X-ray Angiography Research Articles

Adaptation of a channelized Hotelling observer model to accommodate anthropomorphic backgrounds and moving test objects in X-ray angiography.

Prior implementations of the channelized Hotelling observer (CHO) model have succeeded in assessing the performance of X-ray angiography systems under a variety of imaging conditions. However, often times these conditions do not resemble those present in routine clinical imaging scenarios, such as having complex anthropomorphic backgrounds in conjunction with moving test objects. This work builds up on prior established CHO methods and introduces a new approach to switch from the already established "multiple-sample" CHO implementation to a "single-sample" technique. The proposed implementation enables the inclusion of moving test objects upon nonuniform backgrounds by allowing only a single sample to represent the test object present condition that is to be used within the statistical test to estimate the detectability index. To assess the proposed method, two image data sets were acquired with a clinical X-ray angiography system. The first set consisted of a uniform background in combination with static test objects while the second consisted of an anthropomorphic chest phantom in conjunction with moving test objects. The first set was used to validate the proposed approach against the multiple-sample method while the second was used to assess the feasibility of the proposed method under a variety of imaging conditions, including seven object sizes and seven detector target dose (DTD)levels. For the uniform background data set, considering all detectability indices greater or equal than 1, the ratio between the detectability indices of the proposed single-sample approach versus the multiple-sample method was 0.997 ± 0.056 (range 0.884-1.159). The average single-direction width of the 95% confidence intervals (CIs) of the detectability index estimates for the multiple-sample method was 0.38 ± 0.43 (range 0.03-2.20). For the single-sample approach, the average width was 2.52 ± 0.63 (range 1.11-5.44). For the anthropomorphic background image set, the results were consistent with classical quantum-limited signal-to-noise ratio (SNR) theory. The magnitude of the detectability indices varied predictably with changes in both object size and DTD, with the highest value associated with the highest dose and the largest object size. Additionally, the proposed method was able to capture differences in the imaging performance for a given test object across the field of view, which was associated with the attenuation levels provided by different features of the anthropomorphicbackground. A new single-sample variant of the CHO model to assess the performance of X-ray angiography imaging systems is proposed. The new approach is consistent with quantum-limited image quality theory and with a standard implementation of the CHO model. The proposed method enables the assessment of moving test objects in combination with complex, nonuniform image backgrounds, thereby opening the possibility to assess imaging conditions which more closely resemble those used in clinicalcare.

Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation.

Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various contrast variations are critical and challenging, and most existing methods focus only on achieving one or two of these aspects. In this paper, we present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach. Specifically, we compute a multiscale affinity field for each pixel, capturing its semantic relationships with neighboring pixels in the predicted mask image. This field represents the local geometry of vessel segments of different sizes, allowing us to learn spatial- and scale-aware adaptive weights to strengthen vessel features. We evaluate our AFN on four different types of vascular datasets: X-ray angiography coronary vessel dataset (XCAD), portal vein dataset (PV), digital subtraction angiography cerebrovascular vessel dataset (DSA) and retinal vessel dataset (DRIVE). Extensive experimental results demonstrate that our AFN outperforms the state-of-the-art methods in terms of both higher accuracy and topological metrics, while also being more robust to various contrast changes. The source code of this work is available at https://github.com/TY-Shi/AFN.

Unsupervised synthesis of realistic coronary artery X-ray angiogram.

Medical image analysis suffers from a sparsity of annotated data necessary in learning-based models. Cardiorespiratory simulators have been developed to counter the lack of data. However, the resulting data often lack realism. Hence, the proposed method aims to synthesize realistic and fully customizable angiograms of coronary arteries for the training of learning-based biomedical tasks, for cardiologists performing interventions, and for cardiologist trainees. 3D models of coronary arteries are generated with a fully customizable realistic cardiorespiratory simulator. The transfer of X-ray angiography style to simulator-generated images is performed using a new vessel-specific adaptation of the CycleGAN model. The CycleGAN model is paired with a vesselness-based loss function that is designed as a vessel-specific structural integrity constraint. Validation is performed both on the style and on the preservation of the shape of the arteries of the images. The results show a PSNR of 14.125, an SSIM of 0.898, and an overlapping of 89.5% using the Dice coefficient. We proposed a novel fluoroscopy-based style transfer method for the enhancement of the realism of simulated coronary artery angiograms. The results show that the proposed model is capable of accurately transferring the style of X-ray angiograms to the simulations while keeping the integrity of the structures of interest (i.e., the topology of the coronary arteries).

