CCTA Images Research Articles

CT-derived Fractional Flow Reserve: How, When, and Where to use this Novel Cardiac Imaging Tool.

Fractional flow reserve computed tomography (FFRCT) is a novel imaging modality. It utilizes computational fluid dynamics analysis of coronary blood flow obtained from CCTA images to estimate the decrease in pressure across coronary stenosis during the maximum hyperemia. The FFRCT can serve as a valuable tool in the assessment of coronary artery disease (CAD). This non-invasive option can be used as an alternative to the invasive fractional Flow Reserve (FFR) evaluation, which is presently considered the gold standard for evaluating the physiological significance of coronary stenoses. It can help in several clinical situations, including Assessment of Acute and stable chest pain, virtual planning for coronary stenting, and treatment decision-making. Although FFRCT has demonstrated potential clinical applications as a non-invasive imaging technique, it is also crucial to acknowledge its limitations in clinical practice. As a result, it is imperative to meticulously evaluate the advantages and drawbacks of FFRCT individually and contemplate its application in combination with other diagnostic examinations and clinical data.

Coronary computed tomographic angiography predicts crossing through chronic total occlusion within three guidewires

BackgroundCoronary computed tomography angiography (CCTA) is a valuable non-invasive method for the diagnosis and treatment of coronary artery disease, especially providing important clinical decision-making basis for percutaneous coronary intervention (PCI) of chronic total occlusion (CTO). However, there is no data to discuss whether CCTA can predict the success rate of crossing through occlusive lesions within 3 guidewires. ObjectiveThe aim of this study was to establish a scoring model based on CCTA images to predict the success rate of CTO-PCI within 3 guidewires for grading treatment difficulty. MethodsData including 267 CTO lesions in 252 patients with pre-procedural CCTA were analyzed. Successfully crossing through the lesion within 3 guidewires was set as a new endpoint to eliminate operator bias and estimate surgical costs. The Guidewires of CTO (GW-CTO) score was determined by assigning an appropriate integer point for each independent predictor of this endpoint and summing all points accrued. Results72.3% reached the new endpoint. We identified 5 predictors. Including blunt proximal stump, both proximal and distal branches, occluded segmental curvature>45°, length of occlusion≥20 mm, and calcified plaque burden≥0.125. With the GW-CTO score rising from 0 to 5, the success rate dropped from 98.83% to 12.50%. The CTO-PCI difficulty was stratified into three grades: easy (0–1), intermediate (2–3), and difficult (4–5). The GW-CTO score yielded good predictive results, with an area under receiver-operator characteristic (ROC) curve of 0.87. ConclusionThis model is very novel and presents a new perspective to predict the probability of successfully passing through CTO lesions within 3 guidewires. This may help grade the difficulty of CTO-PCI and determining the cost efficiency.

The Effect of Severe Coronary Calcification on Diagnostic Performance of Computed Tomography-Derived Fractional Flow Reserve Analyses in People with Coronary Artery Disease.

Negative CCTA can effectively exclude significant CAD, eliminating the need for further noninvasive or invasive testing. However, in the presence of severe CAD, the accuracy declines, thus necessitating additional testing. The aim of our study was to evaluate the diagnostic performance of noninvasive cFFR derived from CCTA, compared to ICA in detecting hemodynamically significant stenoses in participants with high CAC scores (>400). This study included 37 participants suspected of having CAD who underwent CCTA and ICA. CAC was calculated and cFFR analyses were performed using an on-site machine learning-based algorithm. Diagnostic accuracy parameters of CCTA and cFFR were calculated on a per-vessel level. The median total CAC score was 870, with an IQR of 642-1370. Regarding CCTA, sensitivity and specificity for RCA were 60% and 67% with an AUC of 0.639; a LAD of 87% and 50% with an AUC of 0.688; an LCX of 33% and 90% with an AUC of 0.617, respectively. Regarding cFFR, sensitivity and specificity for RCA were 60% and 61% with an AUC of 0.606; a LAD of 75% and 54% with an AUC of 0.647; an LCX of 50% and 77% with an AUC of 0.647. No significant differences between AUCs of coronary CTA and cFFR for each vessel were found. Our results showed poor diagnostic accuracy of CCTA and cFFR in determining significant ischemia-related lesions in participants with high CAC scores when compared to ICA. Based on our results and study limitations we cannot exclude cFFR as a method for determining significant stenoses in people with high CAC. A key issue is accurate and detailed lumen segmentation based on good-quality CCTA images.

Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus

BackgroundDiabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients.MethodsR language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC.ResultsThe results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset.ConclusionsIn this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.

Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT Angiography

Patients with atherosclerosis have a rather high risk of showing complications, if not diagnosed quickly and efficiently. Objective: In this paper we aim to test and compare different pre-trained deep learning models, to find the best model for atherosclerosis detection in coronary CT angiography. We experimented with different pre-trained deep learning models and fine-tuned each model to achieve the best classification accuracy. We then used the Haar wavelet decomposition to improve the model’s sensitivity We found that the Resnet101 architecture had the best performance with an accuracy of 95.2%, 60.8% sensitivity, and 90.48% PPV. Compared to the state of the art which uses a 3D CNN and achieved 90.9% accuracy, 68.9% Sensitivity and 58.8% PPV, sensitivity was quite low. To improve the sensitivity, we chose to use the Haar wavelet decomposition and trained the CNN model with the module of the three details: Low_High, High_Low, and High_High. The best sensitivity reached 80% with the CNN_KNN classifier. It is possible to perform atherosclerosis detection straight from CCTA images using a pretrained Resnet101, which has good accuracy and PPV. The low sensitivity can be improved using Haar wavelet decomposition and CNN-KNN classifier.

Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study

BackgroundArtificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making. MethodsOne hundred cases were reviewed by 3 highly experienced practicing cardiologists who are SCCT level 3 CCTA readers. Patients had varying levels of calcium (median CACS: 99.5) and CAD-RADS scores. Initial management plan for each case was a majority decision based upon patient demographics, clinical history, and CCTA report. AI-QCPA was then provided for each patient, and the plan was reconsidered. The primary endpoint was the reclassification rate (RR). In a secondary analysis of 40 cases, the above process was repeated but the initial plan was based upon review of the actual CCTA images. ResultsRR following AI-QCPA review was 66% (66/100) of cases (95% CI, 56.72%-75.28%). RR ranged from 47% in cases with CACS 0 to 96% in cases with CACS >400, and from 40% in CAD-RADS 1 cases to 94% in CAD-RADS 4 cases. RR was higher in cases with coronary stenoses ≥50% (89.5%) vs cases with stenoses <50% (51.6%). RR was 39% in cases with LDL <70 mg/dL vs 70% in LDL ≥70 mg/dL. Following review of the CCTA images rather than the CCTA report, the RR was 50% (95% CI of 34.51% - 65.49%). The primary reclassification effect was to intensify preventative medical therapy. ConclusionsAdding AI-QCPA to CCTA alone leads to a change in clinical care in two-thirds of patients.

Relationship Between Coronary Artery Inflammation And The Presence Of Atrial Fibrillation Assessed By Ccta Imaging

Relationship Between Coronary Artery Inflammation And The Presence Of Atrial Fibrillation Assessed By Ccta Imaging

Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.

Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data. Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that included all-cause mortality, myocardial infarction, or late revascularization. We employed an Automated Machine Learning (AutoML) approach to predict MACE using clinical, CCTA, and SPECT data. Various mainstream models with different sets of hyperparameters have been explored, and critical predictors of risk are obtained using explainable techniques on the global and patient levels. Ten-fold cross-validation was used in training and evaluating the AutoML model. A total of 956 patients were included (mean age 61.1 ±14.2 years, 54% men, 89% hypertension, 81% diabetes, 84% dyslipidemia). Obstructive CAD on CCTA and ischemia on SPECT were observed in 14% of patients, and 11% experienced MACE. ML prediction's sensitivity, specificity, and accuracy in predicting a MACE were 69.61%, 99.77%, and 96.54%, respectively. The top 10 global predictive features included 8 CCTA attributes (segment involvement score, number of vessels with severe plaque ≥70, ≥50% stenosis in the left marginal coronary artery, calcified plaque, ≥50% stenosis in the left circumflex coronary artery, plaque type in the left marginal coronary artery, stenosis degree in the second obtuse marginal of the left circumflex artery, and stenosis category in the marginals of the left circumflex artery) and 2 clinical features (past medical history of MI or left bundle branch block, being an ever smoker). ML can accurately predict risk of developing a MACE in patients suspected of CAD undergoing SPECT MPI and CCTA. ML feature-ranking can also show, at a sample- as well as at a patient-level, which features are key in making such a prediction.

