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Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation., Materials and Methods: The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges., Results: Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows., Conclusion: To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized., Critical Relevance Statement: Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics., Key Points: Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community., (© 2024. The Author(s).)

To compare the diagnostic value of ultrahigh-resolution CT-angiography (UHR-CTA) compared with high-pitch spiral CTA (HPS-CTA) using a first-generation, dual-source photon-counting CT (PCD-CT) scanner for preprocedural planning of transcatheter aortic valve replacement (TAVR). Clinically referred patients with severe aortic valve stenosis underwent both, retrospective ECG-gated cardiac UHR-CTA (collimation: 120 × 0.2 mm) and prospective ECG-triggered aortoiliac HPS-CTA (collimation: 144 × 0.4 mm, full spectral capabilities) for TAVR planning from August 2022 to March 2023. Radiation dose was extracted from the CT reports, and the effective dose was calculated. Two radiologists analyzed UHR-CTA and HPS-CTA datasets, assessing the image quality of the aortic annulus, with regard to the lumen visibility and margin delineation using a 4-point visual-grading scale (ranges: 4 = "excellent" to 1 = "poor"). Aortic annulus area (AAA) measurements were taken for valve prosthesis sizing, with retrospective UHR-CTA serving as reference standard. A total of 64 patients were included (mean age, 81 years ± 7 SD; 28 women) in this retrospective study. HPS-CTA showed a lower radiation dose, 4.1 mSv vs. 12.6 mSv (p < 0.001). UHR-CTA demonstrated higher image quality to HPS-CTA (median score, 4 [IQR, 3-4] vs. 3 [IQR, 2-3]; p < 0.001). Quantitative assessments of AAA from both CTA datasets were strongly positively correlated (mean 477.4 ± 91.1 mm 2 on UHR-CTA and mean 476.5 ± 90.4 mm 2 on HPS-CTA, Pearson r 2  = 0.857, p < 0.001) with a mean error of 22.3 ± 24.6 mm 2 and resulted in identical valve prosthesis sizing in the majority of patients (91%). Patients with lower image quality on HPS-CTA (score value 1 or 2, n = 28) were more likely to receive different sizing recommendations (82%). Both UHR-CTA and HPS-CTA acquisitions using photon-counting CT technology provided reliable aortic annular assessments for TAVR planning. While UHR-CTA offers superior image quality, HPS-CTA is associated with lower radiation exposure. However, severely impaired image quality on HPS-CTA may impact on prosthesis sizing, suggesting that immediate post-scan image evaluations may require complementary UHR-CTA scanning., (© 2024. The Author(s).)

Purpose: Artificial intelligence in computer vision has been increasingly adapted in clinical application since the implementation of neural networks, potentially providing incremental information beyond the mere detection of pathology. As its algorithmic approach propagates input variation, neural networks could be used to identify and evaluate relevant image features. In this study, we introduce a basic dataset structure and demonstrate a pertaining use case., Methods: A multidimensional classification of ankle x-rays (n = 1493) rating a variety of features including fracture certainty was used to confirm its usability for separating input variations. We trained a customized neural network on the task of fracture detection using a state-of-the-art preprocessing and training protocol. By grouping the radiographs into subsets according to their image features, the influence of selected features on model performance was evaluated via selective training., Results: The models trained on our dataset outperformed most comparable models of current literature with an ROC AUC of 0.943. Excluding ankle x-rays with signs of surgery improved fracture classification performance (AUC 0.955), while limiting the training set to only healthy ankles with and without fracture had no consistent effect., Conclusion: Using multiclass datasets and comparing model performance, we were able to demonstrate signs of surgery as a confounding factor, which, following elimination, improved our model. Also eliminating pathologies other than fracture in contrast had no effect on model performance, suggesting a beneficial influence of feature variability for robust model training. Thus, multiclass datasets allow for evaluation of distinct image features, deepening our understanding of pathology imaging., (© 2023. The Author(s).)

Purpose: Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans., Methods: The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD)., Results: Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] - 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] - 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] - 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] - 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen., Conclusion: In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications., (© 2022. The Author(s).)