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

IntroductionWe previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported.MethodsRetrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50–99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS – 0 -100 points) - previously developed and published - were measured.Results123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09–0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87–96), similar to the previously obtained value in the training dataset.Conclusionthe AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.

The effectiveness of MRI and CT with intravenous contrast in the assessment of carotid chemodectoma at the preoperative stage and in dynamics after surgery

Background. Carotid chemodectoma is rare, slow growing, richly vascularized lesion. It is a single unilateral or bilateral tumor, localized mainly in the area of the carotid triangle. Different forms and bilateral cases are also known. A complex of radiological studies, including MRI and CT with intravenous contrast, can help in determining the surgical treatment strategy, as well as treatment monitoring. Objective. To determine the role of MRI and CT with intravenous contrast in the assessment of carotid chemodectoma at the preoperative stage and at post-surgery monitoring. Materials and Methods. We analyzed the results of a comprehensive radiological examination of 12 patients with suspected carotid chemodectoma, including MRI and MSCT with intravenous contrast, and X-ray angiography. All patients got operated, the results were confirmed by histological and immunohistochemical study. Results. Multislice computed tomography, supplemented by CT angiography, allows visualizing chemodectoma, determining its size, shape, structure, and relations with the nearby vessels. In addition to size, shape, structure of chemodectoma MRI can provide a detailed picture of the internal vascular network, the walls of adjacent vascular structures, as well as the soft tissue structures of the peripharyngeal area, which is helpful for vascular surgeons in surgical intervention. It was not possible to determine the histogenesis of the tumor according to the methods of radiation diagnostics. Conclusions. In order to determine the option of surgical treatment of carotid chemodectomas and reduce the risk of intraoperative complications at the preoperative stage, it is necessary to carry out the entire spectrum of radiation diagnostic methods (including ultrasound, MSCT, MRI and X-ray angiography).

An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction.

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".

Preclinical Evaluation of an Everolimus-Eluting Bioresorbable Vascular Scaffold Via a Long-Term Rabbit Iliac Artery Model.

Biodegradable poly (l-lactic acid) (PLLA), a bio safe polymer with a large elastic modulus, is widely used in biodegradable medical devices. However, because of its poor mechanical properties, a PLLA strut must be made twice as thick as a metal strut for adequate blood vessel support. Therefore, the mechanical properties of a drug-eluting metal-based stents (MBS) and a bioresorbable vascular scaffolds (BVS) were evaluated and their safety and efficacy were examined via a long-term rabbit iliac artery model. The surface morphologies of the MBSs and BVSs were investigated via optical and scanning electron microscopy. An everolimus-eluting (EE) BVS or an EE-MBS was implanted into rabbit iliac arteries at a 1.1:1 stent-to-artery ratio. Twelve months afterward, stented iliac arteries from each group were analyzed via X-ray angiography, optical coherence tomography (OCT), and histopathologic evaluation. Surface morphology analysis of the EE coating on the MBS confirmed that it was uniform and very thin (4.7μm). Comparison of the mechanical properties of the EE-MBS and EE-BVS showed that the latter outperformed the former in all aspects (radial force (2.75 vs. 0.162N/mm), foreshortening (0.24% vs. 1.9%), flexibility (0.52 vs. 0.19N), and recoil (3.2% vs. 6.3%). At all time points, the percent area restenosis was increased in the EE-BVS group compared to the EE-MBS group. The OCT and histopathological analyses indicate no significant changes in strut thickness. BVSs with thinner struts and shorter resorption times should be developed. A comparable long-term safety/efficacy evaluation after complete absorption of BVSs should be conducted.

