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While the benefits of photon counting (PC) CT are significant, the performance of current PCDs is limited by physical effects distorting the spectral response. In this work, we examine a deep learning (DL) approach for spectral correction and material decomposition of PCCT data using multi-energy CT data sets acquired with an energy integrating detector (EID) for spectral calibration. We use a convolutional neural network (CNN) with a U-net structure and compare image domain and projection domain approaches to spectral correction and material decomposition. We trained our networks using noisy, spectrally distorted PCD projections as input data while the labels were derived from multi-energy EID data. For this study, we have scanned: 1) phantoms containing vials of iodine and calcium in water and 2) mice injected with an iodine-based liposomal contrast agent. Our results show that the image-based approach corrects best for spectral distortions and provides the lowest errors in material maps (RMSE in iodine: 0.34 mg/mL) compared with the projection-based approach (0.74 mg/mL) and image domain decomposition without correction (2.67 mg/mL). Both DL methods are, however, affected by a loss of spatial resolution (from 3 lp/mm in EID labels to ~2.2 lp/mm in corrected reconstructions). In summary, we demonstrate that multi-energy EID acquisitions can provide training data for DL-based spectral distortion correction. This data-driven correction does not require intimate knowledge of the spectral response or spectral distortions of the PCD. While the benefits of photon counting (PC) CT are significant, the performance of current PCDs is limited by physical effects distorting the spectral response. In this work, we examine a deep learning (DL) approach for spectral correction and material decomposition of PCCT data using multi-energy CT data sets acquired with an energy integrating detector (EID) for spectral calibration. We use a convolutional neural network (CNN) with a U-net structure and compare image domain and projection domain approaches to spectral correction and material decomposition. We trained our networks using noisy, spectrally distorted PCD projections as input data while the labels were derived from multi-energy EID data. For this study, we have scanned: 1) phantoms containing vials of iodine and calcium in water and 2) mice injected with an iodine-based liposomal contrast agent. Our results show that the image-based approach corrects best for spectral distortions and provides the lowest errors in material maps (RMSE in iodine: 0.34 mg/mL) compared with the projection-based approach (0.74 mg/mL) and image domain decomposition without correction (2.67 mg/mL). Both DL methods are, however, affected by a loss of spatial resolution (from 3 lp/mm in EID labels to ~2.2 lp/mm in corrected reconstructions). In summary, we demonstrate that multi-energy EID acquisitions can provide training data for DL-based spectral distortion correction. This data-driven correction does not require intimate knowledge of the spectral response or spectral distortions of the PCD.

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.

Background and purpose Late cardiac toxicity is a major side effect of radiation therapy (RT) for breast cancer. We developed and characterized a mouse model of radiation-induced heart disease that mimics the dose, fractionation, and beam arrangement of left breast and chest wall RT. Material and methods Female wild-type (C57BL6/J) and atherosclerosis-prone apolipoprotein E-deficient (ApoE−/−) mice (on a C57BL/6J background) on regular chow were treated with 2 Gy × 25 fractions of partial-heart irradiation via opposed tangential beams to the left chest wall. The changes in myocardial perfusion and cardiac function of C57BL/6J mice were examined by single-photon emission computed tomography (SPECT) and echocardiography, respectively. In addition to SPECT and echocardiography, the formation of calcified plaques and changes in cardiac function of ApoE−/− mice were examined by dual-energy microCT (DE-CT) and pressure–volume (PV) loop analysis, respectively. The development of myocardial fibrosis was examined by histopathology. Results Compared to unirradiated controls, irradiated C57BL/6J mice showed no significant changes by SPECT or echocardiography up to 18 months after 2 Gy × 25 partial-heart irradiation even though irradiated mice exhibited a modest increase in myocardial fibrosis. For ApoE−/− mice, 2 Gy × 25 partial-heart irradiation did not cause significant changes by SPECT, DE-CT, or echocardiography. However, PV loop analysis revealed a significant decrease in load-dependent systolic and diastolic function measures including cardiac output, dV/dtmax and dV/dt min 12 months after RT. Conclusions Following clinically relevant doses of partial-heart irradiation in C57BL/6J and ApoE−/− mice, assessment with noninvasive imaging modalities such as echocardiography, SPECT, and DE-CT yielded no evidence of decreased myocardial perfusion and cardiac dysfunction related to RT. However, invasive hemodynamic assessment with PV loop analysis indicated subtle, but significant, changes in cardiac function of irradiated ApoE−/− mice. PV loop analysis may be useful for future preclinical studies of radiation-induced heart disease, especially if subtle changes in cardiac function are expected.