Background: The German National Cohort (NAKO) is an interdisciplinary health study aimed at elucidating causes for common chronic diseases and detecting their preclinical stages. This article provides an overview of design, methods, participation in the examinations, and their quality assurance based on the midterm baseline dataset (MBD) of the recruitment., Methods: More than 200,000 women and men aged 20-69 years derived from random samples of the German general population were recruited in 18 study centers (2014-2019). The data collection comprised physical examinations, standardized interviews and questionnaires, and the collection of biomedical samples for all participants (level 1). At least 20% of all participants received additional in-depth examinations (level 2), and 30,000 received whole-body magnet resonance imaging (MRI). Additional information will be collected through secondary data sources such as medical registries, health insurances, and pension funds. This overview is based on the MBD, which included 101,839 participants, of whom 11,371 received an MRI., Results: The mean response proportion was 18%. The participation in the examinations was high with most of the modules performed by over 95%. Among MRI participants, 96% completed all 12 MRI sequences. More than 90% of the participants agreed to the use of complementary secondary and registry data., Discussion: Individuals selected for the NAKO were willing to participate in all examinations despite the time-consuming program. The NAKO provides a central resource for population-based epidemiologic research and will contribute to developing innovative strategies for prevention, screening and prediction of chronic diseases.

High levels of adiposity in the population have a major impact on various diseases, but previous epidemiologic studies have largely been restricted to simple anthropometric measures such as the body mass index (BMI), an imperfect predictor of disease risk. There is a critical need for the use of improved measures of relative weight and body composition in large-scale, population-based research.The current article presents initial descriptive results of body composition and fat distribution based on the midterm baseline dataset of the German National Cohort, which included 101,817 participants who were examined in 18 study centers in Germany between March 2014 and March 2017. The anthropometric measures encompassed body weight, height, waist and hip circumference, bioelectrical impedance analysis (BIA), sonography of abdominal adipose tissue, 3D-body scanning, and magnetic resonance imaging.BMI analyses showed that 46.2% of men and 29.7% of women were overweight and 23.5% of men and 21.2% of women were obese. On average, women in almost all age groups demonstrated more subcutaneous adipose tissue layer thickness than men. The mean values of visceral adipose tissue layer thickness, on the other hand, were higher among men than among women in all age groups and increased continuously across age groups in both sexes.The comprehensive assessment of body composition and fat distribution provides novel future opportunities for detailed epidemiologic analyses of overweight and adiposity in relation to the development of chronic diseases.

Background: Noise annoyance is associated with adverse health-related conditions and reduced wellbeing. Thereby, subjective noise annoyance depends on the objective noise exposure and is modified by personal and regional factors., Objective: How many participants of the German National Cohort Study (GNC; NAKO Gesundheitsstudie) were annoyed by transportation noise during nighttime and what factors were associated with noise annoyance?, Materials and Methods: This cross-sectional analysis included 86,080 participants from 18 study centers, examined from 2014 to 2017. We used multinomial logistic regression to investigate associations of personal and regional factors to noise annoyance (slightly/moderately or strongly/extremely annoyed vs. not annoyed) mutually adjusting for all factors in the model., Results: Two thirds of participants were not annoyed by transportation noise during nighttime and one in ten reported strong/extreme annoyance with highest percentages for the study centers Berlin-Mitte and Leipzig. The strongest associations were seen for factors related to the individual housing situation like the bedroom being positioned towards a major road (OR of being slightly/moderately annoyed: 4.26 [95% CI: 4.01;4.52]; OR of being strongly/extremely annoyed: 13.36 [95% CI: 12.47;14.32]) compared to a garden/inner courtyard. Participants aged 40-60 years and those in low- and medium-income groups reported greater noise annoyance compared to younger or older ones and those in the high-income group., Conclusion: In this study from Germany, transportation noise annoyance during nighttime varied by personal and regional factors.

