Cardiac Computed Tomography Angiography Images Research Articles

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography

Background and Objective:Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value. Methods:Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG. Results:The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from −27.7 to 18.7, outperforming MPGEcho, which ranged from −40.1 to 18.0. Conclusions:The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.

Cardiac computed tomography angiography for assessment of endothelial insufficiency of left atrial appendage disc-like occluder.

This study was performed to explore the diagnostic value of cardiac computed tomography angiography (CCTA) for endothelial insufficiency (EIS) of a left atrial appendage (LAA) disc-like occluder. Fifty-nine patients with nonvalvular atrial fibrillation who underwent placement of an LAA disc-like occluder (LAmbre; Lifetech Scientific) in our hospital were retrospectively analyzed. Patients who were found to have contrast agent entering the LAA at the 3-month postoperative CCTA examination underwent Hounsfield unit (HU) measurement of the LAA and construction of a three-dimensional(3D) model of the device for preliminary discernment between peri-device leakage (PDL) and EIS. These patients were then further examined by transesophageal echocardiography (TEE) to check for concordance with the computed tomography (CT) findings. According to the CT and TEE results, all patients were divided into the PDL group, total endothelialization group, and EIS group. The endothelial conditions and other implantation-related results were also tracked at the 6-month follow-up. All 59 patients underwent successful implantation of the LAmbre LAA closure device with no severe adverse events during the procedure. Thirty-five patients were found to have contrast agent entering the LAA at the 3-month postoperative CCTA follow-up. Based on the CT HU measurement and the 3D construction analysis results, these 35 patients were divided into the PDL group (19 patients) and the EIS group (16 patients). In the PDL group, the contrast agent infiltrated from the shoulder along the periphery of the occluder on two-dimensional (2D)CT images, and the 3D model showed a gap between the LAA and the device cover. However, the CCTA images of the other 16 patients in the EIS group showed that the contrast agent in the occluder on the 2D CTA images and 3D construction model confirmed the absence of a gap between the LAA and the device cover. TEE confirmed all of the CT results. The 6-month follow-up results showed that 14 of 19 patients in the EIS group achieved total endothelialization, whereas this number in the PDL group was only five of 19 patients. CCTA can replace TEE for examination of the endothelialization status, and patients with EIS have a higher chance of endothelialization than patients with PDL.

Multimodality Imaging Diagnosis in Infective Endocarditis.

Imaging is an important tool in the diagnosis and management of infective endocarditis (IE). Echocardiography is an essential examination, especially in native valve endocarditis (NVE), but its diagnostic accuracy is reduced in prosthetic valve endocarditis (PVE). The diagnostic ability is superior for transoesophageal echocardiography (TEE), but a negative test cannot exclude PVE. Both transthoracic echocardiography (TTE) and TEE can provide normal or inconclusive findings in up to 30% of cases, especially in patients with prosthetic devices. New advanced non-invasive imaging tests are increasingly used in the diagnosis of IE. Nuclear medicine imaging techniques have demonstrated their superiority over TEE for the diagnosis of PVE and cardiac implantable electronic device infective endocarditis (CIED-IE). Cardiac computed tomography angiography imaging is useful in PVE cases with inconclusive TTE and TEE investigations and for the evaluation of paravalvular complications. In the present review, imaging tools are described with their values and limitations for improving diagnosis in NVE, PVE and CIED-IE. Current knowledge about multimodality imaging approaches in IE and imaging methods to assess the local and distant complications of IE is also reviewed. Furthermore, a potential diagnostic work-up for different clinical scenarios is described. However, further studies are essential for refining diagnostic and management approaches in infective endocarditis, addressing limitations and optimizing advanced imaging techniques across different clinical scenarios.

Accuracy of 3D-Printed Models of Aortic Valves – a Comparative Analysis Between Planimetric and Photogrammetric Measurements