There is potential to improve CT imaging by adding spectral capabilities as given by photon counting detectors (PCD). Here we describe and assess performance of a new spectral micro-CT prototype system using a CdTe-based PCD with 100-μm pixel size (model XC-Thor, made by Direct Conversion) benefitting from anti-coincidence corrections. To assess the PCD in terms of spectral response, a Ba-133 nuclear source was scanned using full spectrum scanning by sweeping thresholds with 2 keV increments. In a different experiment, we used small vials containing water, iodine (I), gadolinium (Gd), and gold (Au) placed on the surface of the detector and acquired X-ray data in full spectrum mode to verify that the PCD threshold positions yielded the expected changes in contrast around the K edges of these elements. Detector performance was assessed using micro-CT phantoms and during cancer experiments in mice injected with nanoparticle (NP)-based contrast agents. Both phantom and 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 decomposition method was used. The system is capable of high resolution (11.9 lp/mm, 10% MTF) tomographic imaging. Despite the anti-coincidence corrections, the spectral performance of the PCD is, however, not perfect, and it seems to be affected mostly at lower keVs, making accurate iodine decomposition challenging. Our cancer imaging results illustrate that our spectral micro-CT can benefit both nanotechnology and cancer research by providing an imaging method that can help test/optimize various nanoparticle for theranostics.

The maturation of photon-counting detector (PCD) technology promises to enhance routine CT applications with high-fidelity spectral information, transforming X-ray CT into a functional and molecular imaging modality, while maintaining the high spatial resolution, fast scanning times, and relatively low cost. We present our novel dual source pre-clinical micro-CT prototype system that combines a photon counting detector (PCD) and an energy integrating detector (EID) in a single system. We compare performance for PCD-only with combined hybrid (i.e. PCD and EID) CT imaging with equal dose. All CT data sets (EID-only, PCD-only, and hybrid) were reconstructed using our iterative, multi-channel algorithm based on the split Bregman method and regularization with rank-sparse kernel regression. We used a post-reconstruction decomposition method with iodine (I), gold (Au), gadolinium (Gd), and calcium (Ca) basis functions. Performance was assessed using micro-CT phantoms. Our results show the spatial resolution of the PCD-only and the hybrid reconstructions were similar and slightly better than for EID-only reconstruction with ~3.5 lp/mm (10% MTF). The NPS of the hybrid reconstruction was similar to PCD-only reconstruction. By co-reconstructing EID and PCD data, we achieved better image quality in the material decomposition but marginal differences in terms of concentration accuracy. Hybrid spectral micro- CT can benefit both nanotechnology and cancer research by providing imaging that can help test and optimize various NPs for theranostics.

Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects, the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

Small animal imaging has become essential in evaluating new cancer therapies as they are translated from the preclinical to clinical domain. However, preclinical imaging faces unique challenges that emphasize the gap between mouse and man. One example is the difference in breathing patterns and breath-holding ability, which can dramatically affect tumor burden assessment in lung tissue. As part of a co-clinical trial studying immunotherapy and radiotherapy in sarcomas, we are using micro-CT of the lungs to detect and measure metastases as a metric of disease progression. To effectively utilize metastatic disease detection as a metric of progression, we have addressed the impact of respiratory gating during micro-CT acquisition on improving lung tumor detection and volume quantitation. Accuracy and precision of lung tumor measurements with and without respiratory gating were studied by performing experiments with in vivo images, simulations, and a pocket phantom. When performing test-retest studies in vivo, the variance in volume calculations was 5.9% in gated images and 15.8% in non-gated images, compared to 2.9% in post-mortem images. Sensitivity of detection was examined in images with simulated tumors, demonstrating that reliable sensitivity (true positive rate (TPR) ≥ 90%) was achievable down to 1.0 mm3 lesions with respiratory gating, but was limited to ≥ 8.0 mm3 in non-gated images. Finally, a clinically-inspired “pocket phantom” was used during in vivo mouse scanning to aid in refining and assessing the gating protocols. Application of respiratory gating techniques reduced variance of repeated volume measurements and significantly improved the accuracy of tumor volume quantitation in vivo.

