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The field of coronary plaque analysis is advancing including more quantitative analysis of coronary artery diseases such as plaque burden, high-risk plaque features, computed tomography-derived fractional flow reserve, and radiomics. Although these biomarkers have shown great promise for the diagnosis and prognosis of cardiac patients in a research setting, many of these advanced analyses are labour and time intensive and therefore hard to implement in daily clinical practice. Artificial intelligence (AI) is playing an increasing role in supporting the quantification of these new biomarkers. AI offers the opportunity to increase efficiency, reduce human error and reader variability and to increase the accuracy of diagnosis and prognosis by automating many processing and supporting clinicians in their decision-making. With the use of AI these novel analysis approaches for coronary artery disease can be made feasible for clinical practice without increasing cost and workload and potentially improve patient care.

Background Scar burden with late gadolinium enhancement (LGE) cardiac MRI (CMR) predicts arrhythmic events in patients with postinfarction in single-center studies. However, LGE analysis requires experienced human observers, is time consuming, and introduces variability. Purpose To test whether postinfarct scar with LGE CMR can be quantified fully automatically by machines and to compare the ability of LGE CMR scar analyzed by humans and machines to predict arrhythmic events. Materials and Methods This study is a retrospective analysis of the multicenter, multivendor CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry. Patients with chronic heart failure, echocardiographic left ventricular ejection fraction (LVEF) of less than 50%, and LGE CMR were recruited (from January 2015 through December 2020). In the current study, only patients with ischemic cardiomyopathy were included. Quantification of total, dense, and nondense scars was carried out by two experienced readers or a Ternaus network, trained and tested with LGE images of 515 and 246 patients, respectively. Univariable and multivariable Cox analyses were used to assess patient and cardiac characteristics associated with a major adverse cardiac event (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare model performances. Results In 761 patients (mean age, 65 years ± 11, 671 men), 83 MACEs occurred. With use of the testing group, univariable Cox-analysis found New York Heart Association class, left ventricle volume and/or function parameters (by echocardiography or CMR), guideline criterion (LVEF of ≤35% and New York Heart Association class II or III), and LGE scar analyzed by humans or the machine-learning algorithm as predictors of MACE. Machine-based dense or total scar conferred incremental value over the guideline criterion for the association with MACE (AUC: 0.68 vs 0.63, P = .02 and AUC: 0.67 vs 0.63, P = .01, respectively). Modeling with competing risks yielded for dense and total scar (AUC: 0.67 vs 0.61, P = .01 and AUC: 0.66 vs 0.61, P = .005, respectively). Conclusion In this analysis of the multicenter CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry, fully automatic machine learning-based late gadolinium enhancement analysis reliably quantifies myocardial scar mass and improves the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation. ClinicalTrials.gov registration no.: NCT03352648 Published under a CC BY 4.0 license. Supplemental material is available for this article. ispartof: Radiology vol:307 issue:3 pages:e222239- ispartof: location:United States status: published

BackgroundLatent tuberculosis infection (LTBI) has been associated with increased cardiovascular risk. We investigated the activation and pro-inflammatory profile of monocytes in individuals with LTBI and their association with coronary artery disease (CAD).MethodsIndividuals 40–70 years old in Lima, Peru, underwent QuantiFERON-TB testing to define LTBI, completed a coronary computed tomography angiography to evaluate CAD, and provided blood for monocyte profiling using flow cytometry. Cells were stimulated with lipopolysaccharide to assess interleukin-6 (IL-6) and tumor necrosis factor (TNF)–α responses.ResultsThe clinical characteristics of the LTBI (n = 28) and non-LTBI (n = 41) groups were similar. All monocyte subsets from LTBI individuals exhibited higher mean fluorescence intensity (MFI) of CX3CR1 and CD36 compared with non-LTBI individuals. LTBI individuals had an increased proportion of nonclassical monocytes expressing IL-6 (44.9 vs 26.9; P = .014), TNF-α (62.3 vs 35.1; P = .014), and TNF-α+IL-6+ (43.2 vs 36.6; P = .042). Among LTBI individuals, CAD was associated with lower CX3CR1 MFI on classical monocytes and lower CD36 MFI across all monocyte subsets. In multivariable analyses, lower CD36 MFI on total monocytes (b = −0.17; P = .002) and all subsets remained independently associated with CAD in LTBI.ConclusionsIndividuals with LTBI have distinct monocyte alterations suggestive of an exacerbated inflammatory response and tissue migration. Whether these alterations contribute to cardiovascular disease pathogenesis warrants further investigation.

