Cardiac Computed Tomography Imaging Research Articles

Association between left atrial wall thickness and residual potential after first-pass pulmonary vein isolation using very high-power short-duration ablation in patients with atrial fibrillation

Abstract Background In this study, we evaluated efficacy, efficiency, and safety of pulmonary vein isolation (PVI) applying vHPSD ablation strategy in patients with atrial fibrillation (AF) and explored the association between left atrial wall thickness (LAWT) and the residual potential after first-pass PVI. Methods Between April 2, 2023 and October 6, 2023, drug refractory symptomatic AF patients who underwent AF ablation using vHPSD ablation strategy with QDOT MICRO catheter were prospectively enrolled. All patients took cardiac computed tomography (CT) a day before procedure. Using CT image, LAWT map was created. PV segments were categorized into prespecified 14 segments, and LAWT of each segment, segments with residual potential (RP) after first-pass PVI and early reconnection after 20-minute waiting after PVI were evaluated. Total procedure time, ablation time for PVI, and occurrence of procedure-related complication were collected. Results A total of 40 patients were included (mean age 64 years, 45% of persistent AF). Additional ablation other than PV was performed in 48% of patients, total procedure time was 77 minutes, and total PVI time was 9.9 minutes. With vHPSD PVI, all PVs first pass isolation rate was 60%; in terms of per PV, RSPV first-pass isolation was 88%, followed by LSPV and LIPV 85%, RIPV showed lowest first-pass isolation rate, 75% (Figure A). Most common RP sites was right carina, followed by left carina (Figure A). Compared to historical comparator, vHPSD PVI showed shorter procedure time, shorter ablation time for PVI, with numerically lower but statistically comparable first-pass PVI rate (Figure B). In total 560 PV segments in 40 patients, RSPV anterior, left carina, and right carina showed higher prevalence to be thick PV segments (over 60% of patients), followed by RPSV posterior, LSPV posterior, and RIPV anterior (over 40% of patients). Looking into ablation parameter detail, mean contact force was 10g, mean temperature was 47°C, and mean impedance drop was 8.4Ω. Compared to segments without RP, segments with RP showed thicker mean left atrial wall thickness grades, lower minimum contact force, higher maximum temperature, and lower minimum impedance drop. According to the left atrial wall thickness, thick PV segments showed higher prevalence of RP after first-pass PVI with vHPSD ablation. In segments with LAWT grade 3 or more, the RP rate was 9.1%. This thickness means thicker than 1.5 to 2.0 mm of thickness (Figure C). In 10% of patients (n=4) had audible or tactile steam-pops, visible char was confirmed in 5% of patients (n=2). There were no clinical complications. Conclusion PVI using vHPSD achieved shorter procedure time, shorter ablation time for PVI, and comparable first-pass PVI rate compared to historical comparator using ablation-index guided conventional power ablation. Thick segments had higher chance to have residual potential after first-pass PVI with vHPSD.

Automatic 3D left atrial strain extraction framework on cardiac computed tomography

Background and objectiveStrain analysis provides insights into myocardial function and cardiac condition evaluation. However, the anatomical characteristics of left atrium (LA) inherently limit LA strain analysis when using echocardiography. Cardiac computed tomography (CT) with its superior spatial resolution, has become critical for in-depth evaluation of LA function. Recent studies have explored the feasibility of CT-derived strain; however, they relied on manually selected regions of interest (ROIs) and mainly focused on left ventricle (LV). This study aimed to propose a first-of-its-kind fully automatic deep learning (DL)-based framework for three-dimensional (3D) LA strain extraction on cardiac CT. MethodsA total of 111 patients undergoing ECG-gated contrast-enhanced CT for evaluating subclinical atrial fibrillation (AF) were enrolled in this study. We developed a 3D strain extraction framework on cardiac CT images, containing a 2.5D GN-U-Net network for LA segmentation, axis-oriented 3D view extraction, and LA strain measure. The segmentation accuracy was evaluated using Dice similarity coefficient (DSC). The model-extracted LA volumes and emptying fraction (EF) were compared with ground-truth measurements using intraclass correlation coefficient (ICC), correlation coefficient (r), and Bland-Altman plot (B-A). The automatically extracted LA strains were evaluated against the LA strains measured from 2D echocardiograms. We utilized this framework to gauge the effect of AF burden on LA strain, employing the atrial high rate episode (AHRE) burden as the measurement parameter. ResultsThe GN-U-Net LA segmentation network achieved a DSC score of 0.9603 on the test set. The framework-extracted LA estimates demonstrated excellent ICCs of 0.949 (95 % CI: 0.93–0.97) for minimal LA volume, 0.904 (95 % CI: 0.86–0.93) for maximal LA volume, and 0.902 (95 % CI: 0.86–0.93) for EF, compared with expert measurements. The framework-extracted LA strains demonstrated moderate agreement with the LA strains based on 2D echocardiography (ICCs >0.703). Patients with AHRE > 6 min had significantly lower global strain and LAEF, as extracted by the framework than those with AHRE ≤ 6 min. ConclusionThe promising results highlighted the feasibility and clinical usefulness of automatically extracting 3D LA strain from CT images using a DL-based framework. This tool could provide a 3D-based alternative to echocardiography for assessing LA function.