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 vivocardiac 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. Projection sampling was performed with 10 ms temporal resolution, allowing the reconstruction of 10 cardiac phases at each of 4 energies (40, 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 firstin 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 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 thein vivodata. 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.

Small animal imaging is essential in building a bridge from basic science to the clinic by providing the confidence necessary to move new cancer therapies to patients. However, there is considerable variability in preclinical imaging, including tumor volume estimations based on tumor segmentation procedures which can be clearly user-biased. Our group is engaged in developing quantitative imaging methods which will be applied in the preclinical arm of a co-clinical trial studying synergy between anti-PD-1 treatment and radiotherapy using a genetically engineered mouse model of soft tissue sarcoma. This study focuses on a convolutional neural network (CNN)-based method for automatic tumor segmentation based on multimodal MRI images, i.e. T1 weighted, T2 weighted and T1 weighted with contrast agent. Our images were acquired on a 7.0 T Bruker Biospec small animal MRI scanner. Preliminary results show that our U-net structure and 3D patch-wise approach using both Dice and cross entropy loss functions delivers strong segmentation results. We have also compared single performance using only T2 weighted versus multimodal MR images for CNN segmentation. Our results showthat Dice similarity coefficient were higher when using multimodal versus single T2 weighted data (0.84 ± 0.05 and 0.81 ± 0.03). In conclusion, we successfully established a segmentation method for preclinical MR sarcoma data based on deep learning. This approach has the advantage of reducing user bias in tumor segmentation and improving the accuracy and precision of tumor volume estimations for co-clinical cancer trials.

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.

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 [Formula: see text] of [Formula: see text] , a thickness of 3 mm, and four energy thresholds. Due to the PCD’s limited size ([Formula: see text]), our system uses a translate-rotate projection acquisition strategy to cover a field of view relevant for preclinical imaging ([Formula: see text]). 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 ([Formula: see text] voxels) and high-resolution energy-integrating detector data ([Formula: see text] 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.

Spectral CT using photon counting x-ray detectors (PCXDs) can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. PCXDs are especially suited for imaging Kedge contrast agents, revealing the spatial distribution of select imaging probes through quantitative material decomposition. To further advance the field, there is a clear and continuing need to develop PCXD hardware and software as part of a new generation of spectral CT imaging systems. Our group specializes in the development of preclinical microCT systems and of novel imaging probes based on K-edge materials. Toward this goal, we have now developed a prototype spectral micro-CT system with a PCXD produced by DxRay. This CZT-based PCXD has 16x16 pixels, each with a size of 0.5 x 0.5 mm, a thickness of 3 mm, and 4 configurable energy thresholds. The detector is thus only 8 mm x 8 mm in size. Due to the limited size of this detector tile, we have implemented a translate-rotate micro-CT system (i.e. a 2nd generation scanner). In this paper we summarize considerable efforts which went into compensating for dead pixels and for pixels with non-linear responses to prevent artifacts in the CT reconstruction results. We also present spectral response measurements for the detector and the results of both phantom and animal experiments with iodine- and gold-based contrast agents. The results confirm our ability to sample and reconstruct tomographic images, but also show that the PCXD prototype has limitations in imaging iodine.

