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Purpose: To develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin anomaly, stent or bypass graft., Material and Methods: In this retrospective study, a DL model for automatic CCTA reconstruction was developed with training and validation sets from 6063 and 1962 patients. The algorithm was evaluated on an independent external test set of 812 patients (357 with origin anomaly or revascularization, 455 without). The image quality of DL reconstruction and manual reconstruction (using dedicated cardiac reconstruction software provided by CT vendors) was compared using a 5-point scale. The successful reconstruction rates and post-processing time for two methods were recorded., Results: In the external test set, 812 patients (mean age, 64.0 ± 11.6, 100 with origin anomalies, 152 with stents, 105 with bypass grafts) were evaluated. The successful rates for automatic reconstruction were 100% (455/455), 97% (97/100), 100% (152/152), and 76.2% (80/105) in patients with native vessel, origin anomaly, stent, and bypass graft, respectively. The image quality scores were significantly higher for DL reconstruction than those for manual approach in all subgroups (4 vs. 3 for native vessel, 4 vs. 4 for origin anomaly, 4 vs. 3 for stent and 4 vs. 3 for bypass graft, all p < 0.001). The overall post-processing time was remarkably reduced for DL reconstruction compared to manual method (11 s vs. 465 s, p < 0.001)., Conclusions: The developed DL model enabled accurate automatic CCTA reconstruction of bypass graft, stent and origin anomaly. It significantly reduced post-processing time and improved clinical workflow., (© 2024. Italian Society of Medical Radiology.)

Objectives: This study aimed to investigate the diagnostic performance of computed tomography (CT) fractional flow reserve (CT-FFR) derived from standard images (STD) and images processed via first-generation (SnapShot Freeze, SSF1) and second-generation (SnapShot Freeze 2, SSF2) motion correction algorithms., Methods: 151 patients who underwent coronary CT angiography (CCTA) and invasive coronary angiography (ICA)/FFR within 3 months were retrospectively included. CCTA images were reconstructed using an iterative reconstruction technique and then further processed through SSF1 and SSF2 algorithms. All images were divided into three groups: STD, SSF1, and SSF2. Obstructive stenosis was defined as a diameter stenosis of ≥ 50 % in the left main artery or ≥ 70 % in other epicardial vessels. Stenosis with an FFR of ≤ 0.8 or a diameter stenosis of ≥ 90 % (as revealed via ICA) was considered ischemic. In patients with multiple lesions, the lesion with lowest CT-FFR was used for patient-level analysis., Results: The overall quality score in SSF2 group (median = 3.67) was markedly higher than that in STD (median = 3) and SSF1 (median = 3) groups (P < 0.001). The best correlation (r = 0.652, P < 0.001) and consistency (mean difference = 0.04) between the CT-FFR and FFR values were observed in the SSF2 group. At the per-lesion level, CT-FFR SSF2 outperformed CT-FFR SSF1 in diagnosing ischemic lesions (area under the curve = 0.887 vs. 0.795, P < 0.001). At the per-patient level, the SSF2 group also demonstrated the highest diagnostic performance., Conclusion: The SSF2 algorithm significantly improved CCTA image quality and enhanced its diagnostic performance for evaluating stenosis severity and CT-FFR calculations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: To compare the financial and clinical outcomes of CT myocardial perfusion imaging (CT-MPI) + coronary CT angiography (CCTA)–guided versus CCTA-guided strategy in patients suspected of chronic coronary syndrome (CCS). Materials and methods: This study retrospectively included consecutive patients suspected of CCS and referred for CT-MPI+CCTA-guided and CCTA-guided treatment. The details of medical costs within 3 months after index imaging, including downstream invasive procedures, hospitalization, and medications, were recorded. All patients were followed up for major adverse cardiac events (MACE) at a median time of 22 months. Results: A total of 1335 patients (559 in the CT-MPI+CCTA group and 776 in the CCTA group) were finally included. In the CT-MPI+CCTA group, 129 patients (23.1%) underwent ICA and 95 patients (17.0%) received revascularization. In the CCTA group, 325 patients (41.9%) underwent ICA whereas 194 patients (25.0%) received revascularization. An addition of CT-MPI in the evaluation strategy remarkably reduced the healthcare expenditure, compared with CCTA-guided strategy (USD 1441.36 vs. USD 232.91, p < 0.001). After adjustment for potential cofounders after inverse probability weighting, the CT-MPI+CCTA strategy was significantly associated with lower medical expenditure [adjusted cost ratio (95% CI) for total costs: 0.77 (0.65–0.91), p < 0.001]. In addition, there was no significant difference regarding the clinical outcome between the two groups (adjusted HR= 0.97; p = 0.878). Conclusions: CT-MPI+CCTA considerably reduced medical expenditures in patients suspected of CCS, compared to the CCTA strategy alone. Moreover, CT-MPI+CCTA led to a lower rate of invasive procedures with a similar long-term prognosis. Clinical relevance statement: CT myocardial perfusion imaging + coronary CT angiography-guided strategy reduced medical expenditure and invasive procedure rate. Key Points: • CT-MPI+CCTA strategy yielded significantly lower medical expenditure than did the CCTA strategy alone in patients with suspected CCS. • After adjustment for potential confounders, the CT-MPI+CCTA strategy was significantly associated with lower medical expenditure. • No significant difference was observed regarding the long-term clinical outcome between the two groups. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the predictive value of peri-coronary adipose tissue (PCAT) attenuation for microvascular complications in diabetic patients without significant stenosis and to develop a prediction model for early risk stratification. Methods: This study retrospectively included patients clinically identified for coronary computed tomography angiography (CCTA) and type 2 diabetes between January 2017 and December 2020. All patients were followed up for at least 1 year. The clinical data and CCTA-based imaging characteristics (including PCAT of major epicardial vessels, high-risk plaque features) were recorded. In the training cohort comprising of 579 patients, two models were developed: model 1 with the inclusion of clinical factors and model 2 incorporating clinical factors + RCAPCAT using multivariable logistic regression analysis. An internal validation cohort comprising 249 patients and an independent external validation cohort of 269 patients were used to validate the proposed models. Results: Microvascular complications occurred in 69.1% (758/1097) of the current cohort during follow-up. In the training cohort, model 2 exhibited improved predictive power over model 1 based on clinical factors (AUC = 0.820 versus 0.781, p = 0.003) with lower prediction error (Brier score = 0.146 versus 0.164) compared to model 1. Model 2 accurately categorized 78.58% of patients with diabetic microvascular complications. Similar performance of model 2 in the internal validation cohort and the external validation cohort was further confirmed. Conclusions: The model incorporating clinical factors and RCAPCAT predicts the development of microvascular complications in diabetic patients without significant coronary stenosis. Key Points: • Hypertension, HbA1c, duration of diabetes, and RCAPCATwere independent risk factors for microvascular complications. • The prediction model integrating RCAPCATexhibited improved predictive power over the model only based on clinical factors (AUC = 0.820 versus 0.781, p = 0.003) and showed lower prediction error (Brier score=0.146 versus 0.164). [ABSTRACT FROM AUTHOR]

Background: Coronary computed tomography (CT) angiography imaging is useful for the preprocedural evaluation of chronic total occlusion (CTO). However, the predictive value of CT radiomics model for successful percutaneous coronary intervention (PCI) has not been studied. We aimed to develop and validate a CT radiomics model for predicting PCI success of CTOs. Methods: In this retrospective study, a radiomics-based model for predicting PCI success was developed on the training and internal validation sets of 202 and 98 patients with CTO, collected from 1 tertiary hospital. The proposed model was validated on an external test set of 75 CTO patients enrolled from another tertiary hospital. CT radiomics features of each CTO lesion were manually labeled and extracted. Other anatomical parameters, including occlusion length, entry morphology, tortuosity, and calcification burden, were also measured. Fifteen radiomics features, 2 quantitative plaque features, and CT-derived Multicenter CTO Registry of Japan score were used to train different models. The predictive values of each model were evaluated for predicting revascularization success. Results: In the external test set, 75 patients (60 men; 65 years [58.5, 71.5]) with 83 CTO lesions were assessed. Occlusion length was shorter (13.00 mm versus 29.30 mm, P =0.007) in PCI success group whereas the presence of tortuous course was more commonly presented in PCI failure group (1.49% versus 25.00%, P =0.004). The radiomics score was significantly smaller in PCI success group (0.10 versus 0.55, P <0.001). The area under the curve of CT radiomics-based model was significantly higher than that of CT-derived Multicenter CTO Registry of Japan score for predicting PCI success (area under the curve=0.920 versus 0.752, P =0.008). The proposed radiomics model accurately identified 89.16% (74/83) CTO lesions with procedure success. Conclusions: CT radiomics-based model outperformed CT-derived Multicenter CTO Registry of Japan score for predicting PCI success. The proposed model is more accurate than the conventional anatomical parameters to identify CTO lesions with PCI success. [ABSTRACT FROM AUTHOR]