BACKGROUND. Transthoracic echocardiography (TTE) is the standard of care for initial evaluation of patients with suspected cardioembolic stroke. Although TTE is useful for assessing certain sources of cardiac emboli, its diagnostic capability is limited in the detection of other sources, including left atrial thrombus and aortic plaques.OBJECTIVE. The purpose of this article was to investigate sensitivity, specificity, and predictive value of cardiac CTA (CCTA), cardiac MRI (CMRI), and TTE for recurrence in patients with suspected cardioembolic stroke.METHODS. We retrospectively included 151 patients with suspected cardioembolic stroke who underwent TTE and either CMRI (n = 75) or CCTA (n = 76) between January 2013 and May 2017. We evaluated for the presence of left atrial thrombus, left ventricular thrombus, vulnerable aortic plaque, cardiac tumors, and valvular vegetation as causes of cardioembolic stroke. The end point was stroke recurrence. Sensitivity, specificity, PPV, and NPV for recurrent stroke were calculated; the diagnostic accuracy of CMRI, CCTA, and TTE was compared between and within groups using AUC.RESULTS. Twelve and 14 recurrent strokes occurred in the CCTA and CMRI groups, respectively. Sensitivity, specificity, PPV, and NPV were 33.3%, 93.7%, 50.0%, and 88.2% for CCTA; 14.3%, 80.3%, 14.3%, and 80.3% for CMRI; 14.3%, 83.6%, 16.7%, and 80.9% for TTE in the CMRI group; and 8.3%, 93.7%, 20.0%, and 84.5% for TTE in the CCTA group. Accuracy was not different (p>.05) between CCTA (AUC = 0.63; 95% CI, 0.49-0.77), CMRI (0.53; 95% CI, 0.42-0.63), TTE in the CMRI group (0.51; 95% CI, 0.40-0.61), and TTE in the CCTA group (0.51; 95% CI, 0.42-0.59). In the CCTA group, atrial and ventricular thrombus were detected by CCTA in three patients and TTE in one patient; in the CMRI group, thrombus was detected by CMRI in one patient and TTE in two patients.CONCLUSION. CCTA, CMRI, and TTE showed comparably high specificity and NPV for cardioembolic stroke recurrence. CCTA and CMRI may be valid alternatives to TTE. CCTA may be preferred given potentially better detection of atrial and ventricular thrombus.CLINICAL IMPACT. CCTA and CMRI have similar clinical performance as TTE for predicting cardioembolic stroke recurrence. This observation may be especially important when TTE provides equivocal findings.

We report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

The purpose of this review is to highlight the most impactful, educational, and frequently downloaded articles published in the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2020. The JCCT reached new records in 2020 for the number of research submissions, published manuscripts, article downloads and social media impressions. The articles in this review were selected by the Editorial Board of the JCCT and are comprised predominately of original research publications in the following categories: Coronavirus disease 2019 (COVID-19), coronary artery disease, coronary physiology, structural heart disease, and technical advances. The Editorial Board would like to thank each of the authors, peer-reviewers and the readers of JCCT for making 2020 one of the most successful years in its history, despite the challenging circumstances of the global COVID-19 pandemic.

This review aims to summarize original articles published in the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2021, focusing on those that had the most scientific and educational impact. The JCCT continues to expand; the number of submissions, published manuscripts, cited articles, article downloads, social media presence, and impact factor continues to increase. The articles selected by the Editorial Board of the JCCT in this review focus on coronary artery disease, coronary physiology, structural heart disease, and technical advances in cardiovascular CT. In addition, we highlight key consensus documents and guidelines published in the Journal in 2021. The Journal recognizes the tremendous work done by each author and reviewer this year - thank you.

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.

Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. Materials and Methods: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. Results: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. Conclusion: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA.

Objectives: Computed tomography (CT) myocardial perfusion imaging (CT-MPI) with hyperemia induced by regadenoson was evaluated for the detection of myocardial ischemia, safety, relative radiation exposure, and patient experience compared with single-photon emission computed tomography (SPECT) imaging.Materials and Methods: Twenty-four patients (66.5 y, 29% male) who had undergone clinically indicated SPECT imaging and provided written informed consent were included in this phase II, IRB-approved, and FDA-approved clinical trial. All patients underwent coronary CT angiography and CT-MPI with hyperemia induced by the intravenous administration of regadenoson (0.4 mg/5 mL). Patient experience and findings on CT-MPI images were compared to SPECT imaging.Results: Patient experience and safety were similar between CT-MPI and SPECT procedures and no serious adverse events due to the administration of regadenoson occurred. SPECT resulted in a higher number of mild adverse events than CT-MPI. Patient radiation exposure was similar during the combined coronary computed tomography angiography and CT-MPI (4.4 [2.7] mSv) and SPECT imaging (5.6 [1.7] mSv) (P-value 0.401) procedures. Using SPECT as the reference standard, CT-MPI analysis showed a sensitivity of 58.3% (95% confidence interval [CI]: 27.7-84.8), a specificity of 100% (95% CI: 73.5-100), and an accuracy of 79.1% (95% CI: 57.9-92.87). Low apparent sensitivity occurred when the SPECT defects were small and highly suspicious for artifacts.Conclusions: This study demonstrated that CT-MPI is safe, well tolerated, and can be performed with comparable radiation exposure to SPECT. CT-MPI has the benefit of providing both complete anatomic coronary evaluation and assessment of myocardial perfusion.

