Coronary Computed Tomography Angiography Datasets Research Articles

Selective FFRCT testing in suspected stable angina in clinical practice - initial experiences

Coronary CT angiography (CTA) derived fractional flow reserve (FFRCT) is recommended for physiological assessment in intermediate coronary stenosis for guiding referral to invasive coronary angiography (ICA). In this study, we report real-world data on the feasibility of implementing a CTA/FFRCT test algorithm as a gatekeeper to ICA at referral hospitals. Retrospective all-comer study of patients with new onset stable symptoms and suspected coronary stenosis (30–89%) by CTA. Evaluation of CTA datasets, interpretation of FFRCT analysis, and decisions on downstream testing were performed by skilled CT-cardiologists. CTA was performed in 3974 patients, of whom 381 (10%) were referred directly to ICA, whereas 463 (12%) to non-invasive functional testing: FFRCT 375 (81%) and perfusion imaging 88 (19%). FFRCT analysis was rejected in 8 (2%) due to inadequate CTA image quality. Number of patients deferred from ICA after FFRCT was 267 (71%), while 100 (27%) were referred to ICA. Obstructive coronary artery disease (CAD) was confirmed in 62 (62%) patients and revascularization performed in 53 (53%). Revascularization rates, n (%), were higher in patients undergoing FFRCT-guided versus CTA-guided referral to ICA: 30–69% stenosis, 28 (44%) versus 8 (21%); 70–89% stenosis, 39 (69%) versus 25 (46%), respectively, both p < 0.05. Implementation of FFRCT at referral hospitals was feasible, reduced the number of invasive procedures, and increased the revascularization rate.

Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements

PurposeTo assess the impact of different quantum iterative reconstruction (QIR) levels on objective and subjective image quality of ultra-high resolution (UHR) coronary CT angiography (CCTA) images and to determine the effect of strength levels on stenosis quantification using photon-counting detector (PCD)-CT. MethodA dynamic vessel phantom containing two calcified lesions (25 % and 50 % stenosis) was scanned at heart rates of 60, 80 and 100 beats per minute with a PCD-CT system. In vivo CCTA examinations were performed in 102 patients. All scans were acquired in UHR mode (slice thickness0.2 mm) and reconstructed with four different QIR levels (1–4) using a sharp vascular kernel (Bv64). Image noise, signal-to-noise ratio (SNR), sharpness, and percent diameter stenosis (PDS) were quantified in the phantom, while noise, SNR, contrast-to-noise ratio (CNR), sharpness, and subjective quality metrics (noise, sharpness, overall image quality) were assessed in patient scans. ResultsIncreasing QIR levels resulted in significantly lower objective image noise (in vitro and in vivo: both p < 0.001), higher SNR (both p < 0.001) and CNR (both p < 0.001). Sharpness and PDS values did not differ significantly among QIRs (all pairwise p > 0.008). Subjective noise of in vivo images significantly decreased with increasing QIR levels, resulting in significantly higher image quality scores at increasing QIR levels (all pairwise p < 0.001). Qualitative sharpness, on the other hand, did not differ across different levels of QIR (p = 0.15). ConclusionsThe QIR algorithm may enhance the image quality of CCTA datasets without compromising image sharpness or accurate stenosis measurements, with the most prominent benefits at the highest strength level.

Image Characteristics of Virtual Non-Contrast Series Derived from Photon-Counting Detector Coronary CT Angiography-Prerequisites for and Feasibility of Calcium Quantification.

In photon-counting detector CT (PCD-CT), coronary artery calcium scoring (CACS) can be performed using virtual non-contrast (VNC) series derived from coronary CT angiography (CCTA) datasets. Our study analyzed image characteristics of VNC series in terms of the efficacy of virtual iodine "removal" and image noise to determine whether the prerequisites for calcium quantification were satisfied. We analyzed 38 patients who had undergone non-enhanced CT followed by CCTA on a PCD-CT. VNC reconstructions were performed at different settings and algorithms (conventional VNCConv; PureCalcium VNCPC). Virtual iodine "removal" was investigated by comparing histograms of heart volumes. Noise was assessed within the left ventricular cavity. Calcium was quantified on the true non-contrast (TNC) and all VNC series. The histograms were comparable for TNC and all VNC. Image noise between TNC and all VNC differed slightly but significantly. VNCConv CACS showed a significant underestimation regardless of the reconstruction setting, while VNCPC CACS were comparable to TNC. Correlations between TNC and VNC were excellent, with a higher predictive accuracy for VNCPC. In conclusion, the iodine contrast can be effectively subtracted from CCTA datasets. The remaining VNC series satisfy the requirements for CACS, yielding results with excellent correlation compared to TNC-based CACS and high predicting accuracy.