Distance transform learning for structural and functional analysis of coronary artery from dual-view angiography

The structural and functional analysis of coronary arteries in X-ray angiography (XRA) images has great clinical importance for the intraoperative treatment of coronary artery disease. However, due to the complexity of image surroundings, the non-intuition of functional information, and the complex relationships between structure and function, the structural and functional analysis in XRA images is a challenging task. To address these problems, we are proposing an efficient framework, termed AngioSFA. It can effectively associate structural analysis with functional analysis. The AngioSFA mainly consists of a distance transform segmentation network (DTSN) for structural analysis and a dual-view sequential learning network (DSLN) for functional analysis. Specifically, DTSN exploits distance transform in the encoder–decoder architecture to predict the distance map of the artery structure. It considers the geometric property of coronary artery structure to preserve a proper shape prototype. DSLN exploits a dual-view temporal convolutional network to learn the non-intuitive physiological knowledge related to the functional significance from the radius sequences extracted from dual-view XRA images. Furthermore, a commonality sharing module is designed to map the latent embeddings of radius sequences into a shared latent space through orthogonal mapping. Moreover, we introduce a dual-view interaction module to explore the intra-view attentions and the inter-view correlations among the anatomical features of coronary arteries. Extensive experiments on two clinical datasets show that AngioSFA achieves an overall dice of 91.06% for structural analysis and an overall precision of 89.96% for functional analysis. The experimental results and the discussions demonstrate that AngioSFA has a great potential for the efficient intraoperative treatment of coronary artery disease.

Progressive Perception Learning for Main Coronary Segmentation in X-ray Angiography.

Main coronary segmentation from the X-ray angiography images is important for the computer-aided diagnosis and treatment of coronary disease. However, it confronts the challenge at three different image granularities (the semantic, surrounding, and local levels). The challenge includes the semantic confusion between the main and collateral vessels, low contrast between the foreground vessel and background surroundings, and local ambiguity near the vessel boundaries. The traditional hand-crafted feature-based methods may be insufficient because they may lack the semantic relationship information and may not distinguish the main and collateral vessels. The existing deep learning-based methods seem to have issues due to the deficiency in the long-distance semantic relationship capture, the foreground and background interference adaptability, and the boundary detail information preservation. To solve the main coronary segmentation challenge, we propose the progressive perception learning (PPL) framework to inspect these three different image granularities. Specifically, the PPL contains the context, interference, and boundary perception modules. The context perception is designed to focus on the main coronary vessel based on the semantic dependence capture among different coronary segments. The interference perception is designed to purify the feature maps based on the foreground vessel enhancement and background artifact suppression. The boundary perception is designed to highlight the boundary details based on boundary feature extraction through the intersection between the foreground and background predictions. Extensive experiments on 1085 subjects show that the PPL is effective (e.g., the overall Dice is greater than 95%), and superior to thirteen state-of-the-art coronary segmentation methods.

Quantitative assessment of angioplasty-induced vascular inflammation with 19F cardiovascular magnetic resonance imaging

BackgroundMacrophages play a pivotal role in vascular inflammation and predict cardiovascular complications. Fluorine-19 magnetic resonance imaging (19F MRI) with intravenously applied perfluorocarbon allows a background-free direct quantification of macrophage abundance in experimental vascular disease models in mice. Recently, perfluorooctyl bromide-nanoemulsion (PFOB-NE) was applied to effectively image macrophage infiltration in a pig model of myocardial infarction using clinical MRI scanners. In the present proof-of-concept approach, we aimed to non-invasively image monocyte/macrophage infiltration in response to carotid artery angioplasty in pigs using 19F MRI to assess early inflammatory response to mechanical injury. MethodsIn eight minipigs, two different types of vascular injury were conducted: a mild injury employing balloon oversize angioplasty only (BA, n = 4) and a severe injury provoked by BA in combination with endothelial denudation (BA + ECDN, n = 4). PFOB-NE was administered intravenously three days after injury followed by 1H and 19F MRI to assess vascular inflammatory burden at day six. Vascular response to mechanical injury was validated using X-ray angiography, intravascular ultrasound and immunohistology in at least 10 segments per carotid artery. ResultsAngioplasty was successfully induced in all eight pigs. Response to injury was characterized by positive remodeling with predominantly adventitial wall thickening and concomitant infiltration of monocytes/macrophages. No severe adverse reactions were observed following PFOB-NE administration. In vivo 19F signals were only detected in the four pigs following BA + ECDN with a robust signal-to-noise ratio (SNR) of 14.7 ± 4.8. Ex vivo analysis revealed a linear correlation of 19F SNR to local monocyte/macrophage cell density. Minimum detection limit of infiltrated monocytes/macrophages was estimated at approximately 410 cells/mm2. ConclusionsIn this proof-of-concept study, 19F MRI enabled quantification of monocyte/macrophage infiltration after vascular injury with sufficient sensitivity. This may provide the opportunity to non-invasively monitor vascular inflammation with MRI in patients after angioplasty or even in atherosclerotic plaques.