During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volumes of data, allowing to tackle issues that were unfeasible before, especially with deep learning, which utilizes multilayered neural networks. Cardiac computed tomography (CT) is also experiencing a rise in examination numbers, and ML might help handle the increasing derived information. Moreover, cardiac CT presents some fields wherein ML may be pivotal, such as coronary calcium scoring, CT angiography, and perfusion. In particular, the main applications of ML involve image preprocessing and postprocessing, and the development of risk assessment models based on imaging findings. Concerning image preprocessing, ML can help improve image quality by optimizing acquisition protocols or removing artifacts that may hinder image analysis and interpretation. ML in image postprocessing might help perform automatic segmentations and shorten examination processing times, also providing tools for tissue characterization, especially concerning plaques. The development of risk assessment models from ML using data from cardiac CT could aid in the stratification of patients who undergo cardiac CT in different risk classes and better tailor their treatment to individual conditions. While ML is a powerful tool with great potential, applications in the field of cardiac CT are still expanding, and not yet routinely available in clinical practice due to the need for extensive validation. Nevertheless, ML is expected to have a big impact on cardiac CT in the near future.

Objectives This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning–based coronary computed tomography (CT) angiography (cCTA)–derived fractional flow reserve (CT-FFR). Background CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated. Methods Four hundred eighty-two vessels from 314 patients (62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to Results The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥ 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to 0 to Conclusions Machine-learning–based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).

The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars: big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging.

Coronary physiology assessments have been shown by multiple trials to add clinical value in detecting significant coronary artery disease and predicting cardiovascular outcomes. Fractional flow reserve (FFR) obtained during invasive coronary angiography (ICA) has become the new reference standard for hemodynamic significance detection. Absolute myocardial blood flow (MBF) quantification by means of dynamic positron emission tomography (dPET) has high diagnostic and prognostic values. FFR is an invasive measure and as such cannot be applied broadly, while MBF quantification is commonly performed on standard vascular territories intermixing normal flow from normal regions with abnormal flow from abnormal regions and consequently limiting its diagnostic power. The aim of this study is to provide physicians with reliable software tools for the non-invasive assessment of lesion-specific physiological significance for the entire coronary tree by combining PET-derived absolute flow data and coronary computed tomography angiography (CTA)-derived anatomy and coronary centerlines. The dynamic PET/CTA myocardial blood flow assessment with fused imagery (DEMYSTIFY) study is an observational prospective clinical study to develop algorithms and software tools to fuse coronary anatomy data obtained from CTA with dPET data to non-invasively measure absolute MBF, myocardial flow reserve, and relative flow reserve across specific coronary lesions. Patients (N = 108) will be collected from 4 institutions (Emory University Hospital, USA; Chonnam National University Hospital, South Korea; Samsung Medical Center, South Korea; Seoul National University Hospital, South Korea). These results will be compared to those obtained invasively in the catheterization laboratory and to a relatively novel non-invasive technique to estimate FFR based on CTA and computational fluid dynamics. Success of these developments should lead to the following benefits: (1) eliminate unnecessary invasive coronary angiography in patients with no significant lesions, (2) avoid stenting physiologically insignificant lesions, (3) guide percutaneous coronary interventions process to the location of significant lesions, (4) provide a flow-color-coded 3D roadmap of the entire coronary tree to guide bypass surgery, and (5) use less radiation and lower the cost from unnecessary procedures. The DEMYSTIFY study has been registered on ClinicalTrials.gov with registration number NCT04221594.

Background and aims Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Methods Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3,4,5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0–2 vs CAD-RADS 3–5). Time of analysis for both physicians and CNN were recorded. Results Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p=0.01) Conclusions Deep CNN yielded accurate automated classification of patients with CAD-RADS.

Aims Right ventricular systolic dysfunction (RVSD) is an important determinant of outcomes in heart failure (HF) cohorts. While the quantitative assessment of RV function is challenging using 2D-echocardiography, cardiac magnetic resonance (CMR) is the gold standard with its high spatial resolution and precise anatomical definition. We sought to investigate the prognostic value of CMR-derived RV systolic function in a large cohort of HF with reduced ejection fraction (HFrEF). Methods and results Study cohort comprised of patients enrolled in the CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DefibrillAtor ThErapy registry who had HFrEF and had simultaneous baseline CMR and echocardiography (n = 2449). RVSD was defined as RV ejection fraction (RVEF) Conclusion RV systolic dysfunction is an independent predictor of ACM in HFrEF, with a more pronounced prognostic value in select subgroups, likely reflecting the importance of RVSD in the early stages of HF progression.

Rationale and Objectives To evaluate the diagnostic accuracy of a prototype noncontrast, free-breathing, self-navigated 3D (SN3D) MR angiography (MRA) technique for the assessment of coronary artery anatomy in children with known or suspected coronary anomalies, using CT angiography (CTA) as the reference standard. Materials and Methods Twenty-one children (15 male, 12.3 ± 2.6 years) were prospectively enrolled between July 2014 and August 2016 in this IRB-approved, HIPAA-compliant study. Patients underwent same-day unenhanced SN3D-MRA and contrast-enhanced CTA. Two observers rated the visualization of coronary artery segments and diagnostic confidence on a 3-point scale and assessed coronary arteries for anomalous origin, as well as interarterial and intramural course. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of SN3D-MRA for the detection of coronary artery abnormalities were calculated. Interobserver agreement was assessed using Intraclass Correlation Coefficients (ICC). Results Fourteen children showed coronary artery abnormalities on CTA. The visualization of coronary segments was rated significantly higher for CTA compared to MRA (p Conclusions Noncontrast SN3D-MRA is highly accurate for the detection of coronary artery anomalies in pediatric patients while diagnostic confidence and coronary artery visualization remain superior with CTA.