Diagnostic performance of unenhanced electrocardiogram-gated cardiac CT for detecting myocardial edema.

To assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac computed tomography (CT) for detecting myocardial edema, using MRI T2 mapping as the reference standard. This retrospective study protocol was approved by our institutional review board, which waived the requirement for written informed consent. Between December 2017 to February 2019, consecutive patients who had undergone T2 mapping for myocardial tissue characterization were identified. We excluded patients who did not undergo unenhanced ECG-gated cardiac CT within 3 months from MRI T2 mapping or who had poor CT image quality. All patients underwent unenhanced ECG-gated cardiac CT with an axial scan using a third-generation, 320 × 0.5 mm detector-row CT unit. Two radiologists together drew regions of interest (ROIs) in the interventricular septum on the unenhanced ECG-gated cardiac CT images. Using T2 mapping as the reference standard, the diagnostic performance of unenhanced cardiac CT for detecting myocardial edema was evaluated by using the area under the receiver operating characteristic curve with sensitivity and specificity. Youden index was used to find an optimal sensitivity-specificity cutoff point. A cardiovascular radiologist independently performed the measurements, and interobserver reliability was assessed using intraclass correlation coefficients for CT value measurements. A P value of <.05 was considered statistically significant. We included 257 patients who had undergone MRI T2 mapping. Of the 257 patients, 35 patients underwent unenhanced ECG-gated cardiac CT. One patient was excluded from the study because of poor CT image quality. Finally, 34 patients (23 men; age 64.7 ± 14.6 years) comprised our study group. Using T2 mapping, we identified myocardial edema in 19 patients. Mean CT and T2 values for 34 patients were 46.3 ± 2.7 Hounsfield unit and 49.0 ± 4.9 ms, respectively. Mean CT values moderately correlated with mean T2 values (Rho = -0.41; P < .05). Mean CT values provided a sensitivity of 63.2% and a specificity of 93.3% for detecting myocardial edema, with a cutoff value of ≤45.0 Hounsfield unit (area under the receiver operating characteristic curve = 0.77; P < .01). Inter-observer reproducibility in measuring mean CT values was excellent (intraclass correlation coefficient = 0.93; [95% confidence interval: 0.86, 0.96]). Myocardial edema could be detected by CT value of myocardium in unenhanced ECG-gated cardiac CT.

