imaging method for vascular morphology and function. Coronary artery characterization is one of its most important applications. We show that bi-plane DSA of rat coronary arteries can provide a powerful imaging tool for translational safety assessment in drug discovery.

METHODS

A novel, dual tube/detector system, constructed explicitly for preclinical imaging, supports image acquisition at 10 frames/s with 88-micron spatial resolution. Ventilation, x-ray exposure, and contrast injection are all precisely synchronized using a biological sequence controller implemented as a LabVIEW application. A set of experiments were performed to test and optimize the sampling and image quality. We applied the DSA imaging protocol to record changes in the visualization of coronaries and myocardial perfusion induced by a vasodilator drug, nitroprusside. The drug was infused into a tail vein catheter using a peristaltic infusion pump at a rate of 0.07 mL/h for 3 min (dose: 0.0875 mg). Multiple DSA sequences were acquired before, during, and up to 25 min after drug infusion. Perfusion maps of the heart were generated in MATLAB to compare the drug effects over time.

RESULTS

The best trade-off between the injection time, pressure, and image quality was achieved at 60 PSI, with the injection of 150 ms occurring early in diastole (60 ms delay) and resulting in the delivery of 113 μL of contrast agent. DSA images clearly show the main branches of the coronary arteries in an intact, beating heart. The drug test demonstrated that DSA can detect relative changes in coronary circulation via perfusion maps.

CONCLUSIONS

The methodology for DSA imaging of rat coronary arteries can serve as a template for future translational studies to assist in safety evaluation of new pharmaceuticals. Although x-ray imaging involves radiation, the associated dose (0.4 Gy) is not a major limitation.