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Objectives: To validate a method for performing myocardial segmentation based on coronary anatomy using coronary CT angiography (CCTA).Methods: Coronary artery-based myocardial segmentation (CAMS) was developed for use with CCTA. To validate and compare this method with the conventional American Heart Association (AHA) classification, a single coronary occlusion model was prepared and validated using six pigs. The unstained occluded coronary territories of the specimens and corresponding arterial territories from CAMS and AHA segmentations were compared using slice-by-slice matching and 100 virtual myocardial columns.Results: CAMS more precisely predicted ischaemic area than the AHA method, as indicated by 95% versus 76% (p < 0.001) of the percentage of matched columns (defined as percentage of matched columns of segmentation method divided by number of unstained columns in the specimen). According to the subgroup analyses, CAMS demonstrated a higher percentage of matched columns than the AHA method in the left anterior descending artery (100% vs. 77%; p < 0.001) and mid- (99% vs. 83%; p = 0.046) and apical-level territories of the left ventricle (90% vs. 52%; p = 0.011).Conclusions: CAMS is a feasible method for identifying the corresponding myocardial territories of the coronary arteries using CCTA.Key Points: • CAMS is a feasible method for identifying corresponding coronary territory using CTA • CAMS is more accurate in predicting coronary territory than the AHA method • The AHA method may underestimate the ischaemic territory of LAD stenosis. [ABSTRACT FROM AUTHOR]

Objectives: To validate 4D flow MRI in a flow phantom using a flowmeter and computational fluid dynamics (CFD) as reference.Methods: Validation of 4D flow MRI was performed using flow phantoms with 75 % and 90 % stenosis. The effect of spatial resolution on flow rate, peak velocity and flow patterns was investigated in coronal and axial scans. The accuracy of flow rate with 4D flow MRI was evaluated using a flowmeter as reference, and the peak velocity and flow patterns obtained were compared with CFD analysis results.Results: 4D flow MRI accurately measured the flow rate in proximal and distal regions of the stenosis (percent error ≤3.6 % in axial scanning with 1.6-mm resolution). The peak velocity of 4D flow MRI was underestimated by more than 22.8 %, especially from the second half of the stenosis. With 1-mm isotropic resolution, the maximum thickness of the recirculating flow region was estimated within a 1-mm difference, but the turbulent velocity fluctuations mostly disappeared in the post-stenotic region.Conclusion: 4D flow MRI accurately measures the flow rates in the proximal and distal regions of a stenosis in axial scan but has limitations in its estimation of peak velocity and turbulent characteristics.Key Points: • 4D flow MRI accurately measures the flow rate in axial scan. • The peak velocity was underestimated by 4D flow MRI. •4D flow MRI demonstrates the principal pattern of post-stenotic flow. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether plaque characteristics as assessed by coronary computed tomography angiography (CCTA) were associated with the presence of a thin-cap fibroatheroma (TCFA)-a precursor of plaque rupture-defined by optical coherence tomography (OCT) in a section-to-section-level comparison.Methods: From 28 symptomatic patients, 31 coronary lesions were evaluated on 727 cross-sections co-registered by both CCTA and OCT. CCTA plaque characteristics included low attenuation plaque (LAP, <30 HU), napkin ring sign (NRS), positive remodelling (PR, remodelling index ≥1.10), and spotty calcification and plaque area and plaque burden. By OCT, presence of TCFA, lumen area and arc of lipid were determined.Results: OCT revealed a TCFA in 69 (9.4%) sections from 19 (61.2 %) lesions. In per-section analysis, OCT-TCFA showed higher frequency of CCTA-detected LAP (58.0% vs. 18.5%), NRS (31.9% vs. 8.8%) and PR (68.1% vs. 48.0%) and greater plaque burden (70.6% vs. 61.9%) as compared to sections without OCT-TCFA (all p < 0.05). In multivariable analysis, LAP (odds ratio [OR] 4.05, p < 0.001) and NRS (OR 2.47, p = 0.005) were associated with OCT-TCFA. CCTA-measured lumen area correlated well with OCT-measured lumen area (R = 0.859, limits of agreement -0.5 ± 3.7 mm2).Conclusions: LAP and NRS in CCTA were associated with the presence of OCT-defined TCFA in a section-to-section comparison.Key Points: • CT-defined LAP and NRS were associated with OCT-defined TCFA • OCT-TCFA showed higher frequency of LAP, NRS, PR and greater plaque burden • Non-calcified plaque area was correlated with OCT-measured lipid arc. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to evaluate the role of preoperative cardiac computed tomography (CT) for adults with congenital cardiac septal defect (CSD). Methods: Sixty-five consecutive patients who underwent preoperative CT and surgery for CSD were included. The diagnostic accuracy of CT and the concordance rate of the subtype classification of CSD were evaluated using surgical findings as the reference standard. Sixty-five patients without CSD who underwent cardiac valve surgery were used as a control group. An incremental value of CT over echocardiography was described retrospectively. Results: Sensitivity and specificity of CT for diagnosis of CSD were 95 % and 100 %, respectively. The concordance rate of subtype classification was 91 % in CT and 92 % in echocardiography. The maximum size of the defect measured by CT correlated well with surgical measurement ( r = 0.82), and the limit of agreement was -0.9 ± 7.42 mm. In comparison with echocardiography, CT was able to detect combined abnormalities in three cases, and exclusively provided correct subtype classification or clarified suspected abnormal findings found on echocardiography in seven cases. Conclusions: Cardiac CT can accurately demonstrates CSD in preoperative adult patients. CT may have an incremental role in preoperative planning, particularly in those with more complex anatomy. Key Points: • Cardiac CT can demonstrate cardiac septal defect accurately in preoperative planning. • Cardiac CT can demonstrate combined abnormalities of cardiac septal defect. • Cardiac CT may have an incremental role over echocardiography in complex anatomy. [ABSTRACT FROM AUTHOR]