Evaluation of the Angiosome Concept Using Near-Infrared Fluorescence Imaging with Indocyanine Green

The angiosome concept is defined as the anatomical territory of a source artery within all tissue layers. When applying this theory in vascular surgery, direct revascularization (DR) is preferred to achieve increased blood flow toward the targeted angiosome of the foot in patients with lower extremity arterial disease (LEAD). This study evaluates the applicability of the angiosome concept using quantified near-infrared (NIR) fluorescence imaging with indocyanine green (ICG). This study included patients undergoing an endovascular- or surgical revascularization of the leg between January 2019 and December 2021. Preinterventional and postinterventional ICG NIR fluorescence imaging was performed. Three angiosomes on the dorsum of the foot were determined: the posterior tibial artery (hallux), the anterior tibial artery (dorsum of the foot) and the combined angiosome (second to fifth digit). The angiosomes were classified from the electronic patient records and the degree of collateralization was classified based on preprocedural computed tomography angiography and/or X-ray angiography. Fluorescence intensity was quantified in all angiosomes. A subgroup analysis based on endovascular or surgical revascularized angiosomes, and within critical limb threatening ischemia (CLTI) patients was performed. ICG NIR fluorescence measurements were obtained in 52 patients (54 limbs) including a total of 157 angiosomes (121 DR and 36 indirect revascularizations [IR]). A significant improvement of all perfusion parameters in both the directly and indirectly revascularized angiosomes was found (P-values between <0.001-0.007). Within the indirectly revascularized angiosomes, 90.6% of the scored collaterals were classified as significant. When comparing the percentual change in perfusion parameters between the directly and indirectly revascularized angiosomes, no significant difference was seen in all perfusion parameters (P-values between 0.253 and 0.881). Similar results were shown in the CLTI patients subgroup analysis, displaying a significant improvement of perfusion parameters in both the direct and indirect angiosome groups (P-values between <0.001 and 0.007), and no significant difference when comparing the percentual parameter improvement between both angiosome groups (P-values between 0.134 and 0.359). Furthermore, no significant differences were observed when comparing percentual changes of perfusion parameters in directly and indirectly revascularized angiosomes for both endovascular and surgical interventions (P-values between 0.053 and 0.899). This study proves that both DR and IR of an angiosome leads to an improvement of perfusion. This suggests that interventional strategies should not only focus on creating in-line flow to the supplying angiosome. One can argue that the angiosome concept is not applicable in patients with LEAD.

Coronary artery stenosis detection via proposal-shifted spatial-temporal transformer in X-ray angiography

Accurate detection of coronary artery stenosis in X-ray angiography (XRA) images is crucial for the diagnosis and treatment of coronary artery disease. However, stenosis detection remains a challenging task due to complicated vascular structures, poor imaging quality, and fickle lesions. While devoted to accurate stenosis detection, most methods are inefficient in the exploitation of spatio-temporal information of XRA sequences, leading to a limited performance on the task. To overcome the problem, we propose a new stenosis detection framework based on a Transformer-based module to aggregate proposal-level spatio-temporal features. In the module, proposal-shifted spatio-temporal tokenization (PSSTT) scheme is devised to gather spatio-temporal region-of-interest (RoI) features for obtaining visual tokens within a local window. Then, the Transformer-based feature aggregation (TFA) network takes the tokens as the inputs to enhance the RoI features by learning the long-range spatio-temporal context for final stenosis prediction. The effectiveness of our method was validated by conducting qualitative and quantitative experiments on 233 XRA sequences of coronary artery. Our method achieves a high F1 score of 90.88%, outperforming other 15 state-of-the-art detection methods. It demonstrates that our method can perform accurate stenosis detection from XRA images due to the strong ability to aggregate spatio-temporal features.

Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks

Objective. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. Approach. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D. A many-to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. Main results. Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm. Significance. This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions.

