Preclinical CT Research Articles

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

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. 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). 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. 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. 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.

Dose reduction and image enhancement in micro-CT using deep learning.

In preclinical settings, micro-computed tomography (CT) provides a powerful tool to acquire high resolution anatomical images of rodents and offers the advantage to in vivo non-invasively assess disease progression and therapy efficacy. Much higher resolutions are needed to achieve scale-equivalent discriminatory capabilities in rodents as those in humans. High resolution imaging however comes at the expense of increased scan times and higher doses. Specifically, with preclinical longitudinal imaging, there are concerns that dose accumulation may affect experimental outcomes of animal models. Dose reduction efforts under the ALARA (as low as reasonably achievable) principles are thus a key point of attention. However, low dose CT acquisitions inherently induce higher noise levels which deteriorate image quality and negatively impact diagnostic performance. Many denoising techniques already exist, and deep learning (DL) has become increasingly popular for image denoising, but research has mostly focused on clinical CT with limited studies conducted on preclinical CT imaging. We investigate the potential of convolutional neural networks (CNN) for restoring high quality micro-CT images from low dose (noisy) images. The novelty of the CNN denoising frameworks presented in this work consists of utilizingimage pairs with realistic CT noise present in the input as well as thetarget image used for the model training; a noisier image acquired with a low dose protocol is matched to a less noisy image acquired with a higher dose scan of the same mouse. Low and high dose ex vivo micro-CT scans of 38 mice were acquired. Two CNN models, based on a 2D and 3D four-layer U-Net, were trained with mean absolute error (30 training, 4 validation and 4 test sets). To assess denoising performance, ex vivo mice and phantom data were used. Both CNN approaches were compared to existing methods, like spatial filtering (Gaussian, Median, Wiener) and iterative total variation image reconstruction algorithm. Image quality metrics were derived from the phantom images. A first observer study (n=23) was set-up to rank overall quality of differently denoised images. A second observer study (n=18) estimated the dose reduction factor of the investigated 2D CNN method. Visual and quantitative results show that both CNN algorithms exhibit superior performance in terms of noise suppression, structural preservation and contrast enhancement over comparator methods. The quality scoring by 23 medical imaging experts also indicates that the investigated 2D CNN approach is consistently evaluated as the best performing denoising method. Results from the second observer study and quantitative measurements suggest that CNN-based denoising could offer a 2-4× dose reduction, with an estimated dose reduction factor of about 3.2 for the considered 2D network. Our results demonstrate the potential of DL in micro-CT for higher quality imaging at low dose acquisition settings. In the context of preclinical research, this offers promising future prospects for managing the cumulative severity effects of radiation in longitudinal studies.

MODL-24. ESTABLISHING A CLINICALLY RELEVANT CT AND ASSOCIATED RADIOMICS PIPELINE FOR INTRACRANIAL RODENT TUMOUR MODELS

Abstract While magnetic resonance imaging (MRI) is the predominant imaging modality for glioblastoma (GBM), pre-clinical MRI scanner availability is limited. As pre-clinical CT is more widely available and cost-effective, this study aimed to 1) establish preclinical-GBM CT and CT-radiomic workflows, 2) identify whether CE-CT could detect murine orthotopic GBM tumours on two CT instruments [TRIUMPHX-O-CT; IVISSPECTRUM-CT], 3) assess whether CT-radiomic features could distinguish tumour from normal tissue, and support earlier detection of tumours, 4) verify translation of pre-clinical CT-radiomic pipelines to, and assess pre-clinical CT-features in, clinical CE-CT scans.U87R-Luc2(n=25) and NFpp10a-Luc2(n=10) orthotopic GBM models were established and tumours monitored via bioluminescence imaging (BLI). Concurrently, mice underwent CE-CT (IV-iodine/300mg/mL/50kV-scan). Extracted radiomic features (PyRadiomics) underwent dimensionality reduction (Spearman correlation; >0.85). Remaining features were analysed (Recursive feature elimination (RFE)/RepeatedCV/randomforest) in normal and tumour tissue and across timepoints (TRIUMPHX-O-CT-Wk3vsWk6,Wk6vsWk9/12; IVISSPECTRUM-CT-Wk6vsWk9/12).CE-CT and radiomic pipelines were successfully established for orthotopic GBM models, using both CT-systems. On visual assessment of images, BLI was significantly more sensitive, with tumours detectable at Wk1 (BLI) vs Wk9 (CE-CT). However, RFE analysis identified CT-radiomic features (first_order&glcm) which differentiated tumour from normal tissue (TRIUMPHX-O-CT). A subsequent feature set (first_order,glcm,gldm&glzm) were identified (TRIUMPHX-O-CT/IVISSPECTRUM-CT), detecting tumours earlier (Wk3&Wk6) than possible by visual assessment of CTs. Preclinical radiomic methods were successfully applied to exploratory clinical CE-CT scans(n=10). Here, several preclinical CT-features (e.g. Zone_Entropy) showed increased intensity in tumour regions. Overall experimental BLI is the most sensitive method for pre-clinical intracranial tumour detection. However, analysis of clinically relevant CT-radiomic features may facilitate tumour identification and earlier tumour detection (Wk3/Wk6-TRIUMPHX-O-CT/Wk6-IVISSPECTRUM-CT) than possible by visual assessment of CT (Wk9). Clinically relevant CT-derived radiomic features may therefore support intracranial rodent tumour assessment. Importantly, preclinical radiomic methods successfully translate to clinical CT-radiomic analysis. Parallel trends in tumour-specific feature intensities across pre-clinical and clinical scans suggest species conservation of features.