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

Purpose: In medical imaging, the digital post-processing and analysis of acquired images has become an important research field. Topics include various applications of image processing and machine learning aiming to assist radiologists in their diagnostic work. A crucial step in successfully implementing such systems is finding appropriate mathematical descriptions to reflect characteristics of acquired images. Which features are the most meaningful ones strongly depends on the underlying scientific/diagnostic question and the image itself. This makes researching, implementing and testing features time-consuming and cost-intensive. In our work, we aim to address this issue by creating ImFEATbox, a publicly available toolbox to extract and analyze image features for a wide range of applications., Methods: To reduce the amount of time spent for choosing the right features, we provide an assortment of feature extraction algorithms which are suitable for a broad variety of medical image processing problems. The toolbox includes both global and local features as well as feature descriptors. While being primarily developed in MATLAB, the majority of our algorithms is also available in Python to enable access to a wider range of researchers., Results: We tested the applicability of ImFEATbox on an FDG-PET/CT data set of 12 patients diagnosed with lung cancer and an MRI data set of 50 patients with prostate lesions. Employing the implemented algorithms in an exemplary manner, we are able to demonstrate its potential for different scientific problems, e.g., show differences between features, indicate redundancies in extracted feature sets by means of a correlation analysis and training a SVM to distinguish between high-risk and low-risk prostate lesions., Conclusion: ImFEATbox provides a variety of feature extraction algorithms suitable for a large number of post-processing and analysis applications in medical imaging. The toolbox is publicly available and can thus be beneficial to a wide range of researchers working on medical image analysis.

To compare a semi-automatic software tool for the measurement of aortic annulus dimensions with manual measurements by expert readers and to analyze whether and to what extent interchangeability exists between semi-automatic and manual measurements. We retrospectively included 374 consecutive patients with high-grade aortic stenosis who had undergone CT-angiography of the heart prior to trans-catheter aortic valve replacement (TAVR). In independent analyses, two expert readers manually measured aortic annulus dimensions (long axis, short axis, circumference, area) as well as the distance of the coronary ostia from the annulus plane. A third independent reader performed annulus evaluation using a software tool for semi-automatic detection and measurement of the annulus plane. Intraclass correlation coefficients (ICC) and Bland-Altman analysis was used to compare both manual measurements as well as manual and semi-automatic measurements of annulus parameters. Using the respective measurements we simulated size selection for a Sapien XT transcatheter heart valve (THV). Interchangeability of methods was addressed by calculation of the estimated individual equivalence index γ. There was excellent agreement between both expert observers in manual measurements of the annulus with ICC's in the range 0.89-0.94 for all anatomic parameters. Similar high agreements were observed between semi-automatic and manual measurements, with ICC's in the range of 0.89-0.95. THV size recommendation based on manual versus semiautomatic measurements agreed in 80.7% of cases while agreement between both expert readers concerning THV size recommendation was 80.6%. Semi-automatic measurements of anatomic parameters of the aortic root show high agreement and interchangeability with manual measurements in CT-angiography prior to TAVR.

Combined PET/MRI is a novel imaging method integrating the advances of functional and morphological MR imaging with PET applications that include assessment of myocardial viability, perfusion, metabolism of inflammatory tissue and tumors, as well as amyloid deposition imaging. As such, PET/MRI is a promising tool to detect and characterize ischemic and non-ischemic cardiomyopathies. To date, the greatest benefit may be expected for diagnostic evaluation of systemic diseases and cardiac masses that remain unclear in cardiac MRI, as well as for clinical and scientific studies in the setting of ischemic cardiomyopathies. Diagnosis and therapeutic monitoring of cardiac sarcoidosis has the potential of a possible 'killer-application' for combined cardiac PET/MRI. In this article, we review the current evidence and discuss current and potential future applications of cardiac PET/MRI.