Prognostic value of a novel artificial intelligence-based coronary computed tomography angiography-derived ischemia algorithm for patients with suspected coronary artery disease

Abstract Introduction Coronary computed tomography angiography (CCTA) has emerged as the first-line, non-invasive imaging tool to detect coronary artery disease (CAD) in patients with low to intermediate pre-test probability of CAD. However, CCTA cannot directly assess the presence of ischemia, which may lead to further downstream testing. A novel artificial-intelligence (AI)-based algorithm, comprising a machine-learned method that leverages atherosclerosis and vascular morphology features to determine vessel-level coronary ischemia from CCTA images, may be useful in this regard. Purpose We aimed to investigate the prognostic value of the AI-based ischemia algorithm for clinical events among symptomatic patients with suspected CAD referred for CCTA. Methods The AI-based ischemia algorithm was calculated by analysts blinded for patient characteristics and clinical outcomes using CCTA data from patients with suspected CAD who underwent clinically indicated CCTA at a large tertiary care center from February 2007 to December 2016. The primary endpoint was the composite of death, myocardial infarction (MI), and unstable angina pectoris (uAP) throughout a median follow-up of 8.1 years (IQR 6.0-9.6). Secondary endpoints were the association between the AI-based ischemia algorithm and the primary endpoint among patients with no/non-obstructive (i.e. ≤50%) CAD vs. obstructive (i.e. &amp;gt;50% stenosis) CAD in CCTA. Results 1574 patients were evaluated by the AI-based ischemia algorithm and 1540 were analyzable. Patients with positive AI-based ischemia findings (30.4%, n=468) had a significantly higher crude rate of the primary endpoint as compared to patients with negative AI-based ischemia findings (69.6%, n=1072) (HR 2.37, 95% CI 1.80-3.11, p&amp;lt;0.001), driven by higher rates of all endpoint composites (death: HR 1.48, 95% CI 1.13-2.22, p=0.007; MI: HR 5.42, 95% CI 3.10-9.49, p&amp;lt;0.001; uAP: HR 6.76, 95% CI 3.01-15.20, p&amp;lt;0.001). Results were consistent after adjusting for clinical confounders (age, sex, diabetes mellitus, smoking, hypertension, dyslipidemia, BMI&amp;gt;25 kg/m2, CAD family history) (primary endpoint: HR 1.66, 95% CI 1.24-2.22, p=0.001). Positive AI-based ischemia findings were associated with a significantly higher rate of the primary endpoint among patients with no/non-obstructive CAD in CCTA (n=1001/1487) (HR 2.56, 95% CI 1.53-4.27, p&amp;lt;0.001), but not among those with anatomically obstructive CAD in CCTA (n=486/1487) (HR 1.08, 95% CI 0.70-1.66, p=0.715). Conclusions A novel AI-based CCTA-derived ischemia algorithm was associated with a 1.7-fold increased adjusted rate of long-term death, MI, or uAP. This AI-based ischemia algorithm may be helpful to improve risk stratification, especially among patients with normal/non-obstructive CCTA.Kaplan Meier CCTA ≤50% and &amp;gt;50% stenosis

Incremental value of a CCTA-derived AI-based ischemia algorithm over standard CCTA interpretation for coronary ischemia in patients with suspected coronary artery disease