Abstract 15482: Self-Adaptive Deep Learning-Based Segmentation for Uniform and Functional Preclinical and Clinical CT Image Analysis

Currently, effective treatment options for heart failure are lacking. Methods to monitor cardiac function longitudinally can accelerate preclinical and clinical research into disease progression and novel drugs. However, manual image analysis of cardiac substructures is resource-intensive and error-prone. While automated methods exist for clinical CT images, translating these to preclinical μCT data is challenging. We aimed at employing deep-learning to automatically extract quantitative data on cardiac functional parameters, developing a uniform method for both μCT and CT images. For this, we collected 110 contrast-enhanced μCT images of wild-type and accelerated aging (Ercc1 Δ/- ) mice, as well as a public dataset of 60 segmented cardiac CT images. Substructures, i.e., the left ventricle, left myocardium and right ventricle, were manually segmented in 24 μCT images to create a training set. After template-based heart detection, two separate segmentation neural network architectures were trained using the nnU-Net framework. Automatic and manual segmentations of the μCT training set were nearly identical (mean Dice score 0.983). The estimated median Dice score (0.940) of the test set results was comparable to existing methods for μCT image segmentation. Also, the automatic volume metrics were similar to manual observations by an expert. In Ercc1 Δ/- mice, ejection fractions had significantly decreased, and myocardial mass increased by age 24 weeks. The mean Dice score of the CT segmentation results (0.925 ± 0.019, n = 40) was superior to those achieved by state-of-the-art algorithms. While further optimization and validation of the models is recommended, automated data extraction expands the use application of (μ)CT imaging, while reducing subjectivity and workload. In conclusion, the proposed method offers a pipeline to efficiently monitor disease progression in animals and humans, allowing uniform translation from preclinical to clinical studies.

High-Resolution 18F-FDG PET/CT for Assessing Three-Dimensional Intraoperative Margins Status in Malignancies of the Head and Neck, a Proof-of-Concept.

The surgical treatment of head and neck malignancies relies on the complete removal of tumoral tissue, while inadequate margins necessitate the use of adjuvant therapy. However, most positive margins are identified postoperatively as deep margins, and intraoperative identification of the deep positive margins could help achieve adequate surgical margins and decrease adjuvant therapies. To improve deep-margin identification, we investigated whether the use of high-resolution preclinical PET and CT could increase certainty about the surgical margins in three dimensions. Patients with a malignancy of the head and neck planned for surgical resection were administered a clinical activity of 4MBq/kg 18F-FDG approximately one hour prior to surgical initiation. Subsequently, the resected specimen was scanned with a micro-PET-CT imaging device, followed by histopathological assessment. Eight patients were included in the study and intraoperative PET/CT-imaging of 11 tumoral specimens and lymph nodes of three patients was performed. As a result of the increased resolution, differentiation between inflamed and dysplastic tissue versus malignant tissue was complicated in malignancies with increased peritumoral inflammation. The current technique allowed the three-dimensional delineation of 18F-FDG using submillimetric PET/CT imaging. While further optimization and patient stratification is required, clinical implementation could enable deep margin assessment in head and neck resection specimens.