Abstract Background 3D printing has changed the paradigm of personalized medicine. Similarly to fingerprints, there are no two identical hearts; consequently, in cardiology, diagnosis and treatment, either medical, interventional or surgical, must be individualized according to the specific problem of a particular patient. The aim of this proof-of-concept study was to analyze two measurement methods, the planimetric and the photogrammetric method, in the process of creating a 3D-printed model from cardiac computed tomography angiography images and to evaluate the accuracy of an aortic valve anatomical model. Material and methods Cardiac computed tomography images, obtained from 20 patients with severe aortic stenosis, underwent stereolithographic reconstruction using 3D Slicer to create digital 3D models of the aortic valves. Serial measurements of six key elements of the aortic valvular apparatus were measured on the 3D model and compared to the measurements taken on the 2D computed tomography images. Results The differences between the two measurement methods were sub-millimetric in case of the left ventricular outflow tract and the sinotubular junction, and 1.386 mm for the left sinus of Valsalva (p = 0.0412), 0.3476 mm for the right sinus of Valsalva (p = 0.1874), and 0.6905 mm for the non-coronary Valsalva sinuses (p = 0.1353). Sinus heights were also similar, with a difference of 0.0119 mm (p = 0.6521). Conclusion In this study, the results of digital photogrammetry were superimposable to those of computed tomography scan measurements. The accuracy of each 3D-printed model depends on geometric complexity, the level of training of the personnel, and on the resources of each 3D printing laboratory.

Relationship between epicardial adipose tissue attenuation and coronary artery disease in type 2 diabetes mellitus patients.

High epicardial adipose tissue (EAT) attenuation is a key characteristic of adipose tissue dysfunction and associated with coronary artery disease (CAD). As little is known about the modulation of EAT attenuation by metabolic disorders, we investigated the association between EAT attenuation and CAD risk factors, CAD presence and CAD severity in type 2 diabetes mellitus (T2DM) patients. We included 276 inpatients with T2DM and 305 control patients with normal glucose metabolism (NGM), who underwent cardiac computed tomography angiography (CCTA) and coronary artery calcium (CAC) scoring. EAT attenuation and volume were evaluated by contrast-enhanced CCTA image analysis. Furthermore, segment stenosis scores (SSSs) of the left main coronary artery (LMCA), left anterior descending artery (LAD), left circumflex artery (LCX), right coronary artery (RCA), diagonal/intermediate branch (D/I) and obtuse marginal branch (OM) were calculated to assess CAD severity. T2DM patients showed higher significant CAC scores, coronary plaque prevalence, total SSSs and LMCA-SSSs, LAD-SSSs, LCX-SSSs, RCA-SSSs and D/I-SSSs compared with NGM controls. In contrast to NGM controls, EAT volume was significantly increased in T2DM patients, whereas EAT attenuation was similar. In T2DM patients, EAT attenuation was associated with discrete CAD risk factors, the presence of coronary and triple-vessel plaques, as well as LAD-SSSs, LCX-SSSs, RCA-SSSs and total SSSs. In addition, EAT attenuation was only associated with the total SSS of calcified plaques, but not with noncalcified plaques. In T2DM patients, high EAT attenuation is associated with the presence and severity of CAD in general and with coronary stenosis caused by calcified plaques in particular.

Continuous extraction of coronary artery centerline from cardiac CTA images using a regression-based method.

Coronary artery centerline extraction in cardiac computed tomography angiography (CTA) is an effectively non-invasive method to diagnose and evaluate coronary artery disease (CAD). The traditional method of manual centerline extraction is time-consuming and tedious. In this study, we propose a deep learning algorithm that continuously extracts coronary artery centerlines from CTA images using a regression method. In the proposed method, a CNN module is trained to extract the features of CTA images, and then the branch classifier and direction predictor are designed to predict the most possible direction and lumen radius at the given centerline point. Besides, a new loss function is developed for associating the direction vector with the lumen radius. The whole process starts from a point manually placed at the coronary artery ostia, and terminates until tracking the vessel endpoint. The network was trained using a training set consisting of 12 CTA images and the evaluation was performed using a testing set consisting of 6 CTA images. The extracted centerlines had an average overlap (OV) of 89.19%, overlap until first error (OF) of 82.30%, and overlap with clinically relevant vessel (OT) of 91.42% with manually annotated reference. Our proposed method can efficiently deal with multi-branch problems and accurately detect distal coronary arteries, thereby providing potential help in assisting CAD diagnosis.

Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images.

In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987mm, and the average execution time is 2.137s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.