Background: The aim of this study was to assess the potential of rest-stress DECT iodine quantification to discriminate between normal, ischemic, and infarcted myocardium.Methods: Patients who underwent rest-stress DECT on a 2nd generation dual-source system and cardiac magnetic resonance (CMR) were retrospectively included from a prospective study cohort. CMR was performed to identify ischemic and infarcted myocardium and categorize patients into ischemic, infarcted, and control groups. Controls were analyzed on a per-slice and per-segment basis. Regions of interest (ROIs) were placed in ischemic and infarcted areas based on CMR. Additionally, ROIs were placed in the septal area to assess normal and remote myocardium.Results: We included 42 patients: 10 ischemic, 17 infarcted, and 15 controls. Iodine concentrations showed no significant between segments in controls. Iodine concentrations for normal myocardium increased significantly from rest to stress (median 3.7 mg/mL (interquartile range 3.5-3.9) vs. 4.5 mg/mL (4.3-4.9)) (p Conclusion: DECT iodine concentration from rest-stress imaging can potentially differentiate between normal, ischemic, and infarcted myocardium.

The purpose of this work was to assess the performance of a convolutional neural network (CNN) for automatic thoracic aortic measurements in a heterogeneous population. From June 2018 to May 2019, this study retrospectively analyzed 250 chest CT scans with or without contrast enhancement and electrocardiographic gating from a heterogeneous population with or without aortic pathologic findings. Aortic diameters at nine locations and maximum aortic diameter were measured manually and with an algorithm (Artificial Intelligence Rad Companion Chest CT prototype, Siemens Healthineers) by using a CNN. A total of 233 examinations performed with 15 scanners from three vendors in 233 patients (median age, 65 years [IQR, 54–72 years]; 144 men) were analyzed: 68 (29%) without pathologic findings, 72 (31%) with aneurysm, 51 (22%) with dissection, and 42 (18%) with repair. No evidence of a difference was observed in maximum aortic diameter between manual and automatic measurements (P = .48). Overall measurements displayed a bias of −1.5 mm and a coefficient of repeatability of 8.0 mm at Bland-Altman analyses. Contrast enhancement, location, pathologic finding, and positioning inaccuracy negatively influenced reproducibility (P < .003). Sites with dissection or repair showed lower agreement than did sites without. The CNN performed well in measuring thoracic aortic diameters in a heterogeneous multivendor CT dataset. Keywords: CT, Vascular, Aorta © RSNA, 2022

Background Tuberculosis (TB) has been linked to an increased risk of atherosclerotic cardiovascular disease (ASCVD). We assessed whether latent TB infection (LTBI) is associated with subclinical coronary atherosclerosis in 2 TB-prevalent areas. Methods We analyzed cross-sectional data from studies conducted in Lima, Peru, and Kampala, Uganda. Individuals ≥40 years old were included. We excluded persons with known history of ASCVD events or active TB. Participants underwent QuantiFERON-TB (QFT) testing to define LTBI and computed tomography angiography to examine coronary atherosclerosis. A Coronary Artery Disease–Reporting Data System (CAD-RADS) score ≥3 defined obstructive CAD (plaque causing ≥50% stenosis). Results 113 and 91 persons with and without LTBI, respectively, were included. There were no significant differences between LTBI and non-LTBI participants in terms of age (median [interquartile range]; 56 [51–62] vs 55 [49–64] years; P = .829), male sex (38% vs 42%; P = .519), or 10-year ASCVD risk scores (7.1 [3.2–11.7] vs 6.1 [2.8–1.8]; P = .533). CAD prevalence (any plaque) was similar between groups (29% vs 24%; P = .421). Obstructive CAD was present in 9% of LTBI and 3% of non-LTBI individuals (P = .095). LTBI was associated with obstructive CAD after adjusting for ASCVD risk score, HIV status, and study site (adjusted OR, 4.96; 95% CI, 1.05–23.44; P = .043). Quantitative QFT TB antigen minus Nil interferon-γ responses were associated with obstructive CAD (adjusted OR, 1.2; 95% CI, 1.03–1.41; P = .022). Conclusions LTBI was independently associated with an increased likelihood of subclinical obstructive CAD. Our data indicate that LTBI is a nontraditional correlate of ASCVD risk.