Objectives: Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture., Materials and Methods: T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis., Results: On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively., Conclusion: Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

Background: Cohort studies are a longitudinal observational study type. They are firmly established within epidemiology to assess the course of diseases and risk factors. Yet, standards to describe and evaluate quality characteristics of cohort studies need further development., Objective: Within the TMF ("Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V.") project "Quality management standards in cohort studies", a catalogue of requirements was compiled and evaluated, focusing on the preparation and conduct of epidemiologic cohort studies., Materials and Methods: The catalogue of requirements was established based on a consensus process between representatives of seven German epidemiologic cohort studies. For this purpose, a set of expert meetings (telephone, face-to-face, web-based) was conducted and the importance of each element of the catalogue was assessed as well as its implementation., Results: A catalogue of requirements with 138 requirements was consented. It is structured into ten sections: 1. Study documentation; 2. Selection of instruments; 3. Study implementation, 4. Organizational structure; 5. Qualification and certification; 6. Participant recruitment; 7. Preparation, conduct and follow-up processing of examinations; 8. Study logistics and maintenance, 9. Data capture and data management; 10. Reporting and monitoring. In total, 41 elements were categorized as being essential, 91 as important, and 6 as less important., Conclusion: The catalogue of requirements provides a guideline to improve the preparation and operation of cohort studies. The evaluation of the importance and degree of implementation of requirements depended on the study design. With adaptations, the catalogue might be transferable to other study types.

Objectives: To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE)., Materials and Methods: The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m 2 (30.02 ± 6.63 kg/m 2 ) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes., Results: Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images., Conclusion: Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.

The purpose of the study is feasibility of dynamic CT perfusion imaging to detect and differentiate ischemic and infarcted myocardium in a large porcine model. 12 Country pigs completed either implantation of a 75 % luminal coronary stenosis in the left anterior descending coronary artery simulating ischemia or balloon-occlusion inducing infarction. Dynamic CT-perfusion imaging (100 kV, 300 mAs), fluorescent microspheres, and histopathology were performed in all models. CT based myocardial blood flow (MBFCT), blood volume (MBVCT) and transit constant (Ktrans), as well as microsphere's based myocardial blood flow (MBFMic) were derived for each myocardial segment. According to histopathology or microsphere measurements, 20 myocardial segments were classified as infarcted and 23 were ischemic (12 and 14 %, respectively). Across all perfusion states, MBFCT strongly predicted MBFMic (β 0.88 ± 0.12, p < 0.0001). MBFCT, MBVCT, and Ktrans were significantly lower in ischemic/infarcted when compared to reference myocardium (all p < 0.01). Relative differences of all CT parameters between affected and non-affected myocardium were higher for infarcted when compared to ischemic segments under rest (48.4 vs. 22.6 % and 46.1 vs. 22.9 % for MBFCT, MBVCT, respectively). Under stress, MBFCT was significantly lower in infarcted than in ischemic myocardium (67.8 ± 26 vs. 88.2 ± 22 ml/100 ml/min, p = 0.002). In a large animal model, CT-derived parameters of myocardial perfusion may enable detection and differentiation of ischemic and infarcted myocardium.

To evaluate the effect of age, gender and cardiovascular risk factors on vessel wall inflammation and the calcified plaque burden in different vascular beds as assessed by PET/CT. 315 patients (mean age: 57.8 years, 123 male and 192 female) who underwent whole body 18F-FDG PET/CT examinations were included in the study. Blood pool-corrected standardised uptake value (TBR) and the calcified plaque score (CPS, grade 0-4) were determined in the thoracic and abdominal aorta, both common carotid and both iliac arteries. The following cardiovascular risk factors were documented: Age ≥65 years (n = 114), male gender (n = 123), diabetes (n = 15), hyperlipidemia (n = 62), hypertension (n = 76), body mass index (BMI) ≥ 30 (n = 38), current smoker (n = 32). Effects of risk factors on TBR and CPS in different arterial beds were assessed using multivariate regression analysis. In the thoracic aorta TBR was independently associated with age ≥65 years and male gender, CPS was independently associated with age ≥65 years, male gender, hypertension and diabetes. In the abdominal aorta, TBR was independently associated with age ≥65 years and male gender, CPS with age ≥65 years, diabetes and smoking. Independent associations in the carotid arteries were found for age ≥65 years, male gender and BMI ≥ 30 in TBR and for age ≥65 and diabetes in CPS. In the iliac arteries, TBR was independently associated with age ≥65 and CPS with age ≥65, male gender, hypertension, diabetes and smoking. Findings of this PET/CT study demonstrate that the impact of cardiovascular risk factors on vessel wall inflammation and calcified plaque burden differs across vascular territories. Overall, CPS was more closely associated with cardiovascular risk factors compared to TBR.