Derivation and validation of a novel functional FFRCT score incorporating the burden of coronary stenosis severity and flow impairment to predict clinical events

BackgroundA score combining the burden of stenosis severity on coronary computed tomography angiography (CCTA) and flow impairment by fractional flow reserve derived from computed tomography (FFRCT) may be a better predictor of clinical events than either parameter alone. MethodsThe Functional FFRCT Score (FFS) combines CCTA and FFRCT parameters in an allocated point-based system. The feasibility of the FFS was assessed in cohort of 72 stable chest pain patients with matched CCTA and FFRCT datasets. Validation was performed using 2 cohorts: (a) 4468 patients from the ADVANCE Registry to define its association with revascularization and major adverse cardiovascular events (MACE); (b) 212 patients from the FORECAST trial to determine predictors of MACE. ResultsThe median calculation time for the FFS was 10 (interquartile range 6–17) seconds, with strong intra-operator and inter-operator agreement (Cohen's Kappa 0.89 (±0.37, p ​< ​0.001) and 0.83 (±0.04, p ​< ​0.001, respectively). The FFS correlated strongly with both the CT-SYNTAX and the Functional CT-SYNTAX scores (rS ​= ​0.808 for both, p ​< ​0.001).In the ADVANCE cohort the FFS had good discriminatory abilities for revascularization with an area under the curve of 0.82, 95 ​% confidence interval (CI) 0.81–0.84, p ​< ​0.001. Patients in the highest FFS tertile had significantly higher rates of revascularization (61 ​% vs 5 ​%, p ​< ​0.001) and MACE (1.9 ​% vs 0.5 ​%, p ​= ​0.001) compared with the lowest FFS tertile.In the FORECAST cohort the FFS was an independent predictor of MACE at 9-month follow-up (hazard ratio 1.04, 95 ​% CI 1.01–1.08, p ​< ​0.01). ConclusionThe FFS is a quick-to-calculate and reproducible score, associated with revascularization and MACE in two distinct populations of stable symptomatic patients.

Improved Coronary Artery Visualization Using Virtual Monoenergetic Imaging from Dual-Layer Spectral Detector CT Angiography.

Background: To evaluate if coronary CT angiography (CCTA) monoenergetic reconstructions, obtained with a dual-layer spectral detector computed tomography (DLCT) system, offer improved image quality compared with 120 kVp conventional images without affecting the quantitative assessment of coronary stenoses. Methods: Fifty CCTA datasets (30 men; mean age: 61.6 ± 12.3 years) acquired with a DLCT system were reconstructed using virtual monoenergetic images (VMI) from 40 to 100 keV with 10 keV increment and compared with conventional images. An analysis of objective image quality was performed, evaluating the signal- and contrast-to-noise ratio. For the subjective assessment, two readers used a 5-point Likert scoring system to evaluate sharpness, noise, demarcation of coronary plaques, vascular contrast, and an overall score. Furthermore, coronary stenoses were analyzed for each vessel to describe the diagnostic agreement between monoenergetic images and conventional images. Results: The objective image analysis showed that all reconstructions from 70 keV to 40 keV show higher SNR (from 61.33 ± 12.46 to 154.22 ± 42.91, respectively) and CNR (from 51.45 ± 11.19 to 135.63 ± 39.38, respectively) compared with conventional images (all p < 0.001). The 40 keV monoenergetic images obtained the best average score for sharpness, vascular contrast, and for the overall impression (all with p < 0.001). The detection and grading of stenoses of the coronary arteries with conventional and monoenergetic images at 70 keV and 40 keV showed an overall excellent interobserver agreement (k= 0.81 [0.72-0.91]). Conclusions: The 40 keV virtual monoenergetic images obtained with a DLCT system allow the objective and subjective image quality of coronary CT angiography to be improved.