Abstract Background Coronary computed tomography angiography (CCTA) is a widely used non-invasive test to diagnose coronary artery stenosis in patients with suspected coronary artery disease (CAD). Positive predictive value for ischemia has been suboptimal using clinical reading. A novel AI-based ischemia algorithm applying machine learning that considers an array of atherosclerotic and vascular morphology features from CCTA images has been suggested to improve the diagnostic performance for ischemia. Purpose This study aims to evaluate the incremental diagnostic performance of CCTA derived AI-based ischemia algorithm in evaluating myocardial ischemia. Methods Patients with suspected CAD referred for CCTA at Turku University Hospital from February 2007 to December 2016 were analysed. The AI-based ischemia algorithm was calculated by analysts blinded for patient characteristics and clinical outcomes using typically-acquired CCTA images. In diagnostic reading significant stenosis defined by a stenosis &amp;gt; 50% on CCTA. The reference standard was ischemia detected by stress 15O-H2O positron emission tomography (PET). Results A total of 1445 patients (mean age 63 ± 9 years, 46% male) were included in this analysis. Myocardial ischemia by PET was detected in 384 (27%) patients while the other patients had either normal PET or had non-obstructive CAD by CCTA. A positive AI-based ischemia algorithm was found in 431 (30%) patients. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the AI-based ischemia algorithm for the assessment of myocardial ischemia were 83%, 74%, 86%, 66%, and 90%, respectively. Compared with diagnostic reading of CCTA (area under the receiver operating characteristic curve [AUC], 0.853; 95%CI 0.832-0.874), the model including clinical reading and AI-based ischemia algorithm demonstrated superior discrimination of myocardial ischemia (AUC 0.883; 95%CI 0.863-0.904, p-value &amp;lt;0.001). (Figure) Conclusions A novel AI-based ischemia algorithm derived from CCTA improves diagnostic accuracy compared with clinical reading of CCTA in assessing myocardial ischemia by PET perfusion imaging in patients with suspected CAD.Figure

Influence of a new motion correction algorithm (CardioCapture) on the correlation between heart rate and optimal reconstruction phase

PurposeTo investigate the effect of a new motion correction algorithm (CardioCapture) on the correlation between heart rate and optimal reconstruction phase by evaluating the impact of wide detector CT combined with CardioCapture on CCTA image quality. Materials and methodsAll cases were examined from April 2021 to September 2021. Two experienced radiologists scored these images on a four-point Likert scale. First, all images were divided into eight groups according to HR (at an interval of 5 bpm). The subjective score of images, the frequency of used CardioCapture, and the proportion of the diastolic reconstruction phase were compared in each group. Then, all cases were divided into two groups, one group was reconstructed using the automatic temporal reconstruction algorithm (Ephase) only, and the other group was reconstructed using the Ephase with the CardioCapture. The relationship between HR and the diastolic reconstruction phase was analyzed by the receiver operator characteristic curve (ROC). ResultThe data of 515 patients were studied. With the increase in HR, the subjective image score decreased, the frequency of CardioCapture increased, and the phase ratio of diastolic reconstruction decreased. When the HR was less than 70 bpm, the percentage of excellence image in each group surpassed 94.90%. The highest utilization rate of CardioCapture was 65.22%, and the lowest proportion of diastolic reconstruction was 72.46%. When 70 bpm < HR ≤ 75 bpm, the image excellence rate was 90.43%, the CardioCapture utilization rate was 82.05%, and the diastolic reconstruction rate was 56.41%.When 75 bpm < HR ≤ 80 bpm, the image excellence rate was 87.91%, the CardioCapture utilization rate was 80.65%, and the diastolic reconstruction was 6.45%.When the HR > 80 bpm, the image excellence rate was 80.00%, the CardioCapture utilization rate was 75.00%, and the diastolic reconstruction rate was 22.50%. The best cut-off point between HR and the diastolic reconstruction ROC curve in the groups without CardioCapture was 65 bpm, while that in groups with CardioCapture was 68 bpm. ConclusionThe CardioCapture can effectively improve the image quality of CCTA with high HR. By maintaining the HR below 68 bpm and utilizing the prospective ECG-gated narrow phase axial scan, it is possible to ensure optimal image quality and concurrently reduce radiation dose.

Positive-unlabeled learning for coronary artery segmentation in CCTA images

Accurate three-dimensional (3D) segmentation of the coronary artery is an essential step in the quantitative analysis of the coronary arteries. However, due to the small size and complex morphology of the coronary arteries, voxel-by-voxel labeling of the complete coronary artery in 3D computed coronary tomography angiography images is both difficult and laborious. To alleviate the workload of annotating, it is possible to randomly label only a fraction of the positive samples and leave all remaining instances unlabeled, known as the positive-unlabeled (PU) learning problem. Due to the presence of coronary artery-like structures and the absence of negative annotations, we propose a novel sample-selection-based PU learning method for coronary artery segmentation. Specifically, only pseudo-negative labels (PNLs) are generated during the self-training process, and all data are further exploited implicitly using the teacher–student (TS) framework. To address the difficulty of detecting tiny coronary artery branches, we propose a post-processing method by exploiting the variance of multi-scale features in the inference stage. Extensive experiments were conducted on a self-constructed dataset and the publicly available ASOCA dataset. The results demonstrate that our proposed method performs better than baseline supervised and state-of-the-art PU learning methods. Notably, even in extreme cases where more than 80% of annotations are missing, our method still achieves significant gains. When the proportion of missing annotations is relatively low, our method even outperforms the backbone trained with ground truth annotations.