FEops HEARTguide Patient-Specific Computational Simulations for WATCHMAN FLX Left Atrial Appendage Closure: A Retrospective Study

BackgroundThree-dimensional transesophageal echocardiography (3D-TEE) is the primary imaging tool for left atrial appendage closure planning. The utility of cardiac computed tomography angiography (CCTA) and patient-specific computational models is unknown. ObjectivesThe purpose of this study was to evaluate the accuracy of the FEops HEARTguide patient-specific computational modeling in predicting appropriate device size, location, and compression of the WATCHMAN FLX compared to intraprocedural 3D-TEE. MethodsPatients with both preprocedural and postprocedural CCTA and 3D-TEE imaging of the LAA who received a WATCHMAN FLX left atrial appendage closure device were studied (n = 22). The FEops HEARTguide platform used baseline CCTA imaging to generate a prediction of device size(s), device position(s), and device dimensions. Blinded (without knowledge of implanted device size/position) and unblinded (implant device size/position disclosed) simulations were evaluated. ResultsIn 16 (72.7%) patients, the blind simulation predicted the final implanted device size. In these patients, the 3D-TEE measurements were not significantly different and had excellent correlation (Pearson correlation coefficient (r) ≥ 0.90). No patients had peridevice leak after device implant. In the 6 patients for whom the model did not predict the implanted device size, a larger device size was ultimately implanted as per operator preference. The model measurements of the unblinded patients demonstrated excellent correlation with 3D-TEE. ConclusionsThis is the first study to demonstrate that the FEops HEARTguide model accurately predicts WATCHMAN FLX device implantation characteristics. Future studies are needed to evaluate if computational modeling can improve confidence in sizing, positioning, and compression of the device without compromising technical success.

DeepHeartCT: A fully automatic artificial intelligence hybrid framework based on convolutional neural network and multi-atlas segmentation for multi-structure cardiac computed tomography angiography image segmentation.

Cardiac computed tomography angiography (CTA) is an emerging imaging modality for assessing coronary artery as well as various cardiovascular structures. Recently, deep learning (DL) methods have been successfully applied to many applications of medical image analysis including cardiac CTA structure segmentation. However, DL requires a large amounts of data and high-quality labels for training which can be burdensome to obtain due to its labor-intensive nature. In this study, we aim to develop a fully automatic artificial intelligence (AI) system, named DeepHeartCT, for accurate and rapid cardiac CTA segmentation based on DL. The proposed system was trained using a large clinical dataset with computer-generated labels to segment various cardiovascular structures including left and right ventricles (LV, RV), left and right atria (LA, RA), and LV myocardium (LVM). This new system was trained directly using high-quality computer labels generated from our previously developed multi-atlas based AI system. In addition, a reverse ranking strategy was proposed to assess the segmentation quality in the absence of manual reference labels. This strategy allowed the new framework to assemble optimal computer-generated labels from a large dataset for effective training of a deep convolutional neural network (CNN). A large clinical cardiac CTA studies (n = 1,064) were used to train and validate our framework. The trained model was then tested on another independent dataset with manual labels (n = 60). The Dice score, Hausdorff distance and mean surface distance were used to quantify the segmentation accuracy. The proposed DeepHeartCT framework yields a high median Dice score of 0.90 [interquartile range (IQR), 0.90-0.91], a low median Hausdorff distance of 7 mm (IQR, 4-15 mm) and a low mean surface distance of 0.80 mm (IQR, 0.57-1.29 mm) across all segmented structures. An additional experiment was conducted to evaluate the proposed DL-based AI framework trained with a small vs. large dataset. The results show our framework also performed well when trained on a small optimal training dataset (n = 110) with a significantly reduced training time. These results demonstrated that the proposed DeepHeartCT framework provides accurate and rapid cardiac CTA segmentation that can be readily generalized for handling large-scale medical imaging applications.

Segmentation of human aorta using 3D nnU-net-oriented deep learning.

Computed tomography angiography (CTA) has become the main imaging technique for cardiovascular diseases. Before performing the transcatheter aortic valve intervention operation, segmenting images of the aortic sinus and nearby cardiovascular tissue from enhanced images of the human heart is essential for auxiliary diagnosis and guiding doctors to make treatment plans. This paper proposes a nnU-Net (no-new-Net) framework based on deep learning (DL) methods to segment the aorta and the heart tissue near the aortic valve in cardiac CTA images, and verifies its accuracy and effectiveness. A total of 130 sets of cardiac CTA image data (88 training sets, 22 validation sets, and 20 test sets) of different subjects have been used for the study. The advantage of the nnU-Net model is that it can automatically perform preprocessing and data augmentation according to the input image data, can dynamically adjust the network structure and parameter configuration, and has a high model generalization ability. Experimental results show that the DL method based on nnU-Net can accurately and effectively complete the segmentation task of cardiac aorta and cardiac tissue near the root on the cardiac CTA dataset, and achieves an average Dice similarity coefficient of 0.9698 ± 0.0081. The actual inference segmentation effect basically meets the preoperative needs of the clinic. Using the DL method based on the nnU-Net model solves the problems of low accuracy in threshold segmentation, bad segmentation of organs with fuzzy edges, and poor adaptability to different patients' cardiac CTA images. nnU-Net will become an excellent DL technology in cardiac CTA image segmentation tasks.

Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction "TrueFidelity" in children with congenital heart disease.

Background:Several recent studies have reported that deep learning reconstruction “TrueFidelity” (TF) improves computed tomography (CT) image quality. However, no study has compared adaptive statistical repeated reconstruction (ASIR-V) using TF in pediatric cardiac CT angiography (CTA) with a low peak kilovoltage.Objective:This study aimed to determine whether ASIR-V or TF CTA image quality is superior in children with congenital heart disease (CHD).Materials and methods:Fifty children (median age, 2 months; interquartile range, 0–5 months; 28 men) with CHD who underwent CTA were enrolled between June and September 2020. Images were reconstructed using 2 ASIR-V blending factors (80% and 100% [AV-100]) and 3 TF settings (low, medium, and high [TF-H] strength levels). For the quantitative analyses, 3 objective image qualities (attenuation, noise, and signal-to-noise ratio [SNR]) were measured of the great vessels and heart chambers. The contrast-to-noise ratio (CNR) was also evaluated between the left ventricle and the dial wall. For the qualitative analyses, the degree of quantum mottle and blurring at the upper level to the first branch of the main pulmonary artery was assessed independently by 2 radiologists.Results:When the ASIR-V blending factor level and TF strength were higher, the noise was lower, and the SNR was higher. The image noise and SNR of TF-H were significantly lower and higher than those of AV-100 (P < .01), except for noise in the right atrium and left pulmonary artery and SNR of the right ventricle. Regarding CNR, TF-H was significantly better than AV-100 (P < .01). In addition, in the objective assessment of the degree of quantum mottle and blurring, TF-H had the best score among all examined image sets (P < .01).Conclusion:TF-H is superior to AV-100 in terms of objective and subjective image quality. Consequently, TF-H was the best image set for cardiac CTA in children with CHD.

Coronary CT Value in Quantitative Assessment of Intermediate Stenosis.

Background and Objectives: Cardiac computed tomography angiography (CCTA) is an excellent non-invasive imaging tool to evaluate coronary arteries and exclude coronary artery disease (CAD). Managing intermediate coronary artery stenosis with negative or inconclusive functional tests is still a challenge. A regular stenosis evaluation together with high-risk plaque features, using semi-automated programs, are becoming promising tools. This case–control study was designed to evaluate the intermediate lesion features’ impact on CAD outcomes, using a semi-automated CCTA atherosclerotic plaque analysis program. Materials and Methods: We performed a single-center, prospective cohort study. A total of 133 patients with low to intermediate risk of CAD, older than 18 years with no previous history of CAD and good quality CCTA images were included in the study, and 194 intermediate stenosis (CAD-RADS 3) were analyzed. For more detailed morphological analysis, we used semi-automated CCTA-dedicated software. Enrolled patients were prospectively followed-up for 2 years. Results: Agatston score was significantly higher in the major adverse cardiovascular events (MACE) group (p = 0.025). Obstruction site analysis showed a significantly lower coronary artery remodeling index (RI) among patients with MACE (p = 0.037); nonetheless RI was negative in both groups. Plaque consistency analysis showed significantly bigger necrotic core area in the MACE group (p = 0.049). In addition, unadjusted multivariate analysis confirmed Agatston score and RI as significant MACE predictors. Conclusions: The Agatston score showes the total area of calcium deposits and higher values are linked to MACE. Higher plaque content of necrotic component is also associated with MACE. Additionally, negatively remodeled plaques are linked to MACE and could be a sign of advanced CAD. The Agatston score and RI are significant in risk stratification for the development of MACE.

401 Deepheartct: A Fully Automatic Hybrid Structure Segmentation Framework Based On Atlas, Reverse Ranking, And Convolutional Neural Network For Computed Tomography Angiography

Introduction: Cardiac computed tomography angiography (CTA) opens new opportunities on image analysis for diagnosing cardiovascular disease. Manual delineation remains the main method to quantify different chambers on CTA images. Deep learning (DL) methods have shown great promise in medical image analysis. However, DL requires large and high-quality training labels which are often limited due to its labor intensive nature. We aim to develop a fully automatic artificial intelligence system based on an optimal computer-labeled dataset for accurate CTA segmentation.