Objectives To assess the feasibility of quantitative analysis of dynamic computed tomography angiography (dCTA) for the detection of endoleaks in patients who underwent endovascular repair of abdominal aortic aneurysms (EVAR). Material and methods Twenty patients scheduled for contrast-enhanced CT angiography (CTA) of the abdominal aorta post-EVAR were prospectively enrolled. All patients received a standard triphasic CTA protocol, followed by an additional dCTA. The dCTA acquisition enabled reconstruction of color-coded maps depicting blood perfusion and a dCTA dataset of the aneurysm sac. Observers assessed the dCTA and dynamic CT perfusion (dCTP) images for the detection of endoleaks, establishing diagnostic confidence based on a modified 5-point Likert scale. An index was calculated for the ratio between the endoleak and aneurysm sac using blood flow for dCTP and Hounsfield units (HU) for dCTA. The Wilcoxon test compared the endoleak index and the diagnostic confidence of the observers. Results In total, 19 patients (18 males, median age 74 years [70.5–75.7]) were included for analysis. Nine endoleaks were detected in 7 patients using triphasic CTA as the reference standard. There was complete agreement for endoleak detection between the two techniques on a per-patient basis. Both dCTA and dCTP identified an additional endoleak in one patient. The diagnostic confidence using dCTP for detection of endoleaks was not significantly superior to dCTA (5.0 [5–5] vs. 4.5 [4–5], respectively; p = 0.11); however, dCTP demonstrated superior diagnostic confidence for endoleak exclusion compared to dCTA (1.0 [1–1] vs 1.5 [1.5–1.5], respectively; p Conclusions Quantitative analysis of dCTP imaging can aid in the detection of endoleaks and demonstrates a higher endoleak detection rate than triphasic CTA, as well as a strong correlation with visual assessment of dCTA images.

To determine the association of asymptomatic diverticular disease as assessed by magnetic resonance imaging (MRI) with adipose tissue compartments, hepatic steatosis and constitutional risk factors within a cohort drawn from a Western general population. Asymptomatic subjects enrolled in a prospective case-control study underwent a 3 Tesla MRI scan, including an isotropic VIBE-Dixon sequence of the entire trunk. The presence and extent of diverticular disease were categorized according to the number of diverticula in each colonic segment in a blinded fashion. The amount of visceral, subcutaneous, and total adipose tissue (VAT, SAT, and TAT) was quantified by MRI. Additionally, the degree of hepatic steatosis, indicated as hepatic proton density fat fraction (hepatic PDFF) was determined using a multi-echo T1w sequence. Constitutional cardiometabolic risk factors were obtained and univariate and multivariate associations were calculated. A total of 371 subjects were included in the analysis (58.2 % male, 56.2 ± 9.2 years). Based on MRI, 154 participants (41.5 %) had diverticular disease with 62 cases (17 %) being advanced diverticular disease. Subjects with advanced diverticular disease had a significantly higher body mass index (BMI) (BMI: 29.9 ± 5.1 vs. 27.5 ± 4.6, p 0.001; respectively). Furthermore, all adipose tissue compartments were increased in subjects with advanced diverticular disease (e. g. VAT: 6.0 ± 2.8 vs. 4.2 ± 2.6 and SAT: 9.2 ± 3.6 vs. 7.8 ± 3.6, all p 0.001, respectively). Similarly, subjects with advanced diverticular disease had significantly higher hepatic PDFF (4.9 [2.7, 11.4] vs. 6.1 [5.5, 14.6], p = 0.002). Advanced diverticular disease is associated with an increased volume of adipose tissue compartments and BMI, which may suggest a metabolic role in disease development.· Diverticular disease is associated with constitutional risk factors such as BMI.. · Excess of adipose tissue compartments and hepatic steatosis are associated with the prevalence of diverticular disease.. · Our results suggest a shared pathological pathway of cardiometabolic alterations and the prevalence of diverticular disease.. · MRI is feasible for the assessment of adipose tissue compartments, hepatic steatosis, and diverticular disease and allows identification of patients who are at risk but in an asymptomatic disease state..· Storz C, Rospleszcz S, Askani E et al. Magnetic Resonance Imaging of Diverticular Disease and its Association with Adipose Tissue Compartments and Constitutional Risk Factors in Subjects from a Western General Population. Fortschr Röntgenstr 2021; 193: 33 - 41.ZIEL: Ziel dieser Studie war es, den Zusammenhang zwischen asymptomatischer Divertikulose und Körperfettkompartimenten, Hepatosteatose sowie konstitutionellen Risikofaktoren, erhoben mittels Magnetresonanztomografie (MRT), in einem westlichen Normalkolletkiv zu untersuchen. Gesunde, asymptomatische Teilnehmer wurden in eine prospektive Fall-Kontroll-Studie eingeschlossen und mittels 3-Tesla-Ganzkörper-MRT untersucht. Anhand einer isotropen VIBE-Dixon-Sequenz des Abdomens wurden das Ausmaß einer Divertikulose (Anzahl der Divertikel pro Kolonsegment) sowie das Volumen des viszeralen, subkutanen und totalen Fettgewebes (VAT, SAT und TAT) bestimmt. Zusätzlich wurde der Grad einer Hepatosteatose (hepatic proton density fat fraction (hepatic PDFF)) mittels einer Multiecho-T1w-Sequenz ermittelt. Konstitutionelle kardiometabolische Risikofaktoren wurden erhoben und univariate und multivariate Analysen durchgeführt. Insgesamt wurden 371 Teilnehmer in die Analyse eingeschlossen (58,2 % Männer; 56,2 ± 9,2 Jahre alt). Basierend auf den MRT-Ergebnissen hatten 154 Probanden (41,5 %) eine Divertikulose, 62 (17 %) eine fortgeschrittene Divertikulose. Probanden mit fortgeschrittener Divertikulose hatten im Vergleich zu Probanden ohne Divertikulose einen höheren BMI (BMI: 29,9 ± 5,1 vs. 27,5 ± 4,6; p 0,001). Alle Fettkompartimente waren bei Teilnehmern mit fortgeschrittener Divertikulose erhöht (bspw. VAT: 6,0 ± 2,8 vs. 4,2 ± 2,6 und SAT: 9,2 ± 3,6 vs. 7,8 ± 3,6, jeweils p 0,001). Ebenfalls wurde eine signifikant höhere Hepatosteatose bei Teilnehmern mit fortgeschrittener Divertikulose gefunden (hepatic PDFF: 4,9 (2,7, 11,4] vs. 6,1 (5,5, 14,6); p = 0,002). Die fortgeschrittene Divertikulose geht signifikant mit höheren metabolischen Fettgewebsveränderungen, insbesondere vermehrten Körperfettkompartimenten und erhöhten BMI-Werten einher. Dies lässt einen metabolischen Pathomechanismus in der Entstehung der Divertikulose vermuten.· Das Vorhandensein einer Divertikulose ist mit konstitutionellen Risikofaktoren (bspw. BMI) assoziiert.. · Vermehrte Körperfettmengen und Hepatosteatose sind mit einer Divertikulose assoziiert.. · Unsere Ergebnisse lassen einen geteilten Pathomechanismus durch kardiometabolische Veränderungen und das Vorhandensein einer Divertikulose vermuten.. · Die MRT ermöglicht die Erfassung von Körperfettkompartimenten, einer Hepatosteatose sowie einer Divertikulose und ermöglicht somit eine Identifikation von Risikofaktoren in einem asymptomatischen Krankheitsstatus..