Rationale and design of the INVICTUS Registry: (Multicenter Registry of Invasive and Non-Invasive imaging modalities to compare Coronary Computed Tomography Angiography, Intravascular Ultrasound and Optical Coherence Tomography for the determination of Severity, Volume and Type of coronary atherosclerosiS)

BackgroundCoronary CT angiography (CCTA) is a first-line noninvasive imaging modality for evaluating coronary artery disease (CAD). Recent advances in CCTA technology enabled semi-automated detection of coronary arteries and atherosclerosis. However, there have been to date no large-scale validation studies of automated assessment of coronary atherosclerosis phenotype and coronary artery dimensions by artificial intelligence (AI) compared to current standard invasive imaging. MethodsINVICTUS registry is a multicenter, retrospective, and prospective study designed to evaluate the dimensions of coronary arteries, as well as the characteristic, volume, and phenotype of coronary atherosclerosis by CCTA, compared with the invasive imaging modalities including intravascular ultrasound (IVUS), near-infrared spectroscopy (NIRS)-IVUS and optical coherence tomography (OCT). All patients clinically underwent both CCTA and invasive imaging modalities within three months. ResultsPatients data are sent to the core-laboratories to analyze for stenosis severity, plaque characteristics and volume. The variables for CCTA are measured using an AI-based automated software and assessed independently with the variables measured at the imaging core laboratories for IVUS, NIRS-IVUS, and OCT in a blind fashion. ConclusionThe INVICTUS registry will provide new insights into the diagnostic value of CCTA for determining coronary atherosclerosis phenotype and coronary artery dimensions compared to IVUS, NIRS-IVUS, and OCT. Our findings will potentially shed new light on precision medicine informed by an AI-based coronary CTA assessment of coronary atherosclerosis burden, composition, and severity. (ClinicalTrials.gov: NCT04066062).

Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer.

Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%.

Double Device Approach for Percutaneous Closure of Postmyocardial Infarction Ventricular Septal Defect

The benefit related to the use of preprocedural computed tomography angiography (CCTA) on top of periprocedural echocardiography to plan percutaneous left atrial appendage closure (LAAC) procedures is still unclear.The authors sought to evaluate the impact of preprocedural CCTA on LAAC procedural success.In the investigator-initiated SWISS-APERO (Comparison of Amplatzer Amulet and Watchman Device in Patients Undergoing Left Atrial Appendage Closure) trial, patients undergoing echocardiography-guided LAAC were randomly assigned to receive the Amulet (Abbott) or Watchman 2.5/FLX (Boston Scientific) device across 8 European centers. According to the study protocol ongoing at the time of the procedure, the first operators had (CCTA unblinded group) or did not have (CCTA blinded group) access to preprocedural CCTA images. In this post hoc analysis, we compared blinded vs unblinded procedures in terms of procedural success defined as complete left atrial appendage occlusion as evaluated at the end of LAAC (short-term) or at the 45-day follow-up (long-term) without procedural-related complications.Among 219 LAACs preceded by CCTA, 92 (42.1%) and 127 (57.9%) were assigned to the CCTA unblinded and blinded group, respectively. After adjusting for confounders, operator unblinding to preprocedural CCTA remained associated with a higher rate of short-term procedural success (93.5% vs 81.1%; P = 0.009; adjusted OR: 2.76; 95% CI: 1.05-7.29; P = 0.040) and long-term procedural success (83.7% vs 72.4%; P = 0.050; adjusted OR: 2.12; 95% CI: 1.03-4.35; P = 0.041).In a prospective multicenter cohort of clinically indicated echocardiography-guided LAACs, unblinding of the first operators to preprocedural CCTA images was independently associated with a higher rate of both short- and long-term procedural success. Further studies are needed to better evaluate the impact of preprocedural CCTA on clinical outcomes.