Calcium scoring on coronary computed angiography with photon-counting technology: predictors of performance

Abstract Funding Acknowledgements Type of funding sources: None. Background Spectral CT imaging and virtual non-contrast (VNC) reconstruction techniques allow coronary artery calcium score (CACS) calculations from coronary CT angiography (CCTA) datasets. A novel virtual material decomposition algorithm (PureCalcium, PC) developed for photon-counting detector CT (PCD-CT) yields the potential of further increasing the accuracy of CCTA-based CACS measurements, however, little is known about the means of optimizing its performance for routine Agatston-scoring. Purpose The aim of the current study was to determine the predictors of the performance of a novel virtual non-iodine algorithm for PCD-CT. Materials and Methods Calcium scoring on non-enhanced scans is predominantly used as a complementary examination to CCTA increasing both acquisition time and radiation dose, thus, acquiring accurate CACS from CCTA datasets are warranted. In the current study, CACS and CAC volumes were calculated using true non-contrast (TNC) images and PC reconstructions in 139 consecutive patients (61.5 ± 10.9 years, 41.7% female) undergoing clinically indicated CCTA. Relative differences (TNC–PC/TNC) of PC-based CACS calculations from non-enhanced scans was calculated for each patient. Predictors of the degree of discrepancy were determined using linear regression analysis. Results Overall, BMI and heart rate did not affect the accuracy of PC measurements. However, the application of a higher tube voltage (140 kVp instead of 120 kVp) significantly decreased the relative difference of PC-based measurements from TNC values (β=-0.33 [95%CI: −0.64 – −0.03]). Furthermore, the average volume/lesion ratio also proved to be an independent predictor of performance (β=-0.01 [95%CI: −0.02 – −0.004]). Conclusions Spectral imaging based PC reconstructions may substitute conventional calcium scoring for a selected population of patients. It seems plausible that its performance can be improved by increased tube voltage protocols, provided spectral information gathered from CTA datasets is enhanced by using 140 kVp. It must be noted that the accuracy of PC may be hindered for smaller calcified lesions.

Latent Coronary Plaque Morphology From Computed Tomography Angiography, Molecular Disease Activity on Positron Emission Tomography, and Clinical Outcomes.

Assessments of coronary disease activity with 18F-sodium fluoride positron emission tomography and radiomics-based precision coronary plaque phenotyping derived from coronary computed tomography angiography may enhance risk stratification in patients with coronary artery disease. We sought to investigate whether the prognostic information provided by these 2 approaches is complementary in the prediction of myocardial infarction. Patients with known coronary artery disease underwent coronary 18F-sodium fluoride positron emission tomography and coronary computed tomography angiography on a hybrid positron emission tomography/computed tomography scanner. Coronary 18F-NaF uptake was determined by the coronary microcalcification activity. We performed quantitative plaque analysis of coronary computed tomography angiography datasets and extracted 1103 radiomic features for each plaque. Using weighted correlation network analysis, we derived latent morphological features of coronary lesions which were aggregated to patient-level radiomics nomograms to predict myocardial infarction. Among 260 patients with established coronary artery disease (age, 65±9 years; 83% men), 179 (69%) participants showed increased coronary 18F-NaF activity (coronary microcalcification activity>0). Over 53 (40-59) months of follow-up, 18 patients had a myocardial infarction. Using weighted correlation network analysis, we derived 15 distinct eigen radiomic features representing latent morphological coronary plaque patterns in an unsupervised fashion. Following adjustments for calcified, noncalcified, and low-density noncalcified plaque volumes and 18F-NaF coronary microcalcification activity, 4 radiomic features remained independent predictors of myocardial infarction (hazard ratio, 1.46 [95% CI, 1.03-2.08]; P=0.03; hazard ratio, 1.62 [95% CI, 1.04-2.54]; P=0.02; hazard ratio, 1.49 [95% CI, 1.07-2.06]; P=0.01; and hazard ratio, 1.50 (95% CI, 1.05-2.13); P=0.02). In patients with established coronary artery disease, latent coronary plaque morphological features, quantitative plaque volumes, and disease activity on 18F-sodium fluoride positron emission tomography are additive predictors of myocardial infarction.