While high-sensitivity troponin-T (hsTnT) and C-reactive protein (hsCRP) are associated with structural heart disease, we thought to determine whether biomarkers can predict which heart is healthy based on multimodality imaging. Patients from the emergency department with acute chest pain suggestive of acute coronary syndrome undergoing contrast enhanced cardiac CT and stress single photon emission computed tomography (SPECT) myocardial perfusion imaging were included. HsTnT and hsCRP were assessed at time of CT. Imaging data were assessed for coronary atherosclerosis, left ventricular hypertrophy/dysfunction and myocardial perfusion abnormalities. Patients were stratified into those with or without any cardiac findings, who were considered as cardiac healthy. For biomarkers, low cut-off corresponding to good specificity and high cut-off corresponding to good sensitivity for cardiac health were derived. Among 117 patients (52 years, 55 % male), 42 (36 %) were cardiac healthy based on cardiac CT and SPECT imaging. These patients had significantly lower hsTnT and hsCRP levels as compared to those with functional or structural abnormalities (3.58 vs. 5.63 ng/L, p = 0.002; 0.82 vs. 1.93 mg/L, p = 0.0005; respectively). Patients with both low hsTnT (<3.00 ng/L) and hsCRP (<0.45 mg/L) had a probability of 85 % for being cardiac healthy. In contrast, patients with high hsTnT (>7.00 ng/L) and hsCRP (>2.00 mg/L) had 8 % probability for being cardiac healthy. Discriminative capacity of a dual-biomarker strategy was significantly improved as compared to hsTnT or hsCRP alone or to Framingham Risk score (AUC: 0.781 vs. 0.691; vs. 0.678; vs. 0.649; all p ≤ 0.02, respectively). A dual-biomarker strategy of hsTnT and hsCRP is highly discriminative for patients with normal cardiac structure and function and provides incremental value beyond the Framingham risk score.

CT-based myocardial perfusion imaging (CTP) has been shown to accurately detect myocardial perfusion defects when compared to SPECT. When performing single-phase first-pass stress CTP, timing is of major importance. The aim of this study was to provide guidance for optimal timing of single-phase first-pass stress CTP acquisitions. 16 patients (12 male, age, 69 ± 8 years) with known or suspected coronary artery disease underwent invasive coronary angiography with fractional flow reserve (FFR) measurements using a pressure wire as well as a time-resolved CTP protocol under adenosine stress, performed on a dual-Source CT scanner over a period of 30 s. From the CTP data, time-attenuation curves have been determined both in known ischemic myocardium with a corresponding coronary artery stenosis as proven by a FFR below 0.75 in invasive coronary angiography, as well as in non-ischemic reference myocardium during pharmacological stress. Furthermore, contrast enhancement in the ascending aorta was determined. The time point for an optimal contrast (i.e., difference in Hounsfield Units, HU) between ischemic and normal myocardium was determined. Under pharmacological stress using adenosine, a maximum mean HU difference between ischemic and non-ischemic myocardium (17.7-22.5 HU) was observed 24-32 s after injection of contrast medium. The maximal attenuation difference between normal and ischemic myocardium ranged from 15 to 77 HU in the analyzed patient cohort. When applying a bolus-tracking technique with an automatic contrast detection in the proximal ascending aorta, the optimal time frame for stress CTP was between 8 and 16 s after contrast enhancement in the aorta exceeds 100 HU, or between 7 and 15 s using a threshold of 150 HU. For first-pass CT myocardial perfusion imaging there is a time frame of approximately 8 s for optimal differentiation of ischemic and non-ischemic myocardium, which will be helpful to optimize single-phase CTP scans.