CCT‘S CRYSTAL BALL: PREDICTING CARDIOVASCULAR RISK IN AORTIC VALVE STENOSIS PATIENTS UNDERGOING VALVE REPLACEMENT

Abstract Background and Aims Currently, computed tomography cardiac imaging (CTCI) plays a fundamental role in the pre–procedural planning of degenerative–calcific aortic valve stenosis(AS) correction. However, its prognostic role in assessing mortality and major adverse cardiovascular events (MACE) has not been extensively explored. CTCI allows for the evaluation of coronary artery calcification(CAC), mitral annular calcifications (MAC), aortic valve calcifications (CVA), and thoracic aorta calcifications (CAT). The aim of our study was to assess whether the presence and extent of CAC, MAC, CVA, and CAT could influence non–cardiovascular mortality (NCM), cardiovascular mortality (CM), and MACE in patients undergoing valve replacement(VR). Materials and Methods We collected retrospectivelly clinical and instrumental data and reviewed CTCI using a semi–quantitative approach. CACs were classified according to the visual 2022 Coronary Artery Disease–Reporting and Data System. MACs were defined following the Guerrero et al. study, CVAs according to the Win Registry and CATs were assessed in the ascending aorta, aortic arch, and descending aorta according to the Vos classification. Results 244 patients(59% male, mean age 80.1±6.6 years), with 91% undergoing transfemoral VR, were enrolled(2016–2018). CM was significantly higher in patients with hypertension,cerebro/peripheral vascular disease, stage IV–V renal insufficiency and history of atrial fibrillation (p=0.012,p&amp;lt;0.001,p=0.005, p=0.034 and p=0.024, respectively). CM was also more pronounced in patients with STS SCORE≥6.7±5.2 and EuroSCOREII≥ 5.5±5.8(p=0.016 and p=0.037). Extensive CAC(p=0.019), a MAC score&amp;gt;7(p=0.051), moderate and severe AVC (p=0.029 and p=0.053) and TAC&amp;gt;270° in aortic arch(p=0.019) were linked to CM. In multivariate analysis, a high MAC score and the presence of moderate AVC persisted as risk factors for CM. The presence of a MAC score &amp;gt;7(p=0.006) and the extension of descending TAC &amp;gt;1.5 mm (p=0.034) were associated with an increased risk of NCM.The presence of extensive TAC (≥270°) measured in the aortic arch and descending aorta were associated with MACE (p=0.034 and p=0.0504 respectively). Conclusions CTCI emerges as a crucial tool for evaluating prognostic risk in patients undergoing VR for AS. This type of evaluation can assist clinicians in pre–procedural planning and patient management, allowing for better identification of high–risk patients and optimization of therapeutic strategies

A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.

Radiomics extracts hundreds of features from medical images to quantitively characterize a region of interest (ROI). When applying radiomics, imbalanced or small dataset issues are commonly addressed using under or over-sampling, the latter being applied directly to the extracted features. Aim of this study is to propose a novel balancing and data augmentation technique by applying perturbations (erosion, dilation, contour randomization) to the ROI in cardiac computed tomography images. From the perturbed ROIs, radiomic features are extracted, thus creating additional samples. This approach was tested addressing the clinical problem of distinguishing cardiac amyloidosis (CA) from aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM). Twenty-one CA, thirty-two AS and twenty-one HCM patients were included in the study. From each original and perturbed ROI, 107 radiomic features were extracted. The CA-AS dataset was balanced using the perturbation-based method along with random over-sampling, adaptive synthetic (ADASYN) and the synthetic minority oversampling technique (SMOTE). The same methods were tested to perform data augmentation dealing with CA and HCM. Features were submitted to robustness, redundancy, and relevance analysis testing five feature selection methods (p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA). Support vector machine performed the classification tasks, and its performance were evaluated by means of a 10-fold cross-validation. The perturbation-based approach provided the best performances in terms of f1 score and balanced accuracy in both CA-AS (f1 score: 80%, AUC: 0.91) and CA-HCM (f1 score: 86%, AUC: 0.92) classifications. These results suggest that ROI perturbations represent a powerful approach to address both data balancing and augmentation issues.

Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography

Objective. Cardiac computed tomography (CT) is widely used for diagnosis of cardiovascular disease, the leading cause of morbidity and mortality in the world. Diagnostic performance depends strongly on the temporal resolution of the CT images. To image the beating heart, one can reduce the scanning time by acquiring limited-angle projections. However, this leads to increased image noise and limited-angle-related artifacts. The goal of this paper is to reconstruct high quality cardiac CT images from limited-angle projections. Approach. The ability to reconstruct high quality images from limited-angle projections is highly desirable and remains a major challenge. With the development of deep learning networks, such as U-Net and transformer networks, progresses have been reached on image reconstruction and processing. Here we propose a hybrid model based on the U-Net and Swin-transformer (U-Swin) networks. The U-Net has the potential to restore structural information due to missing projection data and related artifacts, then the Swin-transformer can gather a detailed global feature distribution. Main results. Using synthetic XCAT and clinical cardiac COCA datasets, we demonstrate that our proposed method outperforms the state-of-the-art deep learning-based methods. Significance. It has a great potential to freeze the beating heart with a higher temporal resolution.

