This paper presents a deep learning approach to automated segmentation of cardiac structures in 5D (3D + Time + Energy) Photon-Counting micro-CT (PCCT) imaging sets. We have acquired, reconstructed, and fully segmented a preclinical dataset of cardiac micro-PCCT scans in APOE mouse models. These APOE genotypes serve as models of varying degrees of risk of Alzheimer’s disease and cardiovascular disease. The dataset of user-guided segmentations served as the training data for a deep learning 3D UNet model capable of segmenting the four primary cardiac chambers, the aorta, pulmonary artery, inferior and superior vena cava, myocardium, and the pulmonary tree. Experimental results demonstrate the effectiveness of the proposed methodology in achieving reliable and efficient cardiac segmentation. We demonstrate the difference in performance when using single-energy PCCT images versus decomposed iodine maps as input. We achieved an average Dice score of 0.799 for the network trained on single-energy images and 0.756 for the network trained using iodine maps. User-guided segmentations took approximately 45 minutes/mouse while CNN segmentation took less than one second on a system with a single RTX 5000 GPU. This novel deep learning-based cardiac segmentation approach holds significant promise for advancing phenotypical analysis in mouse models of cardiovascular disease, offering a reliable and time-efficient solution for researchers working with photon-counting micro-CT imaging data.
Brain region segmentation and morphometry in mouse models of Alzheimer’s Disease (AD) risk allow us to understand how various factors affect the brain. Photon-Counting Detector (PCD) micro-CT can provide faster brain imaging than MRI and superior contrast and spatial resolution to Energy-Integrating Detector (EID) micro-CT. This paper demonstrates a PCD micro-CT based approach for mouse brain imaging, segmentation, and morphometry. We extracted and stained the brains of 26 mice from three genotypes (APOE22HN, APOE33HN, APOE44HN). We scanned these brains with PCD and EID micro-CT, performed hybrid (PCD and EID) iterative reconstruction, and used the Small Animal Multivariate Brain Analysis (SAMBA) tool to segment the brains via registration to our new PCD CT mouse brain atlas. We used the outputs of SAMBA to run region-based and voxel-based comparisons by genotype and sex. Together, PCD and EID scanning take approximately five hours and produce images with a voxel size of 22 μm, which is faster than prior MRI protocols that produce images with a voxel size above 40 μm. PCD images from hybrid iterative reconstruction have minimal artifacts and higher spatial resolution and contrast than EID images. Qualitative and quantitative analyses confirmed that our PCD atlas is similar to the prior MRI atlas and that it successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant difference in relative size in 26 brain regions. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus. This study successfully establishes a PCD micro-CT approach for mouse brain analysis that can be used for future AD research.
Photon Counting Detectors (PCD) have emerged as a transformative technology in CT and micro-CT imaging, offering enhanced contrast resolution and quantitative material separation in a single scan, a notable advancement from traditional energy-integrating detectors. The unique properties of bismuth tungstate (Bi2WO6) nanoparticles (NPs), hold promise in many applications, including contrast-enhanced CT imaging and photothermal therapy, especially in addressing tumor hypoxia challenges. However, despite these promising traits, the performance of PCCT imaging using Bi2WO6 NPs has not been fully explored. Our study bridges this gap by employing both simulations and real experiments. Using iterative PCCT reconstruction, we achieved significant noise reduction, from a noise standard deviation up to 786 Hounsfield Units (HU) down to 54 HU, enabling material decomposition. The dual K-edge of Bi2WO6, coupled with a precise 2:1 Bismuth to Tungsten ratio, offers a unique, quantifiable signature for PCCT imaging: the enhancement of Bi2WO6 remains largely constant over the diagnostic x-ray range (stddev: 1.24 HU/mg/mL over 25-91 keV energy thresholds, 125 kVp spectrum; iodine stddev: 11.62 HU/mg/mL). Improved separation of contrast material from intrinsic tissues promises to enhance all facets of clinical CT, including new avenues for radiation dose and metal artifact reduction. Potential new clinical applications include targeted radiation therapy, where Bi2WO6 NPs could intensify treatment efficacy and optimize chemotherapeutic delivery.
