State of the art: iterative CT reconstruction techniques

Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms over established analytical methods, such as filtered back projection. IR improves image quality through cyclic image processing. Although all available solutions share the common mechanism of artifact reduction and/or potential for radiation dose savings, chiefly due to image noise suppression, the magnitude of these effects depends on the specific IR algorithm. In the first section of this contribution, the technical bases of IR are briefly reviewed and the currently available algorithms released by the major CT manufacturers are described. In the second part, the current status of their clinical implementation is surveyed. Regardless of the applied IR algorithm, the available evidence attests to the substantial potential of IR algorithms for overcoming traditional limitations in CT imaging

Isolated Non-Compaction of the Left Ventricle in a Patient with New-Onset Heart Failure: Morphologic and Functional Evaluation with Cardiac Multidetector Computed Tomography

We describe a case of new-onset heart failure in a patient in whom cardiac CT enabled the non-invasive diagnosis of isolated non-compaction and associated functional abnormalities of the left ventricle with the concomitant evaluation of coronary arteries. This case highlights the utility of cardiac CT for the morphological and functional evaluation of the heart as a single imaging modality

Low CT temporal sampling rates result in a substantial underestimation of myocardial blood flow measurements

The purpose of this study was to evaluate the effect of temporal sampling rate in dynamic CT myocardial perfusion imaging (CTMPI) on myocardial blood flow (MBF). Dynamic perfusion CT underestimates myocardial blood flow compared to PET and SPECT values. For accurate quantitative analysis of myocardial perfusion with dynamic perfusion CT a stable calibrated HU measurement of MBF is essential. Three porcine hearts were perfused using an ex-vivo Langendorff model. Hemodynamic parameters were monitored. Dynamic CTMPI was performed using third generation dual source CT at 70 kVp and 230-350 mAs/rot in electrocardiography(ECG)-triggered shuttle-mode (sampling rate, 1 acquisition every 2-3 s; z-range, 10.2 cm), ECG-triggered non-shuttle mode (fixed table position) with stationary tube rotation (1 acquisition every 0.5-1 s, 5.8 cm), and non-ECG-triggered continuous mode (1 acquisition every 0.06 s, 5.8 cm). Stenosis was created in the circumflex artery, inducing different fractional flow reserve values. Volume perfusion CT Myocardium software was used to analyze ECG-triggered scans. For the non-ECG triggered scans MASS research version was used combined with an in-house Matlab script. MBF (mL/g/min) was calculated for non-ischemic segments. True MBF was calculated using input flow and heart weight. Significant differences in MBF between shuttle, non-shuttle and continuous mode were found, with median MBF of 0.87 [interquartile range 0.72-1.00], 1.20 (1.07-1.30) and 1.65 (1.40-1.88), respectively. The median MBF in shuttle mode was 56% lower than the true MBF. In non-shuttle and continuous mode, the underestimation was 41% and 18%. Limited temporal sampling rate in standard dynamic CTMPI techniques contributes to substantial underestimation of true MBF

Non-invasive fractional flow reserve (FFRCT) in the evaluation of acute chest pain ? Concepts and first experiences

Objective: To evaluate 30 day rate of major adverse cardiac events (MACE) utilizing cCTA and FFRCT for evaluation of patients presenting to the Emergency Department (ED) with acute chest pain. Materials and methods: Patients between the ages of 18?95 years who underwent clinically indicated cCTA and FFRCT in the evaluation of acute chest pain in the emergency department were retrospectively evaluated for 30 day MACE, repeat presentation/admission for chest pain, revascularization, and additional testing. Results: A total of 59 patients underwent CCTA and subsequent FFRCT for the evaluation of acute chest pain in the ED over the enrollment period. 32 out of 59 patients (54 %) had negative FFRCT (>0.80) out of whom 18 patients (55 %) were discharged from the ED. Out of the 32 patients without functionally significant CAD by FFRCT, 32 patients (100 %) underwent no revascularization and 32 patients (100 %) had no MACE at the 30-day follow-up period. Conclusion: In this limited retrospective study, patients presenting to the ED with acute chest pain and with CCTA with subsequent FFRCT of >0.8 had no MACE at 30 days; however, for many of these patients results were not available at time of clinical decision making by the ED physician

Myocardial extracellular volume fraction to differentiate healthy from cardiomyopathic myocardium using dual-source dual-energy CT

Objective: To evaluate the feasibility of dual-energy CT (DECT)-based iodine quantification to estimate myocardial extracellular volume (ECV) fraction in patients with and without cardiomyopathy (CM), as well as to assess its ability to distinguish healthy myocardial tissue from cardiomyopathic, with the goal of defining a threshold ECV value for disease detection. Methods: Ten subjects free of heart disease and 60 patients with CM (mean age 66.4 ± 9.4; 59 males and 11 females; 40 ischemic and 20 non-ischemic CM) underwent late iodine enhanced DECT imaging. Myocardial iodine maps were obtained using 3-material decomposition. ECV of the left ventricle was estimated from hematocrit levels and the iodine maps using the AHA 16-segment model. Receiver operating characteristic curve analysis was performed, with corresponding area under the curve, along with Youden's index assessment, to establish a threshold for CM detection. Results: The median ECV for healthy myocardium, non-ischemic CM, and ischemic CM were 25.4% (22.9–27.3), 38.3% (33.7–43.0), and 36.9% (32.4–41.1), respectively. Healthy myocardium showed significantly lower ECV values compared to ischemic and non-ischemic CM (p 29.5% would indicate the presence of CM in the myocardium (sensitivity = 90.3; specificity = 90.3); the AUC for this criterion was 0.950 (p < 0.001). Conclusion: The findings of this study resulted in a statistically significant distinction between healthy myocardium and CM ECVs. This led to the establishment of a promising threshold ECV value that could facilitate the differentiation between healthy and diseased myocardium, and highlights the potential of this DECT methodology to detect cardiomyopathic tissue

Комплексная оценка нефтеперерабатывающих заводов и заводов по переработке нефтей и природных битумов

Статья посвящена вопросам комплексной оценки нефтеперерабатывающих и нефтехимических заводов. Автор раскрывает способы оценки нефтеперерабатывающих заводов с помощью таких значений, как глубина переработки нефти и выход светлых нефтепродуктов, производит примерный расчёт затрат на строительство нефтеперерабатывающих установок с учётом их сложности и производительности за период времени, а также производит расчёт комплексной оценка Нельсона, сопоставленной с глубиной нефтепереработки и проводит анализ полученных результатов.The article is devoted to the issues of integrated assessment of refineries and petrochemical plants. The author reveals ways to evaluate refineries using such values as oil refining depth and light oil yield, makes an approximate calculation of the construction costs of refineries, taking into account their complexity and performance over a period of time, and also calculates a comprehensive assessment of Nelson compared to the depth of refining and analyzes the results obtained

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

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