A prototype spatial-spectral CT system for material decomposition with energy-integrating detectors.

Spectral CT has great potential for a variety of clinical applications due to improved tissue and material discrimination over conventional single-energy CT. Many clinical and preclinical spectral CT systems have two spectral channels enabling dual-energy CT. Strategies include split filtration, dual-layer detectors, photon-counting detectors, and kVp switching. The motivation for this work is the development of an x-ray source spectral modulation device with three or more spectral channels to enable high-sensitivity multi-material decomposition with energy-integrating detectors. We present spatial-spectral filters which are a new x-ray source modulation technology with the potential for additional channel diversity. The filtering device consists of an array of K-edge materials which divide the x-ray beam into spectrally varied beamlets. This design allows for an arbitrary number of spectral channels-trading off spatial and spectral information. We use a one-step model-based material decomposition (MBMD) algorithm to iteratively estimate material density images directly from the spatial-spectral CT data. In this work, we present a prototype spatial-spectral filter integrated with an x-ray CT test bench. The filter is composed of an array of tin, erbium, tantalum, and lead filter tiles which spatially modulate the system spectral sensitivity pattern. In a simulation study, we investigate the particular problem of mis-calibration between the data acquisition and the reconstruction model. With an understanding of the required model accuracy, we present a spectral calibration method to estimate the critical model parameters. To demonstrate feasibility of the spatial-spectral filter with a calibrated beamlet model, we collected a spatial-spectral CT scan of a multicontrast-enhanced phantom containing water, iodine, and gadolinium solutions. With simulations, we show that material decomposition is possible with spatial-spectral-filtered CT data, and we demonstrate the importance of a well-calibrated physical model. We find a 50% increase in error for focal spot model mismatch of 0.27mm and gap width model mismatch of 16mμ. With physical results, we demonstrate that the calibrated system model is in close agreement with the measured data, and that the reconstructed material density images match the ground truth concentrations for the multicontrast phantom. Empirical results indicate gadolinium density estimation had an error of 17-58% mostly due to a systematic constant bias of 0.30-0.60mg/ml. Water density estimates are within 1% and iodine estimates are within 10% of ground truth. These preliminary results demonstrate the potential of spatial-spectral filters to enable multicontrast imaging. Moreover, this device is compatible with energy-integrating detectors and so provides a feasible modification to enable spectral CT imaging with existing single-energy systems.

Phantom and Preclinical Studies for Image Improvement in Clinical CT

A rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach RSEMD that operates on digital imaging and communications in medicine images to improve the quality of clinical CT images is presented. The RSEMD method was tested on Catphan 500 and anthropomorphic 4-D XCAT phantoms to determine the improvements in signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The method was applied to preclinical CT images previously reconstructed by conventional software. To test the potential improvement in clinically relevant CT images we employed the 4-D XCAT phantom to simulate a small, low contrast lesion placed in the liver. In all of the phantom studies, the images proved to have higher resolution and lower noise as compared with filtered back projection. In general, the iterative deblurring restoration reaches the highest SNR and CNR values after approximately 20 iterations with an improvement factor of about 1.5 for both CNR and SNR in noisy CT images. We also found improvements in preclinical and clinical CT images after the application of RSEMD. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms which start in projection space and then follow in image domain. The RSEMD method can be applied to suboptimal routine-dose clinical CT images to improve the image quality to diagnostically acceptable levels.

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

The purpose of this study was to investigate a preclinical spectral photon-counting CT (SPCCT) prototype compared to conventional CT for pulmonary imaging. A custom-made lung phantom, including nodules of different sizes and shapes, was scanned with a preclinical SPCCT and a conventional CT in standard and high-resolution (HR-CT) mode. Volume estimation was evaluated by linear regression. Shape similarity was evaluated with the Dice similarity coefficient. Spatial resolution was investigated via MTF for each imaging system. In-vivo rabbit lung images from the SPCCT system were subjectively reviewed. Evaluating the volume estimation, linear regression showed best results for the SPCCT compared to CT and HR-CT with a root mean squared error of 21.3 mm3, 28.5 mm3 and 26.4 mm3 for SPCCT, CT and HR-CT, respectively. The Dice similarity coefficient was superior for SPCCT throughout nodule shapes and all nodule sizes (mean, SPCCT: 0.90; CT: 0.85; HR-CT: 0.85). 10% MTF improved from 10.1 LP/cm for HR-CT to 21.7 LP/cm for SPCCT. Visual investigation of small pulmonary structures was superior for SPCCT in the animal study. In conclusion, the SPCCT prototype has the potential to improve the assessment of lung structures due to higher resolution compared to conventional CT.