Screening, risk stratification and CT imaging for assessment of coronary artery disease

Early treatment of cardiovascular risk factors and characterization of coronary plaques is essential to collect prognostic information about coronary artery disease (CAD) and prevent cardiovascular events. Discussion of the most important risk factors of CAD, basic diagnostic of CAD, prevention, and prognostic factors of CAD with focus on cardiac computed tomography (CT). Prevalence and prognostic impact of CAD risk factors; description of specific assessment of risk profiles; estimation of pretest probability; conventional prevention of CAD; prognostic assessment of CAD using the Calcium Scoring and coronary CT angiography. Assessment of risk profiles and estimation of pretest probability for obstructive coronary stenosis necessitates athorough evaluation of medical history and laboratory values. The composition and extent of calcified and noncalcified plaques in CT exams based on the criteria of the Coronary Artery Disease-Reporting and Data System give important prognostic information about the risk of cardiovascular events, which increases with high plaque burden and vice versa. Initial imaging with CT for evaluation of CAD leads to areduction of invasive coronary angiographies and catheter-associated complications. Besides early detection of cardiovascular risk factors, the additional assessment of plaque burden and significant stenosis in CT gives further prognostic information to facilitate effective therapies to prevent cardiovascular events and in the case of low plaque burden avoid invasive coronary angiography. However, systmatic screening using Calcium Scoring is not established yet due to insufficient data, although it could potentially be used for an early risk stratification in patients with multiple risk factors.

CAR and CSTR Cardiac Computed Tomography (CT) Practice Guidelines: Part 2-Non-Coronary Imaging.

The cardiac computed tomography (CT) practice guidelines provide an updated review of the technological improvements since the publication of the first Canadian Association of Radiologists (CAR) cardiac CT practice guidelines in 2009. An overview of the current evidence supporting the use of cardiac CT in the most common clinical scenarios, standards of practice to optimize patient preparation and safety as well as image quality are described. Coronary CT angiography (CCTA) is the focus of Part I. In Part II, an overview of cardiac CT for non-coronary indications that include valvular and pericardial imaging, tumour and mass evaluation, pulmonary vein imaging, and imaging of congenital heart disease for diagnosis and treatment monitoring are discussed. The guidelines are intended to be relevant for community hospitals and large academic centres with established cardiac CT imaging programs.

Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images

Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for classifying the patients into four TAC risk categories. The method started by segmenting the thoracic aorta, combining three UNets trained with axial, sagittal and coronal CT images. Afterwards, the surrounding lesion candidates were classified using three combined convolutional neural networks (CNNs) trained with orthogonal patches. Image datasets included 1190 non-enhanced ECG-gated cardiac CT studies from a cohort of cardiovascular patients (age 57 ± 9 years, 80% men, 65% TAC > 0). In the test set (N = 119), the combination of UNets was able to successfully segment the thoracic aorta with a mean volume difference of 0.3 ± 11.7 ml (<6%) and a median Dice coefficient of 0.947. The combined CNNs accurately classified the lesion candidates and 87% of the patients (N = 104) were accurately placed in their corresponding risk categories (Kappa = 0.826, ICC = 0.9915). TAC measurement can be estimated automatically from cardiac CT images using UNets to isolate the thoracic aorta and CNNs to classify calcified lesions.

Comparison of left atrial appendage measurements between conventional transesophageal echocardiography and "Virtual TEE" reconstructed from computed tomography for pre-procedural planning of device closure.

For pre-procedural planning of left atrial appendage (LAA) closure, sizing is crucial. Although transesophageal echocardiography (TEE) is a standard modality, cardiac computed tomography (CT) is also widely used. The virtual TEE (V-TEE) that our group developed enables us to reconstruct images similar to TEE images from CT images. The software should be helpful to understand and plan the procedure strategy. Accordingly, we investigated the utility of V-TEE. Sixty-six patients at 4 participating sites who completed both CT and TEE prior to LAA closure were included. The LAA diameter at the landing zone (LZ) for WATCHMAN™ device implantation was statistically compared at 0°, 45°, 90°, and 135° between V-TEE and TEE. Among 66 cases, only 3 cases were excluded due to poor imaging quality, and 63 cases were analyzed. The device LZ diameters based on V-TEE were strongly correlated with those based on TEE, despite the significantly greater diameter based on V-TEE with mean differences of 2.4 to 3.0mm (all of them: P < 0.001). The discordances (V-TEE/TEE ratio) at most angles were significantly larger in the elliptical LAAs. V-TEE provides a valuable method for the evaluation of the LAA diameters. V-TEE-based measurements were larger than conventional TEE-based measurements, especially in cases of elliptical LAAs. The assessment by V-TEE has the potential benefit of ensuring proper device sizing regardless of the LAA morphology.