This study investigated the application of VivoVist™, a high-contrast micro-CT contrast agent, in spectral Photon-Counting (PC) micro-CT imaging in mouse models. With a long blood half-life, superior concentration, and reduced toxicity VivoVist, composed of barium (Ba)-based nanoparticles, offers a cost-effective solution for enhancing Computed Tomography (CT) imaging. To evaluate its efficacy, we employed an in-house developed spectral micro-CT with a photon-counting detector. VivoVist was administered through retro-orbital injection in a non-tumor-bearing C57BL/6 mouse and in two mice with MOC2 buccal tumors, with scans taken at various post-injection intervals. We used a multi-channel iterative reconstruction algorithm to provide multi-energy tomographic images with a voxel size of 125 microns or 75 microns for high-resolution scans; we performed post-reconstruction spectral decomposition with water, calcium (Ca), iodine (I), and barium (Ba) as bases. Our results revealed effective separation of Ba from I-based contrast agents with minimal cross-contamination and superior contrast enhancement for VivoVist at 39 keV. We also observed VivoVist's potential in delineating vasculature in the brain and its decreasing concentration in the blood over time post-injection, with increased uptake in the liver and spleen. We also explored the simultaneous use of VivoVist and liposomal iodinated nanoparticles in a cancer study involving radiation therapy. Our findings reveal that VivoVist, combined with radiation therapy, did not significantly increase liposomal iodine accumulation within head and neck squamous cell carcinoma tumors. In conclusion, our work confirms VivoVist's promising role in enhancing PCCT imaging and its potential in studying combination therapy, warranting further investigation into its applications in diagnostics and radiotherapy.
Photon-counting detectors (PCDs) represent a technological advancement in X-ray CT imaging, bringing increased spatial resolution and spectral information to imaging in medical and industrial fields. Despite their potential, a critical issue arises from dead pixel gaps between detector tiles, leading to image artifacts and a reliance on imperfect computational infilling methods. Addressing this challenge, we introduced an acquisition-based solution utilizing a custom-built micro-CT system capable of laterally shifting the PCD during scans. We acquired laterally offset projection data to fill pixel gaps in unshifted projection data. The approach's inherent robustness not only bypasses the need for traditional inpainting or interpolation algorithms but also maintains high quantitative fidelity. Our method shows a marked decrease in low-frequency ring artifacts, surpassing conventional methods in performance. With the potential to be integrated into existing systems or combined with emerging deep learning techniques, our contribution opens promising prospects for future research and applications. Ultimately, this work underscores a significant step toward enhancing image quality and diagnostic precision in X-ray CT imaging, offering a practical and innovative solution to a longstanding problem.
In preclinical studies, micro-CT is frequently employed to yield valuable anatomical insights. However, there has been an increasing need for micro-CT in extracting functional measurements such as with perfusion imaging. Perfusion imaging plays a crucial role in understanding and quantifying tissue vascular properties. This paper focuses on our development of preclinical micro-photon counting (PC)CT perfusion imaging and demonstrates quantification of perfusion metrics in a controlled fluid flow phantom experiment. For this study, we utilized a novel bioprinted perfusion phantom and a dedicated preclinical photon-counting CT (PCCT) system to estimate perfusion maps at different flow rates. A continuous water flow through the phantom was maintained by a peristaltic pump. PCCT imaging was performed during a delayed contrast injection of clinical iodinated contrast agent. Imaging was repeated under 3 different flow rates: 4, 6, and 8 mL/min. Our results demonstrate successful visualization and quantification of flow parameters by employing gamma variate curves fit to voxel measurements of temporal PCCT reconstructions as well as decomposed iodine material maps, enabling the calculation of volumetric maps for mean transit time, blood volume index, and blood flow index. Furthermore, we showcase the application of this technique in quantifying in vivo perfusion characteristics in a head and neck cancer model in a mouse. Through our research, we aim to highlight the potential of preclinical micro-PCCT perfusion imaging in advancing our understanding of tissue perfusion dynamics and its potential applications in studying various pathologies and cardiovascular conditions.