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT.Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications.Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category.Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions.

Gated computed tomography (CT) might not adequately predict occurrence of post-implantation transcatheter aortic valve replacement (TAVR) complications in hostile aortic root as it would require a more complex integration of morphological, functional and hemodynamical parameters. We used a computational framework based on finite element analysis (FEA) to simulate patient-specific implantation. Application of biomechanical modelling using FEA to gated-CT was able to demonstrate the relation of the device with voluminous calcification, its consequent misalignment and a significant stent deformation. Use of FEA and other advanced computed predictive modelling techniques as an adjunct to CT scan could improve our understanding of TAVR, potentially predict complications and fate of the devices after implantation and inform patient-specific treatment.

We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)–derived plaque markers combined with deep machine learning–based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard. Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning–based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard. One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p

Purpose To assess the accuracy of computed tomographic (CT) angiography for diagnosis of cerebral aneurysms 5 mm or smaller, with digital subtraction angiography (DSA) as the reference standard, in a large patient cohort Materials and Methods This retrospective study was approved by the local institutional review board with a waiver of written informed consent. A total of 1366 patients who underwent cerebral CT angiography followed by DSA were included. The performance of CT angiography for depiction of aneurysms was evaluated by two readers on a per-patient and per-aneurysm basis and based on size of aneurysm, location, and status of rupture. The performance of CT angiography for diagnosis of aneurysms of different size, location, and rupture status was compared by using χ2 test. κ statistic was used to assess interreader agreement for diagnosis of aneurysms. Results Of 1366 patients, 579 patients had 711 small aneurysms at DSA. By using DSA as the reference standard, the respective sensitivity, specificity, and accuracy of CT angiography for readers 1 and 2 for detection of small aneurysms on a per-patient basis were 97.1% (562 of 579) and 97.4% (564 of 579), 98.5% (451 of 458) and 99.1% (454 of 458), and 97.7% (1013 of 1037) and 98.2% (1018 of 1037) and those on a per-aneurysm basis were 95.2% (677 of 711) and 95.4% (678 of 711), 96.6% (451 of 467) and 97.0% (454 of 468), and 95.8% (1128 of 1178) and 96.0% (1132 of 1179). The sensitivities of CT angiography were lower for detection of aneurysms smaller than 3 mm and unruptured compared with aneurysms that were 3-5 mm and ruptured (P .0167). Excellent or good interreader agreement was found for detection of intracranial aneurysms on a per-patient (κ = 0.982) and per-aneurysm (κ = 0.748) basis. Conclusion This large cohort study demonstrated that CT angiography had high accuracy for detection of small cerebral aneurysms, including those smaller than 3 mm. © RSNA, 2017 Online supplemental material is available for this article.