Influence of contrast enhancement at the contrast injection location for the arm or leg in neonatal and infant patients during cardiac computed tomography

Introduction and ObjectivesObtaining CCTA images with optimal injection location such as the arm or leg is important to avoid the artifacts caused by the CM. This study compares the computed tomography (CT) numbers and visualization scores of the three-dimensional (3D) images of the lumens of the blood vessels in the arm or leg during cardiac computed tomography angiography (CCTA) in neonatal and infant patients. Patients or Materials and MethodsBetween January 2017 and January 2020, 253 consecutive patients were considered for inclusion. We used the estimated propensity scores as a function of the demographic data, including age, body weight, and injection location (right or left side) in the arm (n = 58) and leg (n = 58) of neonatal and infant patients. We compared the mean CT numbers of the pulmonary artery, ascending aorta, and left superior vena cava; contrast–noise ratios (CNR); and visualization scores between the arm and leg as the injection locations. ResultsThe mean CT numbers during CCTA for the arm and leg were 479.4 and 461.3 HU in the ascending aorta, 464.2 and 448.1 HU in the pulmonary artery, and 232.8 and 220.1 HU in the left superior vena cava, respectively. The mean image noise (SD) and CNR values, respectively, were 38.9 HU and 12.1 for the arm as the injection location and 39.1 HU and 12.3 for the leg as the injection location. The median visualization scores of volume rendering of the 3D images were 3.0 and 3.0 for the arm and leg injection sites, respectively. There were no significant differences in the mean CT numbers of the ascending aorta, pulmonary artery, and left superior vena cava; SD value; CNR; and visualization scores between the arm and leg injection locations. ConclusionsThe CT numbers of the lumen of the blood vessel and visualization scores of the 3D images of the arm and leg injection locations are equal during CCTA in neonatal and infant patients with congenital heart disease.

Segmentation Algorithm-Based Safety Analysis of Cardiac Computed Tomography Angiography to Evaluate Doctor-Nurse-Patient Integrated Nursing Management for Cardiac Interventional Surgery.

To deeply analyze the influences of doctor-nurse-patient integrated nursing management on cardiac interventional surgery, 120 patients with coronary heart disease undergoing cardiac interventional therapy were selected as the subjects and randomly divided into two groups, 60 cases in each group. The experimental group used the doctor-nurse-patient integrated nursing, while the control group adopted the routine nursing. The Hessian matrix enhanced filter segmentation algorithm was used to process the cardiac computed tomography angiography (CTA) images of patients to assess the algorithm performance and the safety of nursing methods. The results showed that the Jaccard, Dice, sensitivity, and specificity of cardiac CTA images of patients with coronary heart disease processed by Hessian matrix enhanced filter segmentation algorithm were 0.86, 0.93, 0.94, and 0.95, respectively; the disease self-management ability score and quality of life score of patients in the experimental group after nursing intervention were significantly better than those before nursing intervention, with significant differences (P < 0.05). The number of cases with adverse vascular events in the experimental group was 3 cases, which was obviously lower than that in the control group (15 cases). The diagnostic accuracy of the two groups of patients after segmentation algorithm processing was 0.87 and 0.88, respectively, which was apparently superior than the diagnostic accuracy of conventional CTA (0.58 and 0.61). In summary, cardiac CTA evaluation of doctor-nurse-patient integrated nursing management cardiac interventional surgery based on segmentation algorithm had good safety and was worthy of further promotion in clinical cardiac interventional surgery.

Autostereoscopic 3D Display System for 3D Medical Images

Recent advances in autostereoscopic three-dimensional (3D) display systems have led to innovations in consumer electronics and vehicle systems (e.g., head-up displays). However, medical images with stereoscopic depth provided by 3D displays have yet to be developed sufficiently for widespread adoption in diagnostics. Indeed, many stereoscopic 3D displays necessitate special 3D glasses that are unsuitable for clinical environments. This paper proposes a novel glasses-free 3D autostereoscopic display system based on an eye tracking algorithm and explores its viability as a 3D navigator for cardiac computed tomography (CT) images. The proposed method uses a slit-barrier with a backlight unit, which is combined with an eye tracking method that exploits multiple machine learning techniques to display 3D images. To obtain high-quality 3D images with minimal crosstalk, the light field 3D directional subpixel rendering method combined with the eye tracking module is applied using a user’s 3D eye positions. Three-dimensional coronary CT angiography images were volume rendered to investigate the performance of the autostereoscopic 3D display systems. The proposed system was trialed by expert readers, who identified key artery structures faster than with a conventional two-dimensional display without reporting any discomfort or 3D fatigue. With the proposed autostereoscopic 3D display systems, the 3D medical image navigator system has the potential to facilitate faster diagnoses with improved accuracy.