Acute Pulmonary Embolism with Multiple Septic Emboli and Pulmonary Infarcts in an IV Drug Abuser with Right-Sided Endocarditis: A Case Report

This case report describes the imaging findings of septic pulmonary embolism (SPE) in a 30-year-old male with a history of intravenous drug abuse for one year. The patient initially presented with high grade fever, shortness of breath, and cough in the emergency department of our institution. The patient was evaluated by chest X-ray, blood cultures, echocardiography, and CT pulmonary angiography which confirmed the diagnosis of SPE, and the causative organism was confirmed to be Staphylococcus aureus. In this case report, we discuss the Chest X-ray findings, and characteristic CT imaging findings of SPE and describe the role of different imaging modalities in the timely diagnosis and management of this highly fatal condition.

Dispersion of Heterogeneous Medium in Pulsatile Blood Flow and Absolute Pulsatile Flow Velocity Quantification.

Heterogeneous medium enhanced angiogr- ams are key diagnostic tools in clinical practice; the associated hemodynamic information is crucial for diagnosing cardiovascular diseases. However, the dynamics of such medium in physiological blood flow are poorly understood. Herein, we report a previously unnoticed dispersion pattern, which is a universal phenomenon, of a medium in pulsatile blood flow. We present a physical theory for studying the dispersion of a steadily injected heterogeneous medium into a thin tubular blood vessel in which the blood flow is pulsatile. In a thin tubular blood vessel, we demonstrate that variations of concentration associated with the heterogeneous medium obey a one-dimensional advection diffusion equation, and the diffusion has limited effect whenever a short vascular segment is considered. A distinct feature of the distribution of the medium in the axial distance-time plane is a "dilation-retraction" pattern. The time evolution signals at different axial positions exhibit distinct concentration waveforms. A numerical scheme is proposed for exploiting this information to estimate the pulsatile velocity. Artificial data are adopted to validate the scheme. Real X-ray angiography is also analyzed to support our theory and method. The theory is applicable whenever imaging protocols involve a heterogeneous medium in pulsatile flow.

Centerline-supervision multi-task learning network for coronary angiography segmentation

With convolutional neural networks’ remarkable performance in computer vision, more and more studies are applying deep learning to vessel image segmentation tasks. This work focuses on the task of coronary X-ray angiography segmentation, which is critical in the diagnosis of cardiovascular disorders. For vascular segmentation in coronary X-ray angiography images, we propose a novel deep learning model based on the UNet backbone. We first equip a channel attention module in skip-connections to improve pixel-wise segmentation accuracy by emphasizing the effective channel in low-level features. A centerline auxiliary supervision module is also introduced at the network’s end to provide prior knowledge of vessel connectivity and thick vessels, utilizing the existing binary segmentation annotations efficaciously. Consequently, the network can devote more attention to pixels that ensure vessel tree connectivity and high confidence. Extensive experiments demonstrate the effectiveness of the two modules in improving the performance of the model. We compared our results to the recently proposed networks and revealed that these two modules can be added to other U-shaped networks to enhance performance. In our experiments, our method produced the best results in terms of sensitivity and dice score, with 82.48 and 85.28, respectively.

Experimental optimization of single-exposure dual-energy angiography with photon-counting x-ray detectors.