Advances in CT hardware have propelled the development of novel CT contrast agents. Combined with the spectral capabilities of X-ray CT, molecular imaging is possible using multiple heavy-metal contrast agents. Nanoparticle platforms make particularly attractive agents because of (1) their ability to carry a large payload of imaging moieties, and (2) their ease of surface modification to facilitate molecular targeting. While several novel imaging moieties based on high atomic number elements are being explored, iodine (I) and gadolinium (Gd) are particularly attractive because they are already in clinical use. In this work, we investigate the feasibility for in vivo discrimination of iodine and gadolinium nanoparticles using dual energy micro-CT. Phantom experiments were performed to measure the CT enhancement for I and Gd over a range of voltages from 40 to 80 kVp using a dual-source micro-CT system with energy integrating detectors having cesium iodide scintillators. The two voltages that provide maximum discrimination between I and Gd were determined to be 50 kVp with Cu filtration and 40 kVp without any filtration. Serial dilutions of I and Gd agents were imaged to determine detection sensitivity using the optimal acquisition parameters. Next, an in vivo longitudinal small animal study was performed using Liposomal I (Lip-I) and Liposomal Gd (Lip-Gd) nanoparticles. The mouse was intravenously administered Lip-Gd and imaged within 1 h post-contrast to visualize Gd in the vascular compartment. The animal was reimaged at 72 h post-contrast with dual-energy micro-CT at 40 kVp and 50 kVp to visualize the accumulation of Lip-Gd in the liver and spleen. Immediately thereafter, the animal was intravenously administered Lip-I and re-imaged. The dual energy sets were used to estimate the concentrations of Gd and I via a two-material decomposition with a non-negativity constraint. The phantom results indicated that the relative contrast enhancement per mg/ml of I to Gd was 0.85 at 40 kVp and 1.79 at 50 kVp. According to the Rose criterion (CNR

Dual energy (DE) micro-CT shows great potential to provide accurate tissue 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. We have previously proposed optimal in vivo DE micro-CT methods for imaging using iodinated and gold nanoparticles. However, in vivo studies are limited in spatial resolution due to constraints in sampling time and radiation dose. Ex vivo dual energy imaging can provide much higher resolution and can serve as a validation of in vivo studies. Our study proposes multiscale in vivo and ex vivo DE micro-CT of the same subjects using two in-house developed micro-CT systems. We use a dual source micro-CT system to scan a mouse that has been injected with both iodinated and gold nanoparticles for in vivo DE scanning at 63 micron resolution. The same mouse is then scanned ex vivo with DE on a separate single source micro-CT system at a spatial resolution of 22 microns. We perform reconstructions using filtered back projection followed by noise reduction via joint bilateral filtration. A dynamic flat field correction method has been applied on the ex vivo micro-CT data to correct for image artifacts. A DE post-reconstruction decomposition is used to create iodine and gold material maps which are used to measure accumulation of contrast agent within the body. We evaluate challenges associated with each imaging methodology. Our results compare image quality and material maps. Overall, our methods represent a substantial tool for multiscale DE micro-CT imaging using wellcharacterized contrast agents and serving various applications in biological research.

Spectral CT can provide accurate tissue composition measurements by utilizing the energy dependence of x-ray attenuation in different materials. We have introduced image reconstruction and material decomposition algorithms for multi-energy CT data acquired either with energy integrating detectors (EID) or photon counting detectors (PCD); however, material decomposition is an ill-posed problem due to the potential overlap of spectral measurements and to noise. Recently, convolutional neural networks (CNN) have generated excitement in the field of machine learning and computer vision. The goal of this work is to develop CNN-based methods for material decomposition in spectral CT. The CNN for decomposition had a U-net structure and was trained with either five-energy PCD-CT or DE-CT. As targets for training, we used simulated phantoms constructed from random combinations of water and contrast agents (iodine, barium, and calcium for five-energy PCD-CT; iodine and gold for DE EID-based CT). The experimentally measured sensitivity matrix values for iodine, barium, and calcium or iodine and gold were used to recreate the CT images corresponding to both PCD and DE-CT cases. These CT images were used to train CNNs to generate material maps at each pixel location. After training, we tested the CNNs by applying them to experimentally acquired DE-EID and PCD-based micro-CT data in mice. The predicted material maps were compared to the absolute truth in simulations and to sensitivity-based decompositions for the in vivo mouse data. The CNN-based decomposition provided higher accuracy and lower noise. In conclusion, our U-net performed a more robust spectral micro-CT decomposition because it inherently better exploits spatial and spectral correlations.