Recently, we have released the first open-source version of our Multi-Channel CT Reconstruction (MCR) Toolkit (https://gitlab.oit.duke.edu/dpc18/mcr-toolkit-public). The initial release of the Toolkit represents 10 years of development and provides a complete set of GPU-accelerated tools for solving multi-channel (multi-energy, time-resolved) X-ray CT reconstruction problems with support for both analytical and iterative reconstruction in common preclinical and clinical geometries. This initial version of the Toolkit (v1.0) relies on MATLAB and its MEX interface for orchestrating CT reconstruction pipelines; however, heavy reliance on MATLAB comes with licensing restrictions and limited support for deep learning augmentation of reconstruction pipelines. In this work, we detail the features of v2.0 of the MCR Toolkit which ports all the Toolkit’s v1.0 features from MATLAB to the Python programming language, including the ability to perform regularized, iterative reconstruction of multi-energy photon-counting cardiac CT data. We demonstrate these new features through benchmarks which show comparable performance between our MATLAB (v1.0) and Python (v2.0) implementations of the BiCGSTAB(l) solver, following improved memory management in our Python implementation. We also demonstrate a high-level interface between v2.0 of the Toolkit and PyTorch, allowing the incorporation of a previously trained multi-energy CT denoising model, known as UnetU, directly in our multi-channel reconstruction framework. These preliminary reconstruction results show a reduction in intensity bias from 13HU, after a single pass of the UnetU denoising model, to 7HU after the same model is incorporated into our iterative reconstruction framework; however, some high-contrast edge features are exaggerated in the UnetU reconstruction, and the noise standard deviation increases from 21HU to 34HU.
Photon-counting detectors (PCDs) are advantageous for spectral CT imaging and material decomposition because they simultaneously acquire projections at multiple energies using energy thresholds. Unfortunately, the PCD produces noisy weighted filtered backprojection (wFBP) reconstructions due to diminished photon counts in high-energy bins. Iterative reconstruction generates high quality PCD images, but requires long computation time, especially for 5D (3D + energy + time) in vivo cardiac imaging. Our recent work introduced a convolutional neural network (CNN) approach called UnetU for accurate 4D (3D + energy) photon-counting CT (PCCT) denoising at various acquisition settings. In this study, we explore how to adapt UnetU to denoise 5D in vivo cardiac PCCT reconstructions of mice. We experiment with singular value decomposition (SVD) modifications along the energy and time dimensions and replacing the U-net with a FastDVDNet architecture designed for color video denoising. All CNNs used the same group of 5D cardiac PCCT mouse sets, with 6 for training and a 7th held out for testing. All DL methods were more than 20 times faster than iterative reconstruction. UnetU Energy (which takes SVD along the energy dimension) was the most consistent at producing low root mean square error (RMSE) and spatio-temporal reduced reference entropic difference (STRRED) as well as good qualitative agreement with iterative reconstruction. This result is likely because 5D cardiac PCCT data has lower effective rank along the energy dimension than the time dimension. FastDVDNet showed promise but did not outperform UnetU Energy. Our study establishes UnetU Energy as a very accurate method for denoising 5D cardiac PCCT reconstructions that is more than 32 times faster than iterative reconstruction. This advancement enables high quality cardiac imaging with low computational burden, which is valuable for cardiovascular disease studies in mice.
Spectral micro-CT shows great potential to provide accurate material composition by utilizing the energy dependence of x-ray attenuation in different materials. This is especially well-suited for pre-clinical imaging using nanoparticle-based contrast agents in situations where quantitative material decomposition helps probe processes which are otherwise limited by poor soft tissue contrast. Our group has developed multiple generations of pre-clinical prototype PCCT systems and applied them in cancer and cardiac studies using nanoparticle contrast agents. This work aims to describe and assess the performance of a hybrid system for ex vivo high-resolution micro-CT using photon counting and energy integrating detectors. Both phantom and ex vivo mouse micro-CT data were reconstructed using our iterative, multi-channel algorithm based on the split Bregman method and regularization with rank-sparse kernel regression. A post-reconstruction spectral decomposition method was used. The system is capable of high resolution (15.6 lp/mm, 10% MTF) tomographic imaging. Despite the anti-coincidence corrections, the spectral performance of the PCD is, however, not perfect. Preliminary results show that adding energy integrating data to the PCD scan reduces the prevalence of certain PCD-specific artifacts and offers the potential for various hybrid approaches to PCD corrections. We also show that our spectral hybrid micro-CT separates calcified plaques from the iodine accumulation in our atherosclerosis mouse model. This is not possible in the EID-based CT imaging. Such hybrid spectral micro-CT will benefit both nanotechnology and imaging developments by providing an ex vivo high resolution imaging method that can validate experiments in theranostics.