Influencia del realce de contraste al inyectar un medio de contraste en el brazo o la pierna en pacientes neonatos y lactantes durante la angiografía por cardiotomografía

Introduction and ObjectivesObtaining CCTA images with optimal injection location such as the arm or leg is important to avoid the artifacts caused by the CM. This study compares the computed tomography (CT) numbers and visualization scores of the three-dimensional (3D) images of the lumens of the blood vessels in the arm or leg during cardiac computed tomography angiography (CCTA) in neonatal and infant patients. Patients or Materials and MethodsBetween January 2017 and January 2020, 253 consecutive patients were considered for inclusion. We used the estimated propensity scores as a function of the demographic data, including age, body weight, and injection location (right or left side) in the arm (n=58) and leg (n=58) of neonatal and infant patients. We compared the mean CT numbers of the pulmonary artery, ascending aorta, and left superior vena cava; contrast–noise ratios (CNR); and visualization scores between the arm and leg as the injection locations. ResultsThe mean CT numbers during CCTA for the arm and leg were 479.4 and 461.3 HU in the ascending aorta, 464.2 and 448.1 HU in the pulmonary artery, and 232.8 and 220.1 HU in the left superior vena cava, respectively. The mean image noise (SD) and CNR values, respectively, were 38.9 HU and 12.1 for the arm as the injection location and 39.1 HU and 12.3 for the leg as the injection location. The median visualization scores of volume rendering of the 3D images were 3.0 and 3.0 for the arm and leg injection sites, respectively. There were no significant differences in the mean CT numbers of the ascending aorta, pulmonary artery, and left superior vena cava; SD value; CNR; and visualization scores between the arm and leg injection locations. ConclusionsThe CT numbers of the lumen of the blood vessel and visualization scores of the 3D images of the arm and leg injection locations are equal during CCTA in neonatal and infant patients with congenital heart disease.

Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images.

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.

Mixed-reality hologram for diagnosis and surgical planning of double outlet of the right ventricle: a pilot study

To evaluate the mixed-reality (MR) hologram, a novel technology based on two-dimensional images, which simulates three-dimensional (3D) images and provides a dynamic and interactive alternative, for its usefulness in the diagnosis and surgical planning of double outlet of the right ventricle (DORV). Thirty-four patients who were suspected of DORV based on ultrasound findings underwent cardiac computed tomography angiography (CTA). The patients were assigned randomly to the MR holographic guidance (MRHG) group or the control group. For the patients in the MRHG group, the CTA images were converted into Standard Template Library (STL) files after segmentation, 3D reconstruction, colourisation, and transparentisation, and then exported for MR holographic visualisation. The CTA images of the patients in the control group were analysed using routine 3D reconstruction only. Diagnostic accuracy and surgical planning were compared between the two groups based on visualisation at surgery. In the MRHG group, the 3D hologram observation was in concordance with the actual anatomical findings, and the DORV type was classified accurately in all patients. The diagnostic accuracy for the malformation was 95.5% in the MRHG group and 89.7% in the control group, but the difference was not significant (p=0.3). All the procedures were exactly the same as planned based on the 3D MR holographic model. The surgical planning time was shorter for the MRHG group (51.65±11.11 min) than that for the control group (65.71±18.07 min, p<0.05). MR 3D holograms may provide a clear and deeper anatomical perception of DORV and improve surgical planning.

Large Coronary Artery Fistula in Tricuspid Atresia – Pulmonary Atresia With Intact Ventricular Septum – A Unique Association Revealed on Dual-Source CT

A 1-year-old cyanotic child underwent echocardiography, which revealed tricuspid atresia and pulmonary atresia with an intact ventricular septum. To clarify the anatomy, cardiac computed tomography (CT) angiography was performed on a 192x2 slice dual-source CT scanner. Review of the CT angiography images confirmed tricuspid atresia and pulmonary atresia with an intact ventricular septum (Figure 1A). There were multiple dilated sinusoids in the right ventricular wall (Figure 1A). Additionally, it revealed a unique combination of coronary anomalies.