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 minutes, 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 less 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 (LV) volume estimation. These methods promise low-dose imaging with a wide range of preclinical applications in cardiac imaging.

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.

In designing co-clinical cancer studies, preclinical imaging brings unique challenges that emphasize the gap between man and mouse. Our group is developing quantitative imaging methods for the preclinical arm of a co-clinical trial studying immunotherapy and radiotherapy in a soft tissue sarcoma model. In line with treatment for patients enrolled in the clinical trial SU2C-SARC032, primary mouse sarcomas are imaged with multi-contrast micro-MRI (T1 weighted, T2 weighted, and T1 with contrast) before and after immune checkpoint inhibition and pre-operative radiation therapy. Similar to the patients, after surgery the mice will be screened for lung metastases with micro-CT using respiratory gating. A systems evaluation was undertaken to establish a quantitative baseline for both the MR and micro-CT systems against which others systems might be compared. We have constructed imaging protocols which provide clinically-relevant resolution and contrast in a genetically engineered mouse model of sarcoma. We have employed tools in 3D Slicer for semi-automated segmentation of both MR and micro-CT images to measure tumor volumes efficiently and reliably in a large number of animals. Assessment of tumor burden in the resulting images was precise, repeatable, and reproducible. Furthermore, we have implemented a publicly accessible platform for sharing imaging data collected during the study, as well as protocols, supporting information, and data analyses. In doing so, we aim to improve the clinical relevance of small animal imaging and begin establishing standards for preclinical imaging of tumors from the perspective of a co-clinical trial.

There are a limited number of methods to guide and confirm the placement of a peripherally inserted central catheter (PICC) at the cavoatrial junction. The aim of this study was to design, test and validate a dual-wavelength, diode laser-based, single optical fiber instrument that would accurately confirm PICC tip location at the cavoatrial junction of an animal heart, in vivo. This was accomplished by inserting the optical fiber into a PICC and ratiometrically comparing simultaneous visible and near-infrared reflection intensities of venous and atrial tissues found near the cavoatrial junction. The system was successful in placing the PICC line tip within 5 mm of the cavoatrial junction.

Background Initial catheter-based renal sympathetic denervation (RSD) studies demonstrated promising results in showing a significant reduction of blood pressure, while recent data were less successful. As an alternative approach, the objective of this study was to evaluate the feasibility of using magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) to perform RSD in a porcine model. Methods An intravascular fiber optic temperature probe was used to confirm energy delivery during MRgHIFU. This technique was evaluated both in a vascular phantom and in a normotensive pig model. Five animals underwent unilateral RSD using MRgHIFU, and both safety and efficacy were assessed. MRI was used to evaluate the acoustic window, target sonications, monitor the near-field treatment region using MR thermometry imaging, and assess the status of tissues post-procedure. An intravascular fiber optic temperature probe verified energy delivery. Animals were sacrificed 6 to 9 days post-treatment, and pathological analysis was performed. The norepinephrine present in the kidney medulla was assessed post-mortem. Results All animals tolerated the procedure well with no observed complications. The fiber optic temperature probe placed in the target renal artery confirmed energy delivery during MRgHIFU, measuring larger temperature rises when the MRgHIFU beam location was focused closer to the tip of the probe. Following ablation, a significant reduction (p = 0.04) of cross-sectional area of nerve bundles between the treated and untreated renal arteries was observed in all of the animals with treated nerves presenting increased cellular infiltrate and fibrosis. A reduction of norepinephrine (p = 0.14) in the kidney medulla tissue was also observed. There was no indication of tissue damage in arterial walls. Conclusions Performing renal denervation non-invasively with MRgHIFU was shown to be both safe and effective as determined by norepinephrine levels in a porcine model. This approach may be a promising alternative to catheter-based strategies.