Introduction: Little information exists regarding how accurately emergency physicians (EPs) predict the probability of acute coronary syndrome (ACS). Our objective was to determine if EPs can accurately predict ACS in a prospectively identified cohort of emergency department (ED) patients who met enrolment criteria for a study of coronary computed tomographic angiography (CCTA) and were admitted for a "rule out ACS" protocol., Methods: A prospective observational pilot study in an academic medical centre was carried out. EPs caring for patients with chest pain provided whole-number estimates of the probability of ACS after clinical review. This substudy was part of the now published Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography (ROMICAT) study, a study of CCTA and admission of patients for a rule out ACS protocol after a nondiagnostic evaluation. Predictions were grouped into probability groups based on the validated Goldman criteria. ACS was determined by an adjudication committee using American Heart Association/American College of Cardiology/European Society of Cardiology guidelines., Results: A total of 334 predictions were obtained for a study population with a mean age of 54 (SD 12) years, 63% of whom were male. There were 35 ACS events. EPs predicted ACS better than by chance, and increasingly higher estimates were associated with a higher incidence of ACS (p = 0.0004). The percentage of patients with ACS was 0%, 6%, 7%, and 17%, respectively, for very low, low, intermediate, and high probability groups. EPs' estimates had a sensitivity of 63% using a > 20% probability of ACS to define a positive test. Lowering this threshold to > 7% to define a test as positive increased the sensitivity of physician estimates to 89% but lowered specificity from 65% to 24%., Conclusion: Our data suggest that for a selected ED cohort meeting eligibility criteria for a study of CCTA, EPs predict ACS better than by chance, with an increasing proportion of patients proving to have ACS with increasing probability estimates. Lowering the estimate threshold does not result in an overall sensitivity level that is sufficient to send patients home from the ED and is associated with a poor specificity.

Although epidemiologic data link biomarkers of cardiovascular risk with incident and prevalent coronary artery disease, exact anatomic relationships between biomarkers and coronary atherosclerosis as measured by coronary CT angiography remain unclear. Patients with acute chest pain who ultimately had no evidence of acute coronary syndrome underwent contrast-enhanced 64-slice coronary CT angiography to determine presence, extent and composition of coronary atherosclerotic plaque. We determined the differences in levels of blood biomarkers measured at the time of the CT scan between different CT-based atherosclerotic plaque groups. Among 313 patients (mean age: 51.6 ± 11 years, 62% male) high-sensitivity C-reactive protein (hs-CRP) and matrix metalloproteinase-2 were associated with the extent of calcified plaque (P = 0.03 and P < 0.001), while hs-CRP and apolipoprotein A1 were associated with the extent of non-calcified plaque (P = 0.03 and P = 0.004; respectively). Despite a generally lower risk profile, subjects with exclusively non-calcified plaque had significantly higher levels of hs-CRP and oxidized low-density lipoprotein (P = 0.01 and P = 0.03; respectively) and lower levels of adiponectin (P = 0.03) when compared to subjects with calcified plaque (n = 130, 42%). Biomarkers reflecting inflammation, vascular remodeling, oxidation, and lipoprotein metabolism maybe associated with different patterns of coronary atherosclerosis as quantified by coronary CT angiography.

In recent years, technical advances and improvements in cardiac computed tomography (CT) and cardiac magnetic resonance imaging (MRI) have provoked increasing interest in the potential clinical role of these techniques in the non-invasive work-up of patients with suspected coronary artery disease (CAD) and correct patient selection for these emerging imaging techniques. In the primary detection or exclusion of significant CAD, e.g. in the patient with unspecific thoracic complaints, and also in patients with known CAD or advanced stages of CAD, both CT and MRI yield specific advantages. In this review, the major aspects of non-invasive MR and CT imaging in the diagnosis of CAD will be discussed. The first part describes the clinical value of contrast-enhanced non-invasive CT coronary angiography (CTCA), including the diagnostic accuracy of CTCA for the exclusion or detection of significant CAD with coronary artery stenoses that may require angioplastic intervention, as well as potentially valuable information on the coronary artery vessel wall. In the second section, the potential of CT for the imaging of myocardial viability and perfusion will be highlighted. In the third and final part, the range of applications of cardiac MRI in CAD patients will be outlined.