Cardiac Computed Tomography Angiography in Infants and Young Children Without Sedation.

Cardiac computed tomography (CT) imaging plays a pivotal role in the diagnosis and management of infants and young children with congenital heart disease (CHD). While the benefits of CT imaging are well-established, the challenge lies in adapting these procedures to the unique requirements of infants and young children. Traditionally, sedation has been a common practice to ensure cooperation and motion control during imaging. However, using sedation introduces its challenges including potential risks, limitations, and cost implications. In this study, authors explore the feasibility, safety, and diagnostic accuracy of unsedated cardiac CT examinations in infants and young children. This study proves cardiac CT can be performed in India without sedation using simple restraining techniques. This approach aligns with the cultural and familial dynamics prevalent in the country and holds the potential to address economic and infrastructure challenges.

Cardiac Computed Tomography Assessment of the Variation of Papillary Muscle Morphology before Mitral Valve Surgery

Background. The morphology of the papillary muscles of the left ventricle is highly variable. Few studies have investigated papillary muscle morphology using imaging. Objective. This study aimed to assess papillary muscle morphology with primary mitral regurgitation (MR) using cardiac computed tomography (CT). Methods. We examined 116 patients who underwent robotic mitral valve repair for primary MR using preoperative cardiac CT. Papillary muscle morphology was assessed using CT images and compared with operative findings. Results. CT images of papillary muscles were consistent with the operative findings during robotic mitral valve repair in all cases. Both the anterolateral papillary muscle (APM) and posteromedial papillary muscle (PPM) groups were identified in all cases, and the middle papillary muscle (MPM) group was identified in 24.1% of cases. The PPM group had a higher proportion of complex morphologies with more heads and bases than the APM group (head: p&lt;0.001 and base: p&lt;0.001). The PPM group had smaller papillary muscle sizes than the APM group. The MPM group in most patients had one base and one head (78.6%). Papillary muscle sizes were significantly smaller in the order of the APM, PPM, and MPM groups (p&lt;0.001). Conclusions. Cardiac CT allowed clear visualization and accurate assessment of papillary muscle morphology in the left ventricle. It may be useful to obtain the papillary muscle variations preoperatively using CT imaging in procedures involving the papillary muscles such as mitral valve repair.

Membranous Septum Length Predicts New Conduction Abnormalities in Surgical Aortic Valve Replacement: A Novel Predictor for Permanent Pacemaker Implantation After Surgical Aortic Valve Replacement

IntroductionThe membranous septum (MS) length measured by cardiac computed tomography (CT) is useful for the prediction of permanent pacemaker implantation (PPMI) and new left bundle branch block (LBBB) after transcatheter aortic valve replacement. However, its predictive value for patients undergoing surgical aortic valve replacement (SAVR) is unknown. MethodsA total of 2531 consecutive patients were registered in the institutional Society of Thoracic Surgeons database between July 2017 and June 2020. Patients who underwent non-SAVR procedures, had prior pacemaker/implantable cardioverter defibrillator, prior SAVR, no preprocedural CT assessment, or suboptimal CT imaging were excluded. ResultsA total of 126 SAVR with preprocedural CT assessment were analyzed. Bicuspid aortic valve morphology was confirmed on CT in 59.5% of patients. There were three new PPMIs and five new LBBBs observed after SAVR at the time of discharge. In-hospital mortality was 0.8%. Low left ventricular (LV) ejection fraction (<50%), LV mass index >120 g/m2, large right coronary artery height, and MS length <1.5 mm predicted new PPMI/LBBB. Multivariate analysis showed LV mass index >120 g/m2 (odds ratio: 9.165; 95% confidence interval: 1.644-51.080; P = 0.011) and MS length <1.5 mm (odds ratio: 14.449; 95% confidence interval: 1.632-127.954; P = 0.016) were independent predictors for new PPMI/LBBB. ConclusionsShort MS length on preoperative cardiac CT is a powerful and novel predictor for the risk of new PPMI/LBBB after SAVR. Special care should be taken in patients with short MS length to avoid suture-mediated trauma.

TT U-Net: Temporal Transformer U-Net for Motion Artifact Reduction Using PAD (Pseudo All-Phase Clinical-Dataset) in Cardiac CT.