Photon-counting x-ray detectors may enable single-exposure dual-energy (DE) x-rayangiography. The purpose of this paper is to experimentally optimize the energy thresholds and tube voltage for single-exposure DE x-rayangiography. We optimized single-exposure DE x-ray angiography using the iodine signal-difference-to-noise ratio (SDNR) per root patient air kerma (κ) as a figureof merit. We measured the iodine SDNR by imaging an iodine stepwedge immersed in a water tank with a depth of 30cm in the direction of x-ray propagation. The stepwedge was imaged using tube voltages ranging from 90 to 150kV and a cadmium telluride (CdTe) x-ray detector with two energy bins and analog charge summing for charge sharing suppression. The energy threshold that separates the two energy bins was varied from approximately 35keV to approximately 75% of the maximum energy of the x-ray beam. Curve fitting was used to determine the threshold that maximized . The effect of scatter was determined from measurements of the scatter-to-primary ratios (SPRs) of the low-energy and high-energy images and a semi-empirical model of the relationship between SDNR and SPR. Using the optimal parameters, we imaged a phantom with vessel-simulating structures and backgroundclutter. The optimal energy thresholds increased monotonically from ∼50 to ∼85keV over the range of tube voltages considered. For tube voltages greater than 90kV, the optimal energy thresholds consistently allocated approximately two thirds of all detected primary photons to the low energy bin; this ratio was preserved without scatter. Consistent with prior modeling studies, increased monotonically with tube voltage from 90 to 150kV; at 150kV was approximately 38% higher than that at 90kV for an iodine area density of ∼50mg/cm2 . Scatter reduced SDNR by approximately 25% for SPRs of ∼1 and 0.4 in low-energy and high-energy images, respectively. Achieving optimal image quality in single-exposure DE angiography with photon-counting x-ray detectors will require high tube voltages (i.e.,>130kV) and, for thick patients, energy thresholds that allocate approximately two thirds of all primary photons to the low-energy image. Future work will compare the image quality of singe-exposure photon-counting and kV-switching approaches to DE x-rayangiography.

Incidence of Barotrauma in COVID-19 Patients Requiring Mechanical Ventilation: A Retrospective Study in a Community Hospital.

Background and aim Acute respiratory distress syndrome (ARDS) is a severe complication of COVID-19 and traditional ventilation strategies using ARDSNet protocol, including low tidal volumes, appear to cause barotrauma in COVID-19 patients at a higher rate than non-COVID-19 ARDS patients. The purpose of our study was to determine if COVID-19 patients with ARDS undergoing mechanical ventilation at St. Joseph's Medical Center (SJMC) developed barotrauma at a higher rate than non-COVID-19 ARDS patients. Methods and materials This study was a retrospective chart review of all patients admitted to critical care units at SJMC with COVID-19 infection and requiring mechanical ventilation from March 1, 2020 to September 30, 2020. The sample included adult patients (aged 18 and above) with the International Classification of Diseases (ICD) 10 code for COVID-19 (U07.1) and patients who were placed on mechanical ventilation for longer than 24 hours, from March 1, 2020 to September 30, 2020. Barotrauma was confirmed via radiographic imaging including chest X-ray,CT, or CT angiography (CTA). Results One hundred and forty COVID-19 patients underwent mechanical ventilation for longer than 24 hours from March 1, 2020 to September 30, 2020 at our facility. Twenty-six COVID-19 patients (18.6%) met our inclusion criteria (development of barotrauma during hospital admission) of which 25 patients (17.9%) underwent mechanical (invasive and/or non-invasive) ventilation prior to the development of barotrauma. Around 80% of the patients were on non-invasive mechanical ventilation prior to intubation and invasive mechanical ventilation. The categorical breakdown of barotrauma was as follows: pneumothorax 65.4%, subcutaneous emphysema 61.5%, pneumomediastinum 34.6%, and pneumoperitoneum 7.7%. None of the included patients had any previous history of documented barotrauma. Prior to the time of barotrauma, 17 patients were on volume control, seven were on pressure control, and one was not on mechanical ventilation. Of the 17 patients on volume control, only one patient was above the ARDSNet guideline of 6-8 mL/kg ideal body weight (IBW).In comparison to ARDS patients at SJMC in 2019, only two out of 28 patients (7.14%) developed barotrauma during mechanical ventilation. Conclusions Patients with COVID-19 who underwent mechanical ventilation developed barotrauma at a higher rate than traditional non-COVID-19 patients with ARDS.

Theoretical comparison of energy-resolved and digital-subtraction angiography.