Impact of Preprocedural Computed Tomography on Left Atrial Appendage Closure Success: A Swiss-Apero Trial Subanalysis

BackgroundThe benefit related to the use of preprocedural computed tomography angiography (CCTA) on top of periprocedural echocardiography to plan percutaneous left atrial appendage closure (LAAC) procedures is still unclear. ObjectivesThe authors sought to evaluate the impact of preprocedural CCTA on LAAC procedural success. MethodsIn the investigator-initiated SWISS-APERO (Comparison of Amplatzer Amulet and Watchman Device in Patients Undergoing Left Atrial Appendage Closure) trial, patients undergoing echocardiography-guided LAAC were randomly assigned to receive the Amulet (Abbott) or Watchman 2.5/FLX (Boston Scientific) device across 8 European centers. According to the study protocol ongoing at the time of the procedure, the first operators had (CCTA unblinded group) or did not have (CCTA blinded group) access to preprocedural CCTA images. In this post hoc analysis, we compared blinded vs unblinded procedures in terms of procedural success defined as complete left atrial appendage occlusion as evaluated at the end of LAAC (short-term) or at the 45-day follow-up (long-term) without procedural-related complications. ResultsAmong 219 LAACs preceded by CCTA, 92 (42.1%) and 127 (57.9%) were assigned to the CCTA unblinded and blinded group, respectively. After adjusting for confounders, operator unblinding to preprocedural CCTA remained associated with a higher rate of short-term procedural success (93.5% vs 81.1%; P = 0.009; adjusted OR: 2.76; 95% CI: 1.05-7.29; P = 0.040) and long-term procedural success (83.7% vs 72.4%; P = 0.050; adjusted OR: 2.12; 95% CI: 1.03-4.35; P = 0.041). ConclusionsIn a prospective multicenter cohort of clinically indicated echocardiography-guided LAACs, unblinding of the first operators to preprocedural CCTA images was independently associated with a higher rate of both short- and long-term procedural success. Further studies are needed to better evaluate the impact of preprocedural CCTA on clinical outcomes.

A high-fidelity geometric multiscale hemodynamic model for predicting myocardial ischemia

Background and ObjectivesCoronary computed tomography angiography (CCTA) derived fractional flow reserve (CT-FFR) requires a maximal hyperemic state to be modeled by assuming the total coronary resistance decreased to a constant 0.24 of that under the resting state. However, this assumption neglects the vasodilator capacity of individual patients. Herein, we proposed a high-fidelity geometric multiscale model (HFMM) to characterize coronary pressure and flow under the resting state, seeking to better predict myocardial ischemia by using CCTA-derived instantaneous wave-free ratio (CT-iFR). MethodsFifty-seven patients (62 lesions) who had undergone CCTA and were then referred to invasive FFR were prospectively enrolled. The coronary microcirculation resistance hemodynamic model (RHM) under the resting condition was established on a patient-specific basis. Coupled with a closed-loop geometric multiscale model (CGM) of their individual coronary circulations, the HFMM model was established to non-invasively derive the CT-iFR from CCTA images. ResultsWith the invasive FFR being the reference standard, accuracy of the obtained CT-iFR in identifying myocardial ischemia was greater than those of the CCTA and non-invasively derived CT-FFR (90.32% vs. 79.03% vs. 84.3%). The overall computational time of CT-iFR was 61 ± 6 min, faster than that of the CT-FFR (8 h). The sensitivity, specificity, positive predictive value, and negative predictive value of the CT-iFR in discriminating an invasive FFR > 0.8 were 78% (95% CI: 40–97%), 92% (95% CI: 82–98%), 64% (95% CI: 39–83%), and 96% (95% CI:88–99%), respectively. ConclusionsA high-fidelity geometric multiscale hemodynamic model was developed for rapid and accurate estimation of CT-iFR. Compared with CT-FFR, CT-iFR is of less computational cost and enables assessment of tandem lesions.

A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.

Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent and lightweight coronary artery segmentation algorithm that can be deployed in hospital systems to assist clinicians in quantitatively analyzing CHD. With the multi-level feature fusion, we proposed Dual-Attention Coordination U-Net (DAC-UNet) that achieves automated coronary artery segmentation in 2D CCTA images. The coronary artery occupies a small region, and the foreground and background are extremely unbalanced. For this reason, the more original information can be retained by fusing related features between adjacent layers, which is conducive to recovering the small coronary artery area. The dual-attention coordination mechanism can select valid information and filter redundant information. Moreover, the complementation and coordination of double attention factors can enhance the integrity of features of coronary arteries, reduce the interference of non-coronary arteries, and prevent over-learning. With gradual learning, the balanced character of double attention factors promotes the generalization ability of the model to enhance coronary artery localization and contour detail segmentation. Compared with existing related segmentation methods, our method achieves a certain degree of improvement in 2D CCTA images for the segmentation accuracy of coronary arteries with a mean Dice index of 0.7920. Furthermore, the method can obtain relatively accurate results even in a small sample dataset and is easy to implement and deploy, which is promising. The code is available at: https://github.com/windfly666/Segmentation . Our method can capture the coronary artery structure end-to-end, which can be used as a fundamental means for automatic detection of coronary artery stenosis, blood flow reserve fraction analysis, and assisting clinicians in diagnosing CHD.

Abstract TP94: Optimizing The Utilization Of Cardiac Computed Tomography Angiography Among Patients With Ischemic Stroke

Background: The current AHA guidelines recommend (class IIb) advanced cardiac imaging in embolic stroke of unknown source (ESUS). We aim to better characterize the role of cardiac CTA (cCTA) in the evaluation of stroke patients. Method: A retrospective review of stroke patients at a comprehensive stroke center, who had cCTA between 12/2016 and 11/2020 was conducted using institutional registries. Ischemic stroke patients with ESUS, cryptogenic (with ESUS as a competing etiology) and cardio-embolic etiologies were included. Only cardioembolic etiologies in which a suspicion of intracardiac thrombus with either a known diagnosis of atrial fibrillation or presence of LVAD were included. Cases with TIA, large vessel disease, small vessel disease, dissection or hemodynamic etiologies were excluded. TTE and cCTA results from all cases were reviewed for remarkable findings, most notably intracardiac thrombus, and analyzed if a change in anti-thrombotic therapy was directly attributable to cCTA results. Results: As per our criteria, 96 cases (Female 42.7%, Black 37.5%, Age mean: 63.7 years) were included, of whom 45 (46.9%) were ESUS, 21 (21.9%) were cryptogenic, and 30 (31.2%) were cardioembolic. cCTA revealed intracardiac thrombus in 5 (5.2%) cases, all with LA thrombus. Of these, 3 (60%) had escalation of anti-thrombotic therapy. Interestingly, in one patient who had both cMRI and cCTA imaging, the cMRI revealed an apical LV thrombus which was not detected on cCTA. Conclusion: In this retrospective analysis of 96 select stroke cases, escalation of anti-thrombotic therapy from anti-platelets to full dose anti-coagulation was indicated in 3 patients (3.1%) with addition of cCTA. Compared to a similar cohort of patients from the same institutional registry using cMRI as the advanced cardiac imaging modality, cCTA did not reveal any LV thrombi and even missed a case of LV thrombus seen on cMRI suggesting lower sensitivity for cCTA to detect LV thrombus. Further analysis of data is in process to determine the subset of stroke patients who would benefit the most from cCTA.

Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease

Coronary artery disease (CAD) represents a widespread burden to both individual and public health, steadily rising across the globe. The current guidelines recommend non-invasive anatomical or functional testing prior to invasive procedures. Both coronary computed tomography angiography (cCTA) and stress cardiac magnetic resonance imaging (CMR) are appropriate imaging modalities, which are increasingly used in these patients. Both exhibit excellent safety profiles and high diagnostic accuracy. In the last decade, cCTA image quality has improved, radiation exposure has decreased and functional information such as CT-derived fractional flow reserve or perfusion can complement anatomic evaluation. CMR has become more robust and faster, and advances have been made in functional assessment and tissue characterization allowing for earlier and better risk stratification. This review compares both imaging modalities regarding their strengths and weaknesses in the assessment of CAD and aims to give physicians rationales to select the most appropriate modality for individual patients.