High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography

BackgroundPlaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and resultsThe coronary artery tree was manually segmented using semi-automatic software by four primary and one senior secondary reader. A randomly selected sample of 469 subjects, all with coronary plaques and stratified for cardiovascular risk using the Systematic Coronary Risk Evaluation (SCORE), were analyzed. The reproducibility study (n = 78) showed an agreement for plaque detection of 0.91 (0.84–0.97). The mean percentage difference for plaque volumes was −0.6% the mean absolute percentage difference 19.4% (CV 13.7%, ICC 0.94). There was a positive correlation between SCORE and total plaque volume (rho = 0.30, p < 0.001) and total low attenuation plaque volume (rho = 0.29, p < 0.001). ConclusionsWe have generated a CCTA dataset with high-quality plaque annotations showing good reproducibility and an expected correlation between plaque features and cardiovascular risk. The stratified data sampling has enriched high-risk plaques making the data well suited as training, validation and test data for a fully automatic analysis tool based on deep learning.

Investigation of the Relationship between Right Coronary Artery-Aorta Angle and Coronary Artery Disease and Associated Risk Factors.

At the level of the left coronary artery tree, there is evidence showing an association between bifurcation angle and coronary artery disease (CAD), and this motivated us to explore similar associations at the level of the right coronary artery (RCA). The purpose of this study was to determine whether there is a relationship between RCA-aorta angle and CAD and age, sex, body mass index, smoking status, hypertension, and high blood cholesterol. The coronary computed tomography angiography datasets and CAD risk factor checklists of 250 patients were retrospectively reviewed, with RCA-aorta angles measured via multiplanar reformation images. Independent t-tests were used to compare mean RCA-aorta angle measurements between groups, correlations between continuous variables were assessed using Pearson and Spearman correlations, and a general linear model was used to adjust for potentially confounding variables. Coronary angle measurements were conducted by two independent assessors with very strong intraclass correlation (r=0.999, p<0.001). A significantly smaller mean RCA-aorta angle was observed in the CAD group (79.07 ± 24.88°) compared to the normal group (92.08 ± 19.51°, p=0.001), in smokers (76.63 ± 22.94°) compared to non-smokers (85.25 ± 23.84°, p=0.016), and a narrow RCA-aorta angle was negatively correlated with BMI (r=-0.174, p=0.010). This study suggests a relationship between narrow RCA-aorta angles and CAD, smoking, and increasing BMI.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound

BackgroundAdvances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. MethodsMatched cross-sections (n ​= ​7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 ​mm and 0.50 ​mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. ResultsSegment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 ​mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p ​< ​0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p ​< ​0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64–0.67; p ​= ​0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p ​< ​0.001 and plaque burden ICC: 0.45, RC: 32.0, p ​< ​0.001). ConclusionCCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. Clinicaltrials.gov (NCT03556644)

Vessel filtering and segmentation of coronary CT angiographic images.

Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images. In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images. The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods. The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm

ObjectivesLiver transplantation (LT) is associated with high stress on the cardiovascular system. Ruling out coronary artery disease (CAD) is an important part of evaluation for LT. The aim of our study was to assess whether CT-derived fractional flow reserve (CT-FFR) allows for differentiation of hemodynamically significant and non-significant coronary stenosis in patients evaluated for LT.MethodsIn total, 201 patients undergoing LT evaluation were included in the study. The patients received coronary computed tomography angiography (CCTA) to rule out CAD and invasive coronary angiography (ICA) to further evaluate coronary lesions found in CCTA if a significant (≥ 50 % on CCTA) stenosis was suspected. CT-FFR was computed from CCTA datasets using a machine learning–based algorithm and compared to ICA as a standard of reference. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant.ResultsIn 127 of 201 patients (63%), an obstructive CAD was ruled out by CCTA. In the remaining 74 patients (37%), at least one significant stenosis was suspected in CCTA. Compared to ICA, sensitivity, specificity, PPV, and NPV of the CT-FFR measurements were 71% (49–92%), 90% (82–98%), 67% (45–88%), and 91% (84–99%), respectively. The diagnostic accuracy was 85% (85–86%). In 69% of cases (52 of 75 lesions), additional analysis by CT-FFR correctly excluded the hemodynamic significance of the stenosis.ConclusionsMachine learning–based CT-FFR seems to be a very promising noninvasive approach for exclusion of hemodynamic significant coronary stenoses in patients undergoing evaluation for LT and could help to reduce the rate of invasive coronary angiography in this high-risk population.Key Points• Machine learning–based computed tomography-derived fractional flow reserve (CT-FFR) seems to be a very promising noninvasive approach for exclusion of hemodynamic significance of coronary stenoses in patients undergoing evaluation for liver transplantation and could help to reduce the rate of invasive coronary angiography in this high-risk population.

Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA). Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA. Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

Clinical value of resting cardiac dual-energy CT in patients suspected of coronary artery disease

BackgroundRest/stress myocardial CT perfusion (CTP) has high diagnostic value for coronary artery disease (CAD), but the additional value of resting CTP especially dual-energy CTP (DE-CTP) beyond coronary CT angiography (CCTA) in chest pain triage remains unclear. We aimed to evaluate the diagnostic accuracy of resting myocardial DE-CTP, and additional value in detecting CAD beyond CCTA (obstructive stenosis: ≥ 50%) in patients suspected of CAD.MethodsIn this prespecified subanalysis of 54 patients, we included patients suspected of CAD referred to invasive coronary angiography (ICA). Diagnostic accuracy of resting myocardial DE-CTP in detecting myocardial perfusion defects was assessed using resting 13N-ammonia positron emission tomography (PET) as the gold standard. Diagnostic accuracy of cardiac dual-energy CT in detecting flow-limiting stenoses (justifying revascularization) by CCTA combined with resting myocardial DE-CTP, using ICA plus resting 13N-ammonia PET as the gold standard. The CCTA and DE-CTP datasets derived from a single-phase scan performed with dual-energy mode.ResultsFor detecting myocardial perfusion defects, DE-CTP demonstrated high diagnostic accuracy with a sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 95.52%, 85.93%, and 0.907 on a per-segment basis. For detecting flow-limiting stenoses by CCTA alone, sensitivity, specificity, and AUC were 100%, 56.47%, and 0.777 respectively on a per-vessel basis. For detecting flow-limiting stenoses by CCTA combined with resting myocardial DE-CTP, sensitivity, specificity, and AUC were 96.10%, 95.29% and 0.956 respectively on a per-vessel basis. Additionally, CCTA combined with resting myocardial DE-CTP detected five patients (9%) with no obstructive stenosis but with myocardial perfusion defects confirmed by ICA plus 13N-ammonia PET.ConclusionsResting cardiac DE-CTP demonstrates a high diagnostic accuracy in detecting myocardial perfusion defects and provides an additional clinical value by reducing rates of false-positive and false-negative patients beyond CCTA in patients suspected of CAD.

Model based adaptive filter: assessment of image noise, sharpness and quality in coronary CT angiography