Involuntary motion of the heart remains a challenge for cardiac computed tomography (CT) imaging. Although the electrocardiogram (ECG) gating strategy is widely adopted to perform CT scans at the quasi-quiescent cardiac phase, motion-induced artifacts are still unavoidable for patients with high heart rates or irregular rhythms. Dynamic cardiac CT, which provides functional information of the heart, suffers even more severe motion artifacts. In this paper, we develop a deep learning based framework for motion artifact reduction in dynamic cardiac CT. First, we build a PAD (Pseudo All-phase clinical-Dataset) based on a whole-heart motion model and single-phase cardiac CT images. This dataset provides dynamic CT images with realistic-looking motion artifacts that help to develop data-driven approaches. Second, we formulate the problem of motion artifact reduction as a video deblurring task according to its dynamic nature. A novel TT U-Net (Temporal Transformer U-Net) is proposed to excavate the spatiotemporal features for better motion artifact reduction. The self-attention mechanism along the temporal dimension effectively encodes motion information and thus aids image recovery. Experiments show that the TT U-Net trained on the proposed PAD performs well on clinical CT scans, which substantiates the effectiveness and fine generalization ability of our method. The source code, trained models, and dynamic demo will be available at https://github.com/ivy9092111111/TT-U-Net.

The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels.

To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ). The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively). There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.

Identification of Mitral Valve Prolapse on Non-electrocardiography-gated Enhanced Chest Computed Tomography.

The primary imaging modality for the diagnosis of mitral valve prolapse (MVP) is echocardiography supplemented by electrocardiography (ECG)-gated cardiac computed tomography (CT) angiography. However, we have recently encountered patients with MVP who were initially identified on non-ECG-gated enhanced chest CT. The purpose of this study is to evaluate the diagnostic accuracy of non-ECG-gated enhanced chest CT to predict the presence of MVP. Of 92 patients (surgically confirmed MVP who underwent non-ECG-gated chest CT), 27 patients were excluded for motion artifact or insufficient surgical correlation, and 65 patients were ultimately included. As a control, 65 patients with dyspnea and without MVP (non-ECG-gated chest CT and echocardiography were performed within 1 month) were randomly selected. We retrospectively analyzed an asymmetric double line sign on axial CT images for the presence of MVP. The asymmetric double line sign was defined as the presence of a linear structure, not located in the plane traversing the mitral annulus. Use of the asymmetric double line sign to predict MVP on non-ECG-gated CT showed modest sensitivity, high specificity, modest negative predictive value, and high positive predictive value of 59% (38/65), 99% (64/65), 70% (64/91), and 97% (38/39), respectively. The asymmetric double line sign on non-ECG-gated enhanced chest CT may be a valuable finding to predict the presence of MVP. Familiarity with this CT finding may lead to prompt diagnosis and proper management of MVP.

Development of a three-dimensional computer model of the equine heart using a polyurethane casting technique and in vivo contrast-enhanced computed tomography

Introduction/objectivesInsight into the three-dimensional (3D) anatomy of the equine heart is essential in veterinary education and to develop minimally invasive intracardiac procedures. The aim was to create a 3D computer model simulating the in vivo anatomy of the adult equine heart. AnimalsTen horses and five ponies. Materials and methodsTen horses, euthanized for non-cardiovascular reasons, were used for in situ cardiac casting with polyurethane foam and subsequent computed tomography (CT) of the excised heart. In five anaesthetized ponies, a contrast-enhanced electrocardiogram-gated CT protocol was optimized to image the entire heart. Dedicated image processing software was used to create 3D models of all CT scans derived from both methods. Resulting models were compared regarding relative proportions, detail and ease of segmentation. ResultsThe casting protocol produced high detail, but compliant structures such as the pulmonary trunk were disproportionally expanded by the foam. Optimization of the contrast-enhanced CT protocol, especially adding a delayed phase for visualization of the cardiac veins, resulted in sufficiently detailed CT images to create an anatomically correct 3D model of the pony heart. Rescaling was needed to obtain a horse-sized model. ConclusionsThree-dimensional computer models based on contrast-enhanced CT images appeared superior to those based on casted hearts to represent the in vivo situation and are preferred to obtain an anatomically correct heart model useful for education, client communication and research purposes. Scaling was, however, necessary to obtain an approximation of an adult horse heart as cardiac CT imaging is restricted by thoracic size.