Superior material discrimination provided by photon counting detector (PCD) technology promises to transform X-ray CT into a functional and molecular imaging modality while maintaining its high spatial resolution, fast scanning times, and relatively low cost. Our group has developed pre-clinical photon-counting CT (PCCT) prototype systems and applied them, in combination with nanoparticle contrast agents, for cancer and cardiac imaging. This work aims to compare the PCCT imaging performance using a gallium arsenide (GaAs) and a cadmium telluride (CdTe) based PCD, both with 150- μm pixels and 4 energy thresholds. The two PCDs were integrated in the same PCCT system. Phantoms containing elemental solutions of Iodine, Gadolinium, Tantalum, Hafnium, Bismuth and Calcium were imaged with each detector to establish the spectral separation capabilities for PCCT. Moreover, combined dual detectors PCCT imaging was also tested. A joint iterative reconstruction followed by image-based material decomposition was used to provide material maps of different elements. The accuracy of the estimated concentrations within the material decompositions were compared. Our results have shown that GaAs-based PCCT imaging has an overall higher sensitivity (by ~15%) to Iodine than CdTe when using identical acquisition parameters. The CdTe imaging, has higher quantum efficiency at high keVs, supporting higher source kVps and energy threshold settings for imaging the K-edges of Bismuth or other high-Z NPs (such as Gold). Based on the average vial measurements, hybrid GaAs-CdTe PCCT decompositions have the most accurate decompositions for nearly all materials except for Bismuth. The combination of CdTe and GaAs PCDs into a dual source PCCT system will provide high sensitivity in separating multi-element contrast agents from intrinsic tissues.
Although photon counting detectors (PCD) can offer numerous benefits for CT imaging, it is difficult to generate accurate material decompositions from photon counting (PC) CT images due to spectral distortions. In this work, we present a deep learning (DL) approach for material decomposition from PCCT. To produce training and testing data for this study, we scanned two ex-vivo mice using a PCD scan protocol with a dose of 36 mGy and a multi-EID scan protocol with a dose of 296 mGy. PCD images were reconstructed using filtered backprojection. EID images were reconstructed using an iterative algorithm to reduce noise, and decomposed into iodine (I), Compton scattering (CS), and photoelectric effect (PE) material maps by a matrix inversion approach. We then trained a convolutional neural network with a 3D U-net structure using PCD images as inputs and multi-EID material maps as labels, and evaluated its performance. The 3D U-net predictions provided substantially lower RMSE compared to decomposition from PCD images using a matrix inversion approach. Measurements from iodine vials in the test set showed that 3D U-net predictions gave mean values within 0.6 mg/mL of the mean values from the multi-EID material maps and much lower standard deviation than PCD material map measurements. Our results show that the trained 3D U-net enables low-noise, quantitatively accurate material decomposition from a low dose PCD scan.
Photon-counting detector (PCD) CT promises to improve routine CT imaging applications with higher spatial resolution, lower levels of noise at fixed dose, and improved image contrast while providing spectral information with every scan. We propose and demonstrate a novel application of PCD imaging in a preclinical model of head and neck squamous cell carcinoma: spectral perfusion imaging of cancer. To handle the high dimensionality of our data set (3D volumes at 12 perfusion time points times 4 energy thresholds), we update our previously proposed multi-channel iterative reconstruction algorithm to handle the perfusion reconstruction problem, and we propose an extension which adds patch-based singular value thresholding (pSVT) along the perfusion dimension. Adding pSVT reduces noise by an additional 45% relative to our standard algorithm, which itself reduces noise by 2-7 times relative to analytical reconstruction. Preliminary analysis suggests that the addition of pSVT does not negatively impact material decomposition accuracy or image spatial resolution. Notable weaknesses of this preliminary study include relatively high contrast agent dose (0.5 mL ISOVUE-370 over 10 seconds), ionizing radiation dose (~570 mGy), and computation time (2.9 hours, no pSVT; 11 hours with pSVT); however, following from our past work, our reconstruction algorithm may be an ideal source of training labels for supervised deep learning applied to computationally cheap analytical reconstructions.