Beam Hardening Correction Using Cone Beam Consistency Conditions.

The polychromatic X-ray spectrum and the energy-dependent attenuation coefficient of materials cause beam hardening artifacts in CT reconstructed volumes. These artifacts appear as cupping and streak artifacts depending on the material composition and the geometry of the imaged object. CT scanners employ projection linearization to transform polychromatic attenuation to monochromatic attenuation using a polynomial model. Polynomial coefficients are computed during calibration or using prior information such as X-ray spectrum and attenuation properties of the materials. In this paper, we are presenting a novel method to correct beam hardening artifacts by enforcing cone beam consistency conditions on the projection data. We used consistency conditions derived from Grangeat's fundamental relation between cone beam projection data and 3-D Radon transform. The optimal polynomial coefficients for artifact reduction are iteratively estimated by minimizing the inconsistency of a set of projection pairs. The results from simulated and real datasets show the visible reduction of artifacts. Our studies also demonstrate the robustness of the algorithm when the projections are perturbed with other physical measurement and geometrical errors. The proposed method requires neither calibration nor prior information like X-ray spectrum, attenuation properties of the materials and detector response. The algorithm can be used for beam hardening correction in clinical, pre-clinical, and industrial CT systems.

High resolution propagation-based imaging system for in vivo dynamic computed tomography of lungs in small animals

We have developed an x-ray imaging system for in vivo four-dimensional computed tomography (4DCT) of small animals for pre-clinical lung investigations. Our customized laboratory facility is capable of high resolution in vivo imaging at high frame rates. Characterization using phantoms demonstrate a spatial resolution of slightly below 50 μm at imaging rates of 30 Hz, and the ability to quantify material density differences of at least 3%. We benchmark our system against existing small animal pre-clinical CT scanners using a quality factor that combines spatial resolution, image noise, dose and scan time. In vivo 4DCT images obtained on our system demonstrate resolution of important features such as blood vessels and small airways, of which the smallest discernible were measured as 55–60 μm in cross section. Quantitative analysis of the images demonstrate regional differences in ventilation between injured and healthy lungs.

Element-specific spectral imaging of multiple contrast agents: a phantom study

This work demonstrates the feasibility of simultaneous discrimination of multiple contrast agents based on their element-specific and energy-dependent X-ray attenuation properties using a pre-clinical photon-counting spectral CT. We used a photon-counting based pre-clinical spectral CT scanner with four energy thresholds to measure the X-ray attenuation properties of various concentrations of iodine (9, 18 and 36 mg/ml), gadolinium (2, 4 and 8 mg/ml) and gold (2, 4 and 8 mg/ml) based contrast agents, calcium chloride (140 and 280 mg/ml) and water. We evaluated the spectral imaging performances of different energy threshold schemes between 25 to 82 keV at 118 kVp, based on K-factor and signal-to-noise ratio and ranked them. K-factor was defined as the X-ray attenuation in the K-edge containing energy range divided by the X-ray attenuation in the preceding energy range, expressed as a percentage. We evaluated the effectiveness of the optimised energy selection to discriminate all three contrast agents in a phantom of 33 mm diameter. A photon-counting spectral CT using four energy thresholds of 27, 33, 49 and 81 keV at 118 kVp simultaneously discriminated three contrast agents based on iodine, gadolinium and gold at various concentrations using their K-edge and energy-dependent X-ray attenuation features in a single scan. A ranking method to evaluate spectral imaging performance enabled energy thresholds to be optimised to discriminate iodine, gadolinium and gold contrast agents in a single spectral CT scan. Simultaneous discrimination of multiple contrast agents in a single scan is likely to open up new possibilities of improving the accuracy of disease diagnosis by simultaneously imaging multiple bio-markers each labelled with a nano-contrast agent.