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study

PurposeThe purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol. Materials and methodsAcquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d’) was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter). ResultsNoise magnitude values were lower with PIQE than with AiCE (−13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f50) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f50 values were found to be close. Whatever the dose and DLR level, d’ values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d’ values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels. ConclusionCompared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.

Abstract 16201: Use of Cardiac Computed Tomography (CT) Imaging Biomarker Variables for the Prediction of Incident Heart Failure: Multi-Ethnic Study of Atherosclerosis

Introduction: Several predictive heart failure (HF) models exist to determine incident HF. We aimed to create a model that uses cardiac CT imaging biomarkers to improve discrimination scores of incident HF. Hypothesis: Cardiac CT variables increase predictive abilities of the Pooled Cohort Equations to Prevent HF (PCP-HF) score (a validated 10-year risk of new-onset HF prediction model) in all HF, HFrEF, and HFpEF. Methods: MESA participants aged 45-84 years old and free of clinical CVD who completed a cardiac CT were included for study analysis. The outcome of interest was new-onset HF. Clinical risk factors were obtained. Cardiac CT variables analyzed included left ventricular size index (LVSi) and calcifications of coronary arteries (CAC), aortic valve (AVC), mitral valve (MVC), and thoracic aorta (TAC). Results: Among 6,667 MESA study participants who underwent cardiac CT, 426 events of new-onset HF occurred during the follow-up period. Among the 426 events, 173 (40.6%) were categorized as HFrEF, 193 (45.3%) were categorized as HFpEF, and 60 (14.1%) had missing ejection fraction data. The reported data is based on the Cox model adjusted for all CT variables in one model with log(PCP-HF). For all incident HF (preserved and reduced), CAC (HR 1.10 95% CI 1.05-1.15, p&lt;0.001) and LVSI (HR 1.14 95% CI 1.10-1.18, p&lt;0.001) were statistically significant with an improvement in c-stat from .773 to .805. For HFpEF, CAC (HR 1.07 95% CI 1.01-1.14, p&lt;0.025), AVC (HR 1.08 95% CI 1.00-1.16, p&lt;0.037) and MVC (HR 1.12 95% CI 1.05-1.19, p&lt;0.001) were statistically significant with an improvement in c-stat from .782 to .800. For HFrEF, CAC (HR 1.15 95% CI 1.08-1.23, p&lt;0.001) and LVSI (HR 1.28 95% CI 1.23-1.34, p&lt;0.001) were statistically significant with an improvement in c-stat from .751 to .815. Conclusions: Current HF prediction models, including PCP-HF model, are based on clinical risk factors. As evidenced by this model CAC, AVC, MVC and LVSi have statistical significance with improvement in c-statistics of the PCP-HF model when adjusted for CT variables. HFpEF and HFrEF have different CT variables with enhanced predictive ability.This study emphasizes the need to study the utility of cardiac CT imaging biomarkers in the development of an incident HF predictive model.

Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images.

Cardiac substructure segmentation is a prerequisite for cardiac diagnosis and treatment, providing a basis for accurate calculation, modeling, and analysis of the entire cardiac structure. CT (computed tomography) imaging can be used for a noninvasive qualitative and quantitative evaluation of the cardiac anatomy and function. Cardiac substructures have diverse grayscales, fuzzy boundaries, irregular shapes, and variable locations. We designed a deep learning-based framework to improve the accuracy of the automatic segmentation of cardiac substructures. This framework integrates cardiac anatomical knowledge; it uses prior knowledge of the location, shape, and scale of cardiac substructures and separately processes the structures of different scales. Through two successive segmentation steps with a coarse-to-fine cascaded network, the more easily segmented substructures were coarsely segmented first; then, the more difficult substructures were finely segmented. The coarse segmentation result was used as prior information and combined with the original image as the input for the model. Anatomical knowledge of the large-scale substructures was embedded into the fine segmentation network to guide and train the small-scale substructures, achieving efficient and accurate segmentation of ten cardiac substructures. Sixty cardiac CT images and ten substructures manually delineated by experienced radiologists were retrospectively collected; the model was evaluated using the DSC (Dice similarity coefficient), Recall, Precision, and the Hausdorff distance. Compared with current mainstream segmentation models, our approach demonstrated significantly higher segmentation accuracy, with accurate segmentation of ten substructures of different shapes and sizes, indicating that the segmentation framework fused with prior anatomical knowledge has superior segmentation performance and can better segment small targets in multi-target segmentation tasks.