Abstract Funding Acknowledgements Type of funding sources: None. On Behalf of Cardiovascular Imaging Research Group Background The effect of filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) on coronary CT angiography (CCTA) images have been extensively studied and such algorithms are ubiquitously applied in the reconstruction of CCTA datasets. Currently, however, no data is available on the impact of a recently developed model based adaptive filter (MBAF). Purpose Our aim was to determine the effect of MBAF on the image quality of prospectively gated CCTA datasets. Methods We evaluated the images of 102 consecutive patients who underwent clinically indicated CCTA at our department. Four reconstructions of coronary cross-sectional images (FBP, ASIR, MBAF, ASIR + MBAF) were co-registered and assessed for qualitative (graininess, sharpness, overall image quality) and quantitative [image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)] image quality parameters. Image noise and signal were measured in the aortic root and the left main coronary artery, respectively. Graininess, sharpness, and overall image quality was assessed on a 5-point Likert scale by two experienced readers blinded to the reconstruction algorithm. Results No difference in sharpness was observed amongst the reconstructions (p = 0.08). Although ASIR + MBAF was non-superior to ASIR regarding overall image quality (p = 0.99), it performed better than FBP (p &amp;lt; 0.001), and MBAF (p &amp;lt; 0.001) alone. As compared to FBP, ASIR, and MBAF, the combination of ASIR and MBAF resulted in reduced image noise [53 ± 12, 31 ± 9, 36 ± 4, and 26 ± 4 Hounsfield units (HU), respectively; p &amp;lt; 0.001] and improved SNR (8 ± 3, 14 ± 4, 12 ± 2,16 ± 3 HU, respectively; p &amp;lt; 0.001) and CNR (17 ± 3, 16 ± 4, 13 ± 2, 18 ± 4 HU, respectively; p &amp;lt; 0.001). Conclusion The combination of ASIR and MBAF resulted in reduced image noise and improved SNR and CNR. The implementation of MBAF in clinical practice may result in easier interpretation of CCTA images and could potentiate radiation dose reduction.

The revolution project: replacing coronary artery angiography with coronary computed tomography with functional evaluation.

In the last two decades, several studies and widespread clinical use demonstrated that coronary computed tomography angiography (CCTA) is an appropriate method for the non-invasive assessment of patients with suspected stable coronary artery disease (CAD) and low-to-intermediate pretest likelihood of CAD. Moreover, a growing body of literature is showing that CCTA may have also a clinical role in patients with high pretest likelihood of CAD, known CAD and complex and diffuse disease. Particularly, the SYNTAX II trial demonstrated the feasibility of planning interventional and surgical coronary procedures with CCTA thanks to its ability to combine, in a single method, precise stenosis quantification, accurate plaque characterization, functional assessment with fractional flow reserve derived from standard acquired CCTA datasets, and selection of the revascularization modality for any individual patient and of the vessels that need to be revascularized. More recently, the SYNTAX III Revolution trial showed, in patients with three-vessel CAD with or without left main involvement, that treatment decision-making between percutaneous coronary intervention and coronary artery bypass grafting based on CCTA only has an almost perfect agreement with the treatment decision derived from invasive coronary angiography (ICA). The high degree of correlation between CCTA and ICA suggests the potential feasibility of treatment decision-making based solely on non-invasive imaging and clinical information. New research prospects have opened up for the future to demonstrate the true feasibility and safety of this innovative approach in the clinical arena.

Coronary flow impairment in asymptomatic patients with early stage type-2 diabetes: Detection by FFRCT.

Purpose:To determine the occurrence of physiological significant coronary artery disease (CAD) by coronary CT angiography (CTA) derived fractional flow reserve (FFRCT) in asymptomatic patients with a new diagnosis (<1 year) of type-2 diabetes mellitus (T2DM).Methods:FFRCT-analysis was performed from standard acquired coronary CTA data sets. The per-patient minimum distal FFRCT-value (d-FFRCT) in coronary vessels (diameter ⩾1.8 mm) was registered. The threshold for categorizing FFRCT-analysis as abnormal was a d-FFRCT ⩽0.75. Total plaque volume and volumes of calcified plaque, non-calcified plaque, and low-density non-calcified plaque (LD-NCP) were assessed by quantitative plaque analysis.Results:Overall, 76 patients; age, mean (SD): 56 (11) years; males, n (%): 49(65), were studied. A total of 57% of patients had plaques. The d-FFRCT was ⩽0.75 in 12 (16%) patients. The d-FFRCT, median (IQR), was 0.84 (0.79–0.87). Median (range) d-FFRCT in patients with d-FFRCT ⩽0.75 was 0.70 (0.6–0.74). Patients with d-FFRCT⩽0.75 versus d-FFRCT >0.75 had numerically higher plaque volumes for all plaques components, although only significant for the LD-NCP component.Conclusion:Every sixth asymptomatic patient with a new diagnosis of T2DM has hemodynamic significant CAD as evaluated by FFRCT. Flow impairment by FFRCT was associated with coronary plaque characteristics.

Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network

We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to model pathological changes (e.g., atherosclerotic plaques) in coronary vessels. The system learns the discriminatory features between vessels with and without atherosclerosis. The discriminative features at the final convolutional layer are visualized with a saliency map approach to provide visual clues related to atherosclerosis likelihood and location. We have evaluated the system on a reference dataset representing 247 patients with atherosclerosis and 246 patients free of atherosclerosis. With five fold cross-validation, an Accuracy = 90.9%, Positive Predictive Value = 58.8%, Sensitivity = 68.9%, Specificity of 93.6%, and Negative Predictive Value (NPV) = 96.1% are achieved at the artery/branch level with threshold 0.5. The average area under the receiver operating characteristic curve is 0.91. The system indicates a high NPV, which may be potentially useful for assisting interpreting physicians in excluding coronary atherosclerosis in patients with acute chest pain.

Learning tree-structured representation for 3D coronary artery segmentation

Extensive research has been devoted to the segmentation of the coronary artery. However, owing to its complex anatomical structure, it is extremely challenging to automatically segment the coronary artery from 3D coronary computed tomography angiography (CCTA). Inspired by recent ideas to use tree-structured long short-term memory (LSTM) to model the underlying tree structures for NLP tasks, we propose a novel tree-structured convolutional gated recurrent unit (ConvGRU) model to learn the anatomical structure of the coronary artery. However, unlike tree-structured LSTM proposed for semantic relatedness as well as sentiment classification in natural language processing, our tree-structured ConvGRU model considers the local spatial correlations in the input data as the convolutions are used for input-to-state as well as state-to-state transitions, thus more suitable for image analysis. To conduct voxel-wise segmentation, a tree-structured segmentation framework is presented. It consists of a fully convolutional network (FCN) for multi-scale discriminative feature extraction and the final prediction, and a tree-structured ConvGRU layer for anatomical structure modeling. The proposed framework is extensively evaluated on four large-scale 3D CCTA dataset (the largest to the best of our knowledge), and experiments show that our method is more accurate as well as efficient, compared with other coronary artery segmentation approaches.

Reference values for mid-diastolic right ventricular volume in population referred for cardiac computed tomography: An additional diagnostic value to cardiac computed tomography

BackgroundWhile an assessment of the right ventricular (RV) size remains challenging, the entire RV is can be imaged on coronary computed tomography angiography (CCTA) studies. With prospective ECG-triggering, the RV end diastolic volume (RVEDV) cannot be measured; however, the RV mid-diastolic volume (RVMDV) can still be measured accurately from routine CCTA data sets. The objective of this study is to establish normal reference values for RVMDV. MethodsRight ventricular mid-diastolic volumes were measured in 4855 consecutive patients undergoing prospectively ECG-triggered coronary CTA. All patients with known cardiac or pulmonary disease (coronary artery disease, myocardial infarction, revascularization, heart failure, pulmonary hypertension, congenital heart disease, valvular heart disease, atrial fibrillation, implantable cardiac defibrillator implantation, cardiac transplant, or cardiac surgery) or smoking history (3313 patients) were excluded. Results1542 patients were analyzed (mean age 56.4 ± 11.1 years, mean BSA 1.96 ± 0.26 and 47% male). The mean RVMDV for men and women was 168.6 ± 37.6 mL and 117.6 ± 26.4 mL, respectively. Mean BSA-indexed RVMDV was 80.0 ± 15.3 mL/m2 and 64.1 ± 12.2 mL/m2 for men and women, respectively. The presence of hypertension and diabetes did not have an impact on these values. RVMDV and BSA-indexed RVMDV were lower in women and in older individuals. ConclusionNormal reference ranges for RVMDV were established using prospectively ECG-triggered coronary CTA studies. This data can be used to identify patients with abnormal RV volumes and potentially RV dysfunction, adding incremental diagnostic value to routine CCTA studies.