High Dose MicroCT Does Not Contribute Toward Improved MicroPET/CT Image Quantitative Accuracy and Can Limit Longitudinal Scanning of Small Animals

Obtaining accurate quantitative measurements in preclinical Positron Emission Tomography/Computed Tomography (PET/CT) imaging is of paramount importance in biomedical research and helps supporting efficient translation of preclinical results to the clinic. The purpose of this study was two-fold: (1) to investigate the effects of different CT acquisition protocols on PET/CT image quality and data quantification; and (2) to evaluate the absorbed dose associated with varying CT parameters. Methods: An air/water quality control CT phantom, tissue equivalent material phantom, an in-house 3D printed phantom and an image quality PET/CT phantom were imaged using a Mediso nanoPET/CT scanner. Collected data was analyzed using PMOD software, VivoQuant software and National Electric Manufactures Association (NEMA) software implemented by Mediso. Measured Hounsfield Unit (HU) in collected CT images were compared to the known HU values and image noise was quantified. PET recovery coefficients (RC), uniformity and quantitative bias were also measured. Results: Only less than 2% and 1% of CT acquisition protocols yielded water HU values < -80 and air HU values < -840, respectively. Four out of eleven CT protocols resulted in more than 100 mGy absorbed dose. Different CT protocols did not impact PET uniformity and RC, and resulted in <4% overall bias relative to expected radioactive concentration. Conclusion: Preclinical CT protocols with increased exposure times can result in high absorbed doses to the small animals. These should be avoided, as they do not contributed towards improved microPET/CT image quantitative accuracy and could limit longitudinal scanning of small animals.

Recombinant polymorphic membrane protein D in combination with a novel, second-generation lipid adjuvant protects against intra-vaginal Chlamydia trachomatis infection in mice

The development of a chlamydial vaccine that elicits protective mucosal immunity is of paramount importance in combatting the global spread of sexually transmitted Chlamydia trachomatis (Ct) infections. While the identification and prioritization of chlamydial antigens is a crucial prerequisite for efficacious vaccine design, it is likely that novel adjuvant development and selection will also play a pivotal role in the translational potential of preclinical Ct vaccines. Although the molecular nature of the immuno-modulatory component is of primary importance, adjuvant formulation and delivery systems may also govern vaccine efficacy and potency. Our study provides the first preclinical evaluation of recombinant Ct polymorphic membrane protein D (rPmpD) in combination with three different formulations of a novel second-generation lipid adjuvant (SLA). SLA was rationally designed in silico by modification of glucopyranosyl lipid adjuvant (GLA), a TLR4 agonistic precursor molecule currently in Phase II clinical development. We demonstrate robust protection against intra-vaginal Ct challenge in mice, evidenced by significantly enhanced resistance to infection and reduction in mean bacterial load. Strikingly, protection was found to correlate with the presence of robust anti-rPmpD serum and cervico-vaginal IgG titres, even in the absence of adjuvant-induced Th1-type cellular immune responses elicited by each SLA formulation, and we further show that anti-rPmpD antibodies recognize Ct EBs. These findings highlight the utility of SLA and rational molecular design of adjuvants in preclinical Ct vaccine development, but also suggest an important role for anti-rPmpD antibodies in protection against urogenital Ct infection.

Quantitative spectral K-edge imaging in preclinical photon-counting x-ray computed tomography.

The objective of this study was to investigate the feasibility and the accuracy of spectral computed tomography (spectral CT) to determine the tissue concentrations and localization of high-attenuation, iodine-based contrast agents in mice. Iodine tissue concentrations determined with spectral CT are compared with concentrations measured with single-photon emission computed tomography (SPECT) and inductively coupled plasma mass spectrometry (ICP-MS). All animal procedures were performed according to the US National Institutes of Health principles of laboratory animal care and were approved by the ethical review committee of Maastricht, The Netherlands. Healthy Swiss mice (n = 4) were injected with an iodinated emulsion radiolabeled with indium as multimodal contrast agent for CT and SPECT. The CT and SPECT scans were acquired using a dedicated small-animal SPECT/CT system. Subsequently, scans were performed with a preclinical spectral CT scanner equipped with a photon-counting detector and 6 energy threshold levels. Quantitative data analysis of SPECT and spectral CT scans were obtained using 3-dimensional volumes-of-interest drawing methods. The ICP-MS on dissected organs was performed to determine iodine uptake per organ and was compared with the amounts determined from spectral CT and SPECT. Iodine concentrations obtained with image-processed spectral CT data correlated well with data obtained either with noninvasive SPECT imaging (slope = 0.96, r = 0.75) or with ICP-MS (slope = 0.99, r = 0.89) in tissue samples. This preclinical proof-of-concept study shows the in vivo quantification of iodine concentrations in tissues using spectral CT. Our multimodal imaging approach with spectral CT and SPECT using radiolabeled iodinated emulsions together with ICP-based quantification allows a direct comparison of all methods. Benchmarked against ICP-MS data, spectral CT in the present implementation shows a slight underestimation of organ iodine concentrations compared with SPECT but with a more narrow distribution. This slight deviation is most likely caused by experimental rather than technical issues.

