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.
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.
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.
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).
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.
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.
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.
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.
The maturation of photon-counting detector (PCD) technology promises to enhance routine CT imaging applications with high-fidelity spectral information. In this paper, we demonstrate the power of ...this synergy and our complementary reconstruction techniques, performing 4D, cardiac PCD-CT data acquisition and reconstruction in a mouse model of atherosclerosis, including calcified plaque. Specifically, in vivo cardiac micro-CT scans were performed in four ApoE knockout mice, following their development of calcified plaques. The scans were performed with a prototype PCD (DECTRIS, Ltd.) with 4 energy thresholds. Projections were sampled every 10 ms with a 10 ms exposure, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40 total 3D volumes per mouse scan). Reconstruction was performed iteratively using the split Bregman method with constraints on spectral rank and spatio-temporal gradient sparsity. The reconstructed images represent the first in vivo, 4D PCD-CT data in a mouse model of atherosclerosis. Robust regularization during iterative reconstruction yields high-fidelity results: an 8-fold reduction in noise standard deviation for the highest energy threshold (relative to unregularized algebraic reconstruction), while absolute spectral bias measurements remain below 13 Hounsfield units across all energy thresholds and scans. Qualitatively, image domain material decomposition results show clear separation of iodinated contrast and soft tissue from calcified plaque in the in vivo data. Quantitatively, spatial, spectral, and temporal fidelity are verified through a water phantom scan and a realistic MOBY phantom simulation experiment: spatial resolution is robustly preserved by iterative reconstruction (10% MTF: 2.8-3.0 lp/mm), left-ventricle, cardiac functional metrics can be measured from iodine map segmentations with ~1% error, and small calcifications (615 μm) can be detected during slow moving phases of the cardiac cycle. Given these preliminary results, we believe that PCD technology will enhance dynamic CT imaging applications with high-fidelity spectral and material information.
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.
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.
Dual-energy CT enabled in vivo separation of gold and iodine contrast agents and showed uptake of gold nanoparticles in the spleen, liver, and tumors. The tumor fractional blood volume measurements determined from the two imaging methods were in agreement, and a high correlation (R(2) = 0.81) was found between measured fractional blood volume and histology-derived microvascular density. Vascular permeability measurements obtained from the two imaging methods agreed well with ex vivo measurements.
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.
Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how ...various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT.
We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex.
Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD.
This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.
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.
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.
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