Objective: Patients who undergo transcatheter aortic valve replacement (TAVR) are at risk for new-onset arrhythmia (NOA) that may require permanent pacemaker (PPM) implantation, resulting in decreased cardiac function. We aimed to investigate the factors that are associated with NOA after TAVR and to compare pre- and post-TAVR cardiac functions between patients with and without NOA using CT-derived strain analyses. Methods: We included consecutive patients who underwent pre- and post-TAVR cardiac CT scans six months after TAVR. New-onset left bundle branch block, atrioventricular block, and atrial fibrillation/flutter lasting over 30 days after the procedure and/or the need for PPM diagnosed within 1 year after TAVR were regarded as NOA. Implant depth and left heart function and strains were analyzed using multi-phase CT images and compared between patients with and without NOA. Results: Of 211 patients (41.7% men; median 81 years), 52 (24.6%) presented with NOA after TAVR, and 24 (11.4%) implanted PPM. Implant depth was significantly deeper in the NOA group than in the non-NOA group (− 6.7 ± 2.4 vs. − 5.6 ± 2.6 mm; p = 0.009). Left ventricular global longitudinal strain (LV GLS) and left atrial (LA) reservoir strain were significantly improved only in the non-NOA group (LV GLS, − 15.5 ± 4.0 to − 17.3 ± 2.9%; p < 0.001; LA reservoir strain, 22.3 ± 8.9 to 26.5 ± 7.6%; p < 0.001). The mean percent change of the LV GLS and LA reservoir strains was evident in the non-NOA group (p = 0.019 and p = 0.035, respectively). Conclusions: A quarter of patients presented with NOA after TAVR. Deep implant depth on post-TAVR CT scans was associated with NOA. Patients with NOA after TAVR had impaired LV reserve remodeling assessed by CT-derived strains. Clinical relevance statement: New-onset arrhythmia (NOA) following transcatheter aortic valve replacement (TAVR) impairs cardiac reverse remodeling. CT-derived strain analysis reveals that patients with NOA do not show improvement in left heart function and strains, highlighting the importance of managing NOA for optimal outcomes. Key Points: • New-onset arrhythmia following transcatheter aortic valve replacement (TAVR) is a concern that interferes with cardiac reverse remodeling. • Comparison of pre-and post-TAVR CT-derived left heart strain provides insight into the impaired cardiac reverse remodeling in patients with new-onset arrhythmia following TAVR. • The expected reverse remodeling was not observed in patients with new-onset arrhythmia following TAVR, given that CT-derived left heart function and strains did not improve. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate commercial deep learning–based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). Methods: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. Results: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, −244.0, 283.9] vs. 45.26 [−248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). Conclusions: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. Key Points: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mmdemonstrated excellent agreement with CSCTmanual(all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mmthan with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mmthan with LDCT2.5-mm. [ABSTRACT FROM AUTHOR]

Objectives: To validate an artificial intelligence (AI)–based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)–gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. Methods: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. Results: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998–0.999 and 0.989, 95% CI 0.987–0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918–0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748–0.924) on LDCT. Conclusions: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. Key Points: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score–based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT. [ABSTRACT FROM AUTHOR]

Objectives: Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning–based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). Methods: We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. Results: CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. Conclusions: The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes. Key Points: • A deep learning–based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated. • Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset. • All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs. [ABSTRACT FROM AUTHOR]

Objectives: The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2. Methods: Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression. Results: Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms. Conclusions: COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure. Key Points: • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s00330-020-07552-8 [ABSTRACT FROM AUTHOR]