Objectives This study sought to evaluate the image quality and diagnostic accuracy of noncontrast quiescent-interval single-shot (QISS) magnetic resonance angiography (MRA) versus iodine-contrast computed tomography angiography (CTA) in patients with peripheral artery disease (PAD), with invasive digital subtraction angiography (DSA) as the reference standard. Background QISS is a recently introduced noncontrast MRA technique. Although the diagnostic accuracy of QISS is reportedly similar to that of contrast-enhanced MRA, its performance compared with contrast-enhanced CTA, the most frequently used noninvasive modality for evaluation of PAD, is unknown. Methods Thirty patients (66 ± 7 years of age) with PAD underwent lower extremity CTA with third-generation dual-source dual-energy CT and 1.5-T MRA using a prototype noncontrast QISS sequence. DSA was performed within 50 days. The abdominal aorta and lower extremity run-off were imaged. Eighteen arterial segments were analyzed. Subjective image quality (3-point Likert scale) and stenosis (5-point grading) were evaluated by 2 observers and compared using the Mann–Whitney U and chi-square tests, respectively. Sensitivity and specificity of MRA and CTA for >50% stenosis detection were compared using the McNemar-test. Results Of 540 segments, 15 (2.8%) and 42 (7.8%) inconclusive segments were excluded from MRA and CTA analysis, respectively (p = 0.0006). The DSA results were available for 410 of the remaining segments. Overall subjective image quality was rated similarly with QISS-MRA (2.52 [95% confidence interval: 2.46 to 2.57]) and CTA (2.49 [95% confidence interval: 2.43 to 2.55]; p = 0.5062). The sensitivity and specificity of MRA for >50% stenosis were 84.9% and 97.2%, respectively, similar to those of CTA (87.3% and 95.4%, respectively). Interobserver agreement for stenosis detection was excellent for MRA (κ > 0.81) and CTA (κ > 0.81). Conclusions Noncontrast QISS-MRA provides high diagnostic accuracy compared with DSA, while being less prone to image artifacts than CTA. QISS better visualizes heavily calcified segments with impaired flow. QISS-MRA obviates the need for contrast administration in PAD patients.

AIM To apply dual-source multidetector computed tomography (DSCT) scanning technology in conjunction with computationally assisted segmentation in order to explore and document skeletal variation that has occurred over the course of evolution. METHODS We examined 4 divergent species of elasmobranchs with high-resolution 3rd generation DSCT. The formalin prepared species examined were: Aptychotrema vincentiana, Mitsukurina owstoni, Negaprion brevirostris and Dactylobatus armatus. RESULTS All three structures of the hyoid arch (hyomandibular, ceratohyal, and basihyal) were clearly visible whereas in the two batoids, the hyomandibular was the prominent feature, the ceratohyal was not visible and the basihyal was more reduced and closer to the gill arches. The general shape of the puboischiadic bar, or pelvic girdle, illustrated a closer relationship between the two sharks and the two batoids than between the two groups. CONCLUSION In exquisite detail, DSCT imaging revealed important morphological variations in various common structures in the four elasmobranch specimens studied, providing insights into their evolutionary diversification.

The purpose of this review is to summarize the work published by the Journal of Cardiovascular Computed Tomography (JCCT) for the year 2019, highlighting original research and new guidelines.

Epicardial adipose tissue (EAT) is a metabolically activated beige adipose tissue, non-homogeneously surrounding the myocardium. Physiologically, EAT regulates toxic fatty acids, protects the coronary arteries against mechanical strain, regulates proinflammatory cytokines, stimulates the production of nitric oxide, reduces oxidative stress, and works as a thermogenic source against hypothermia. Conversely, EAT has pathologic paracrine interactions with the surrounded vessels, and might favour the onset of atrial fibrillation. In addition, initial atherosclerotic lesions can promote inflammation and trigger the EAT production of cytokines increasing vascular inflammation, which, in turn, may help the development of collateral vessels but also of self-stimulating, dysregulated inflammatory process, increasing coronary artery disease severity. Variations in EAT were also linked to metabolic syndrome. Echocardiography first estimated EAT measuring its thickness on the free wall of the right ventricle but does not allow accurate volumetric EAT estimates. Cardiac CT (CCT) and cardiac MR (CMR) allow for three-dimensional EAT estimates, the former showing higher spatial resolution and reproducibility but being limited by radiation exposure and long segmentation times, the latter being radiation-free but limited by lower spatial resolution and reproducibility, higher cost, and difficulties for obese patients. EAT radiodensity at CCT could to be related to underlying metabolic processes. The correlation between EAT and response to certain pharmacological therapies has also been investigated, showing promising results. In the future, semi-automatic or fully automatic techniques, machine/deep-learning methods, if validated, will facilitate research for various EAT measures and may find a place in CCT/CMR reporting.

In this article, the authors discuss the technical background and summarize the current body of literature regarding virtual monoenergetic (VM) images derived from dual-energy CT data, which can be reconstructed between 40 and 200 keV. Substantially improved iodine attenuation at lower kiloelectron volt levels and reduced beam-hardening artifacts at higher kiloelectron volt levels have been demonstrated from all major manufacturers of dual-energy CT units. Improved contrast attenuation with VM imaging at lower kiloelectron volt levels enables better delineation and diagnostic accuracy in the detection of various vascular or oncologic abnormalities. Low-kiloelectron-volt VM imaging may be useful for salvaging CT studies with suboptimal contrast material delivery or providing additional information on the arterial vasculature obtained from venous phase acquisitions. For patients with renal impairment, substantial reductions in the use of iodinated contrast material can be achieved by using lower-energy VM imaging. The authors recommend routine reconstruction of VM images at 50 keV when using dual-energy CT to exploit the increased contrast properties. For reduction of beam-hardening artifacts, VM imaging at 120 keV is useful for the initial assessment. (C) RSNA, 2019