OC-0411: Design and performance of a preclinical proton CT head scanner

OC-0411: Design and performance of a preclinical proton CT head scanner

Energy calibration of the pixels of spectral X-ray detectors.

The energy information acquired using spectral X-ray detectors allows noninvasive identification and characterization of chemical components of a material. To achieve this, it is important that the energy response of the detector is calibrated. The established techniques for energy calibration are not practical for routine use in pre-clinical or clinical research environment. This is due to the requirements of using monochromatic radiation sources such as synchrotron, radio-isotopes, and prohibitively long time needed to set up the equipment and make measurements. To address these limitations, we have developed an automated technique for calibrating the energy response of the pixels in a spectral X-ray detector that runs with minimal user intervention. This technique uses the X-ray tube voltage (kVp) as a reference energy, which is stepped through an energy range of interest. This technique locates the energy threshold where a pixel transitions from not-counting (off) to counting (on). Similarly, we have developed a technique for calibrating the energy response of individual pixels using X-ray fluorescence generated by metallic targets directly irradiated with polychromatic X-rays, and additionally γ-rays from (241)Am. This technique was used to measure the energy response of individual pixels in CdTe-Medipix3RX by characterizing noise performance, threshold dispersion, gain variation and spectral resolution. The comparison of these two techniques shows the energy difference of 1 keV at 59.5 keV which is less than the spectral resolution of the detector (full-width at half-maximum of 8 keV at 59.5 keV). Both techniques can be used as quality control tools in a pre-clinical multi-energy CT scanner using spectral X-ray detectors.

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Three-dimensional printing allows for the production of highly detailed objects through a process known as additive manufacturing. Traditional, mold-injection methods to create models or parts have several limitations, the most important of which is a difficulty in making highly complex products in a timely, cost-effective manner.1 However, gradual improvements in three-dimensional printing technology have resulted in both high-end and economy instruments that are now available for the facile production of customized models.2 These printers have the ability to extrude high-resolution objects with enough detail to accurately represent in vivo images generated from a preclinical X-ray CT scanner. With proper data collection, surface rendering, and stereolithographic editing, it is now possible and inexpensive to rapidly produce detailed skeletal and soft tissue structures from X-ray CT data. Even in the early stages of development, the anatomical models produced by three-dimensional printing appeal to both educators and researchers who can utilize the technology to improve visualization proficiency. 3, 4 The real benefits of this method result from the tangible experience a researcher can have with data that cannot be adequately conveyed through a computer screen. The translation of pre-clinical 3D data to a physical object that is an exact copy of the test subject is a powerful tool for visualization and communication, especially for relating imaging research to students, or those in other fields. Here, we provide a detailed method for printing plastic models of bone and organ structures derived from X-ray CT scans utilizing an Albira X-ray CT system in conjunction with PMOD, ImageJ, Meshlab, Netfabb, and ReplicatorG software packages.

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Three-dimensional printing allows for the production of highly detailed objects through a process known as additive manufacturing. Traditional, mold-injection methods to create models or parts have several limitations, the most important of which is a difficulty in making highly complex products in a timely, cost-effective manner.1 However, gradual improvements in three-dimensional printing technology have resulted in both high-end and economy instruments that are now available for the facile production of customized models.2 These printers have the ability to extrude high-resolution objects with enough detail to accurately represent in vivo images generated from a preclinical X-ray CT scanner. With proper data collection, surface rendering, and stereolithographic editing, it is now possible and inexpensive to rapidly produce detailed skeletal and soft tissue structures from X-ray CT data. Even in the early stages of development, the anatomical models produced by three-dimensional printing appeal to both educators and researchers who can utilize the technology to improve visualization proficiency. 3, 4 The real benefits of this method result from the tangible experience a researcher can have with data that cannot be adequately conveyed through a computer screen. The translation of pre-clinical 3D data to a physical object that is an exact copy of the test subject is a powerful tool for visualization and communication, especially for relating imaging research to students, or those in other fields. Here, we provide a detailed method for printing plastic models of bone and organ structures derived from X-ray CT scans utilizing an Albira X-ray CT system in conjunction with PMOD, ImageJ, Meshlab, Netfabb, and ReplicatorG software packages.