X-ray coronary angiography is a sub-optimal vascular imaging technique because it cannot suppress un-enhanced anatomy that may obscure the visualization of coronary arterydisease. The purpose of this paper is to evaluate the theoretical image quality of energy-resolved x-ray angiography (ERA) implemented with spectroscopic x-ray detectors (SXDs), which may overcome limitations of x-ray coronaryangiography. We modeled the large-area signal-difference-to-noise (SDNR) of contrast-enhanced vasculature in ERA images and compared with that of digital-subtraction angiography (DSA), which served as a gold standard vascular imaging technique. To this end, we used calibrated numerical models of the response of cadmium telluride SXDs including the effects of charge sharing, electronic noise, and energy thresholding. Our models assumed zero scatter, no pulse pile up and small signals such that image contrast is approximately linear in the area density of contrast agents. For DSA, we similarly modeled x-ray detection by cesium iodide energy-integrating detectors using validated numerical models. For ERA, we investigated iodine and gadolinium (Gd) contrast agents, two-material and three-material decompositions, analog charge summing for charge sharing correction, and optimized image quality with respect to the tube voltage and energy thresholds assuming cadmium telluride SXDs with three energybins. Our analysis reveals that a three-material decomposition using iodine contrast agents will require x-ray exposures that are approximately 400 times those of DSA to achieve the same SDNR as DSA in coronary applications, and is therefore not feasible in a clinical setting. However, three-material decompositions with Gd contrast agents have the potential to provide SDNR that is ∼45% of that of DSA for matched patient air kerma. For two-material decompositions that suppress soft-tissue, ERA has the potential to produce images with SDNR that is 50%-75% of that of DSA for matched patient air kermas but lower levels of tube loading. Achieving these SDNR levels will require the use of analog charge summing for charge sharing correction, which increased SDNR by up to a factor of 1.7 depending on the contrast agent and whether or not a two-material or three-material decomposition wasassumed. We conclude that three-material ERA implemented with Gd contrast agents and two-material ERA implemented with either iodine or Gd contrast agents, should be investigated as alternatives to DSA in situations where motion artifacts preclude the use of DSA, such as in coronaryimaging.

The ex vivo on-ECMO amputated human limb model of atherosclerosis

Abstract Introduction The preclinical study of atherosclerosis has been traditionally centred around the use of small animal models, translating to large animal models prior to first-in-man studies. We propose to disrupt this paradigm by designing an ex vivo pump perfused “live” limb model, to enable the molecular targeting of atherosclerosis. Purpose To develop and test a novel experimental model of atherosclerosis, based on perfusion of an amputated human limb, to reduce time spent in translational pipelines and reliance on animal preclinical atherosclerotic research. Methods The novel model consists of taking a freshly amputated limb and incorporating it into an ex-situ pump-perfused bypass system (akin to ECMO) circulating warmed, oxygenated blood. A custom-made operating table was designed to allow the flow of venous blood back into the circuit. The outflow of the table was connected to an oxygenator followed by a pulsatile pump in series. A parallel circuit warms the system to 37°C using a thermocirculator. The circuit was connected to an outflow cannula sutured into the proximal end of the amputated limb artery, permitting the passage of an introducer sheath and guiding catheter for intravascular imaging and x-ray angiography (Figure 1A). The pump was set to provide an output of 15ml per stroke (to represent approximately 20% of clinical stroke volume), with an average pump rate of 70 strokes per minute (estimating a heart rate of 70 beats per minute). Thus, total pump output, or “cardiac output” to the limb was ∼1,050 ml/min. Regular monitoring was performed using arterial blood gas analysis, with correction of pH, oxygenation, haemoglobin, lactate and electrolytes. All participants provided written informed consent, and ethical permission was granted by the Imperial College Healthcare Tissue Bank (REC Wales 17/WA/0161; subcollection CAR_RK_17_070). Results The model has been successfully performed (n=3), maintaining oxygen saturations &amp;gt;99% for the length of perfusion (up to 6 hours). X-ray angiography (Figure 1B), intravascular ultrasound (Eagle Eye, Volcano, Philips; Figure 1C) and optical coherence tomography (Dragonfly Optis, Abbott Cardiovascular; Figure 1D) was performed. In one limb, indocyanine green, a near-infrared fluorescent dye (excitation 788/ emission 813nm) that localises to atherosclerotic plaque, was injected into the system (2 mg/kg) and left to circulate for 90-minutes. The arterial tissue (Figure 1E – posterior tibial [left] and fibular [right]) was then dissected, and fluorescence reflectance imaging performed at 790 nm on the extracted tissue (Figure 1F). This confirmed indocyanine green uptake in areas of calcific atherosclerotic plaque on intravascular ultrasound and optical coherence tomography. Conclusions This is the first demonstration of this novel ex vivo on ECMO “living” limb experimental model of atherosclerosis, which shows promise for future studies in translational interventional imaging and molecular targeting. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Wellcome Trust Clinical Research Fellowship