Background: Maternal pregnancy complications, particularly preeclampsia and gestational diabetes mellitus, are described to increase the risk for subsequent coronary artery disease (CAD). In addition, black women are at higher risk for CAD. The objective of this study was to compare the prevalence and extent of CAD as detected by coronary computed tomographic angiography (CCTA) in black women with and without a history of prior pregnancy complications. Methods: We retrospectively evaluated patient characteristics and CCTA findings in groups of black women with a prior history of preterm delivery (n=154), preeclampsia (n=137), or gestational diabetes mellitus (n=148), and a matched control group of black women who gave birth without such complications (n=445). Univariate and multivariate analyses were performed to assess risk factors of CAD. Results: All groups with prior pregnancy complications showed higher rates of any (≥20% luminal narrowing) and obstructive (≥50% luminal narrowing) CAD (preterm delivery: 29.2% and 9.1%; preeclampsia: 29.2% and 7.3%; and gestational diabetes mellitus: 47.3% and 15.5%) compared with control women (23.8% and 5.4%). After accounting for confounding factors at multivariate analysis, gestational diabetes mellitus remained a strong risk factor of any (odds ratio, 3.26; 95% CI, 2.03–5.22; P P Conclusions: Black women with a history of pregnancy complications, particularly gestational diabetes mellitus, have a higher prevalence of CAD on CCTA while only a history of gestational diabetes mellitus was independently associated with any and obstructive CAD on CCTA.

This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.

Background Obesity is one of the biggest drivers of preventable chronic diseases and healthcare costs in Worldwide. Different prevention activities are suggested. By monitoring daily energy expenditure (EE) could be possible make personalized diets and programming physical activity. In this, physical inactivity is one of the most important public health problems. Some studies refer the effort of the international community in promoting physical activities. Physical activity can be promoted only by increasing citizens' empowerment on taking care of their health, and it passes from the improving of individual information. Technology can offer solutions and metrics for monitoring and measuring daily activity by interacting with individuals, sharing information and feedbacks. Objective In this study we review indicators of total energy expenditure and weaknesses of available devices in assessing these parameters. Methods Literature review and technology testing EuNetHta core model. Results For the clinical aspects, it is fundamental to take into account all the factor that can influence the personal energy expenditure as: heart rate, blood pressure and thermoregulation (influenced by the body temperature). Discussion In this study we focused the attention on the importance of tools to encourage the physical activity. We made an analysis of the factor that can influence the right analysis of energy expenditure and at the same time the energy regime. A punctual monitoring of the exercise regime could be helpful in Telemedicine application as Telemonitorig. More study are needed to value the impact of physical activity tracker in Telemonitorig protocols. Conclusion On the assessment of the energy expenditure, critical issues are related to the physiological data acquisition. Sensors connected with mobile devices could be important tools for disease prevention and interventions affecting health behaviors. New devices applications are potential useful for telemedicine assistance, but security of data and the related communication protocol limits should be taking into account.

Rationale and Objectives To evaluate the diagnostic accuracy of semiautomated global quantification of left ventricular myocardial perfusion derived from stress dynamic computed tomography myocardial perfusion imaging (CTMPI) for detection of territorial perfusion deficits (PD). Materials and Methods Dynamic CTMPI datasets of 71 patients were analyzed using semiautomated volume-based software to calculate global myocardial blood flow (MBF), myocardial blood volume, and volume transfer constant. Optimal cutoff values to assess the diagnostic accuracy of these parameters for detection of one- to three-vessel territories with PD in comparison to visual analysis were calculated. Results Nonsignificant differences ( P = 0.694) were found for average global MBF in patients without PD and single-territorial PD. Significant differences were found for mean global MBF in patients with PD in two ( P P Conclusions Semiautomated global quantification of left ventricular MBF during stress dynamic CTMPI shows high diagnostic accuracy for detection of ≥2 vessel territories with PD, facilitating identification of patients with multi-territorial myocardial PD.

To evaluate the effect of automated tube voltage selection (ATVS) on radiation dose at computed tomography (CT) worldwide encompassing all body regions and types of CT examinations.No patient information was accessed; therefore, institutional review board approval was not sought. Data from 86 centers across the world were analyzed. All CT interactions were automatically collected and transmitted to the CT vendor during two 6-week periods immediately before and 2 weeks after implementation of ATVS. A total of 164 323 unique CT studies were analyzed. Studies were categorized by body region and type of examination. Tube voltage and volume CT dose index (CTDIvol) were compared between examinations performed with ATVS and those performed before ATVS implementation. Descriptive statistical methods and multilevel linear regression models were used for analysis.Across all types of CT examinations and body regions, CTDIvol was 14.7% lower in examinations performed with ATVS (n = 30 313) than in those performed before ATVS implementation (n = 79 275). Relative reductions in mean CTDIvol were most notable for temporal bone CT (-56.1%), peripheral runoff CT angiography (-48.6%), CT of the paranasal sinus (-39.6%), cerebral or carotid CT angiography (-36.4%), coronary CT angiography (-25.1%), and head CT (-23.9%). An increase in mean CTDIvol was observed for renal stone protocols (26.2%) and thoracic or lumbar spine examinations (6.6%). In the multilevel model with fixed effects ATVS and examination type, and the interaction of these variables and the random effect country, a significant influence on CTDIvol for all fixed efects was revealed (ATVS, P = .0031; examination type, P.0001; interaction term, P.0001).ATVS significantly reduces radiation dose across most, but not all, body regions and types of CT examinations.