Iodinated nano-emulsions as contrast agents for preclinical X-ray imaging: Impact of the free surfactants on the pharmacokinetics

This study presents new important aspects in the design of contrast agents for X-ray preclinical imaging. The first one is a new simple formulation of long circulating contrast agents, formulated from a commercial iodinated oil, and resulting in CT contrast agents containing more than twice the iodine concentration commercial contrast agents. The second point is a methodological aspect, utilizing tangential filtration for reducing the residual surfactants in the bulk phase and serving as well for concentrating droplets (and iodine) in the suspension. The last point is a more general aspect regarding the influence of the free surfactant on the pharmacokinetics and biodistribution of the nano-emulsion droplets on mice. We showed that cross-flow filtration is efficient for concentrating the droplets and reducing the concentration of free surfactant from 10wt.% to 1wt.%, without any changes in the nano-emulsion droplet morphologies or surface properties. We also showed that the presence of free surfactant has a significant impact on the elimination way of the nano-emulsion droplets, shared between liver and kidneys. The purified nano-emulsions are preferentially eliminated by the kidneys in contrast to raw nano-emulsions, predominantly eliminated by the liver. In practice, for two similar suspensions, half-life decreases from 4.1±1.10h to 2.5±0.77h before and after purification. Since the design and development of long circulating systems are critical in numerous domains, and not for preclinical CT imaging, this study presents important results in that field, taken under a formulation and technical point of view.

Abstract 363: Development of computer-aided detection (CAD) tool for liver metastasis micro CT imaging using targeted contrast agent

Abstract Introduction: Preclinical in-vivo micro CT studies of liver metastasis are difficult due to poor inherent soft tissue contrast and the need for highly technical, manual analysis of the data. Research has implicated that Kupffer cells in the liver encapsulate liver metastases providing an opportunity to deliver macrophage-specific contrast agents for the detection of small metastatic lesions. A new, long-acting preclinical CT contrast agent that targets Kupffer cells has been developed that may allow automated detection of liver lesions via CAD software. Method: A pancreatic cancer liver metastasis model was created by surgically implanting human pancreatic cancer cell line (L3.6pl) within the spleen. Mice were injected with the contrast agent and scanned to obtain baseline anatomy of the liver before implantation. The mice were scanned 1 day after implantation and weekly after that for 5 weeks to monitor the liver metastasis progression. The control group followed an identical protocol but with a sham surgery. Liver tissues were harvested and fixed in paraffin blocks after the last scan. Paraffin blocks were scanned using high resolution micro CT before IHC staining. Human Mitochondrial and F4/80 IHC were used to identify L3.6pl and Kupffer cells, respectively. The CT images were compared to the IHC images from the same block to verify that the locations of the contrast agent and the Kupffer cells were related. Once the pattern of contrast agent and metastatic tumors had been identified, CAD software was developed for automatic tumor detection. Results: The contrast agent was evenly distributed throughout the healthy liver tissue within 1 hour post injection. In healthy mice, the homogenous distribution of contrast remained unchanged for at least 6 weeks. In liver metastasis models, the contrast began to concentrate in various areas of the liver within 2 weeks post implantation. As tumors developed and grew, the contrast became highly concentrated on the borders of tumors creating a 3 dimensional outline of the lesion. IHC staining and micro CT imaging of the fixed tissue verified that the tumors are surrounded by Kupffer cells and that the distribution of concentrated contrast agent matched them. Software was able to detect the tumors based on these contrast outlines and compare them over successive weekly scans. Conclusion: Our new imaging method enables automated detection and evaluation of liver metastasis 1 mm or smaller from as early as 2 weeks. In addition to allowing better visualization, it provides new insight into macrophage motility within the liver. CAD software can take advantage of this unique capability to automate data analysis and allow for large scale longitudinal studies. This new imaging method could be a useful tool to facilitate longitudinal imaging of liver metastases in mice and has the potential for translation into clinical practice. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 363. doi:1538-7445.AM2012-363