Developing novel contrast agents for multi-energy photon-counting (PC)CT will require a clear translation pathway from preclinical validation to clinical applications. To begin this development, we have used a clinical PCCT scanner (Siemens NAEOTOM Alpha) to study the spectral separation of a few contrast elements (Iodine, I; Gadolinium, Gd; Hafnium, Hf; Tantalum, Ta; Bismuth, Bi; Calcium, Ca) with currently available scanning protocols (fixed: 120 kVp, 20 and 65 keV thresholds). We also explored the capabilities of clinical and preclinical PCCT to image mice with sarcoma tumors injected with nanoparticles (NP). Our results indicate that Ta or Hf are complementary to I or Gd, providing excellent spectral separation for future multi-agent studies. Based on preclinical PCCT with four energy thresholds, we also conclude that additional energy thresholds will benefit clinical PCCT. Furthermore, we demonstrate the role that multi-channel denoising and reconstruction algorithms will play in pushing the bounds of spatial and spectral resolution with clinical PCCT. Performing co-clinical research will facilitate the translation of novel imaging algorithms and NP contrast agents for PCCT
The photon-counting (PC) detector technology promises to enhance the number of CT applications due to the spectral information. Of high interest for the cancer research community is imaging the tumor delivery of Cisplatin (CisPt), a chemotherapeutic agent utilized for treatment of numerous malignancies. CisPt contains platinum (Pt), a high-Z element material with a K-edge (78.4 keV) in the diagnostic spectrum. Our group has developed a preclinical prototype photon counting (PC) CT and applied it in cancer studies using nanoparticles. This study aims to investigate if CisPt can be imaged by K-edge spectral PCCT. Simulations and phantom experiments were performed to investigate CisPt detection using PCCT. We have selected scanning parameters (125 kVp) and energy thresholds (28, 34, 70, 78 keV) to enable K-edge separation of Pt and iodine (I) from calcium (Ca). The simulations include modeling of the polychromatic spectrum, and the PC detector response with spectral distortions. Two digital phantoms were used with maximum concentrations corresponding to low (2 mg/mL) and high (10 mg/mL) concentrations of I and Pt. A physical phantom with CisPt, I and Ca solutions was imaged both on our PC micro-CT and a novel clinical PCCT system. Material decompositions confirm the separation of Pt, Ca and I. However, low concentrations (<1 mg/mL) of CisPt are unlikely to be separated. Nevertheless, a liposomal nanoparticle-based CisPt formulation can enhance tumor delivery, via enhanced permeability and retention (EPR) and benefit from PCCT monitoring. Thus, depending on the levels of tumor accumulation, PCCT imaging of nanoparticles containing CisPt may become possible.
The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrastenhancement can differentiate tumors based on tumor-infiltrating lymphocyte (TIL) burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/- and Rag2-/- mice to model varying TIL 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. RFs were ranked to reduce redundancy and increase relevance based on TIL burden. A leave one out strategy was used to assess separation using a neural network classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/- (TILs present) and Rag2-/- (TIL-deficient) tumors. RFs further enabled differentiation between Rag2+/- and Rag2-/- tumors. The PCD-derived RFs provided the highest accuracy (0.84) followed by decomposition-derived RFs (0.78) and the EID-derived RFs (0.65). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.
Non-invasive imaging strategies are essential in understanding the factors associated with the ongoing rise in cardiovascular disease (CVD) and Alzheimer’s disease (AD), and the possible interactions between these diseases. Our scientific premise is based on the role of the APOE gene where APOE4 is considered a risk factor for CVD and AD. This study incorporates the use of novel apolipoprotein E mouse models with a humanized innate immune system, through mNos2 KO and presence of the human NOS2 gene (APOE4/HN and APOE3/HN), together with the more traditional APOE (-/-) mice. We have imaged these models for CVD and AD with a cardiac photon-counting CT (PCCT) imaging pipeline to characterize their cardiac anatomy and function. The pipeline, consisting of contrast enhanced in vivo PCCT imaging, accurate intrinsic cardiac gating, temporally resolved multi-energy iterative reconstruction, spectral decomposition, 3D cardiac segmentation, and ex vivo, high-resolution PCCT imaging allowed for quantitative analysis and comparison of cardiac function as well as identification of anatomical irregularities such as calcified aortic plaques. Our analysis finds no statistically significant differences in cardiac functional metrics or the aortic diameter in these APOE mouse models. Future work will focus on optimizing the image reconstruction to reduce the computation time and on using staining for ex vivo PCCT imaging of the vascular system in these models.
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