Noninvasive characterization of myocardial tissue damage applying cardiac magnetic resonance

Einführung: Kardiale Magnetresonanztomografie mit der Anwendung von Kontrastmitteln ist eine etablierte klinische Untersuchung zur Differenzierung von Myokardschäden. Die Auswertung der Bilddaten basiert zu einem großen Teil auf subjektiv geführten Methoden. Die Quantifizierung relevanter pathophysiologischer Prozesse könnte zur Objektivierung der Untersuchung beitragen. Daher ist es unser Ziel, ein Verfahren vorzuschlagen, die Pathophysiologie von myokardialen Ödemen und Fibrose zu charakterisieren. Methoden: Mittels Computational Fluid Dynamics (CFD) wurde der Kontrastmittelfluss in pathologischem Gewebe modelliert. Die Modellparameter ExR, welcher den Austausch von Kontrastmittel (KM) zwischen dem vaskulären und extravaskulären Raum darstellt, wurde verwendet, um unterschiedliche Flussszenarien zu erzeugen. Die Simulationen wurden dann mit quantitativen T1-Maps des Herzens von n = 18 Patienten mit akuter und geheilter Myokarditis sowie alters- und geschlechtsangepassten Probanden aus einer zuvor veröffentlichten Studie verglichen. T1-Maps wurden von der medialen Schicht vor und 1,3,5,7 und 10 Minuten nach der KM-Verabreichung verwendet. Ergebnisse: Der Vorgang zum Lesen und Registrieren aller T1-Maps des Myokards und die Umrechnung auf die KM-Konzentration war bei 10 aufeinander abgestimmten Gruppen von akuten und geheilten Patienten sowie gesunden Probanden erfolgreich. Die simulierten KM Auswaschungs-Kurven wurden an die Messungen in der Austauschrate ExR mit einem Fehler von weniger als 5% gefittet. Signifikante Unterschiede (P <0,05) wurden zwischen akuten und geheilt Patienten sowie geheilten Patienten und Probanden gefunden. Ein größerer Unterschied (P <0,01) wurde zwischen akuten Patienten und Probanden festgestellt. Fazit: Unsere Ergebnisse deuten darauf hin, dass CFD für die Simulation von pathologischem und gesundem Myokardgewebe eingesetzt werden kann. Moderne Machine Learning6 Techniken können in Zukunft mit quantitativen Merkmalen wie der Austauschrate ExR oder anderen T1-Map Merkmalen angewendet werden um myokardiale Schäden zu differenzieren.Introduction: Cardiac magnetic resonance imaging with the application of contrast media is a clinical examination for the differentiation of myocardial damage. The evaluation of the images is based to a large extent on subjectively guided methods. The quantification of relevant pathophysiological processes could contribute to the objectification of the investigation. Therefore, our aim is to propose a method to quantify the pathophysiology in myocardial edema and fibrosis. Methods: Using Computational Fluid Dynamics (CFD), the flow of contrast agent in pathological tissue was modeled. The model parameter ExR governing the exchange of contrast medium(CM) between the vascular and extravascular space was used to generate different flow scenarios. The simulations were then compared to quantitative cardiac T1 maps from n=18 patients with acute and healed myocarditis as well as age- and sexmatched volunteers from a previously published study. T1 maps had been acquired of the medial slice before and 1,3,5,7 and 10 minutes after CM administration. Results: The pipeline of reading and registering all myocardial T1 maps and conversion to CM concentration was successful in 10 matched groups of acute and healed patients as well as volunteers. The simulated CM washout curves were fitted to the measurements in the exchange rate ExR with an error of less than 5%. Significant differences (P<0.05) were found between acute and healed patients, as well as healed patients to volunteers. A greater difference (P<0.01) was found between acute patients and volunteers. Conclusion: Our results suggest the feasibility of using CFD for the simulation of pathologic and healthy myocardial tissue. Modern Machine Learning techniques can be applied in the future for the differentiation of myocardial tissue using quantitative features such as the exchange rate ExR or other T1 map characteristics

Highly Accurate, But Still Discriminatory

The study aims to identify whether algorithmic decision making leads to unfair (i.e., unequal) treatment of certain protected groups in the recruitment context. Firms increasingly implement algorithmic decision making to save costs and increase efficiency. Moreover, algorithmic decision making is considered to be fairer than human decisions due to social prejudices. Recent publications, however, imply that the fairness of algorithmic decision making is not necessarily given. Therefore, to investigate this further, highly accurate algorithms were used to analyze a pre-existing data set of 10,000 video clips of individuals in self-presentation settings. The analysis shows that the under-representation concerning gender and ethnicity in the training data set leads to an unpredictable overestimation and/or underestimation of the likelihood of inviting representatives of these groups to a job interview. Furthermore, algorithms replicate the existing inequalities in the data set. Firms have to be careful when implementing algorithmic video analysis during recruitment as biases occur if the underlying training data set is unbalanced

Validation of simulations in multiphase flow metrology by comparison with experimental video observations

One important task in flow metrology is to evaluate the uncertainty in multiphase flow metering. A first important step towards this goal is to establish an accurate computational fluid dynamics (CFD) model of multiphase flows. In this contribution, results of multiphase flow simulations are validated by comparison with experimental data. For the evaluation and quantification of experimental observations, a tool for video analysis has been implemented. This tool extracts the liquid level over time, which is then used for further analysis and comparison with simulation data. Additional relevant parameters are obtained by frequency analysis, which is applied to both, experimental and simulation data. A comparison of the results shows good agreement between experiment and simulation

Deep learning based liquid level extraction from video observations of gas-liquid flows

The slug flow pattern is one of the most common gas–liquid flow patterns in multiphase transportation pipelines, particularly in the oil and gas industry. This flow pattern can cause severe problems for industrial processes. Hence, a detailed description of the spatial distribution of the different phases in the pipe is needed for automated process control and calibration of predictive models. In this paper, a deep-learning based image processing technique is presented that extracts the gas–liquid interface from video observations of multiphase flows in horizontal pipes. The supervised deep learning model consists of a convolutional neural network, which was trained and tested with video data from slug flow experiments. The consistency of the hand-labelled data and the predictions of the trained model have been evaluated in an inter-observer reliability test. The model was further tested with other data sets, which also included recordings of a different flow pattern. It is shown that the presented method provides accurate and reliable predictions of the gas–liquid interface for slug flow as well as for other separate flow patterns. Moreover, it is demonstrated how flow characteristics can be obtained from the results of the deep-learning based image processing technique

Quantification of myocardial strain assessed by cardiovascular magnetic resonance feature tracking in healthy subjects - influence of segmentation and analysis software

OBJECTIVES: Quantification of myocardial deformation by feature tracking is of growing interest in cardiovascular magnetic resonance. It allows the assessment of regional myocardial function based on cine images. However, image acquisition, post-processing, and interpretation are not standardized. We aimed to assess the influence of segmentation procedure such as slice selection and different types of analysis software on values and quantification of myocardial strain in healthy adults. METHODS: Healthy volunteers were retrospectively analyzed. Post-processing was performed using CVI(42) and TomTec. Longitudinal and radial(Long axis (LAX)) strain were quantified using 4-chamber-view, 3-chamber-view, and 2-chamber-view. Circumferential and radialShort axis (SAX) strain were assessed in basal, midventricular, and apical short-axis views and using full coverage. Global and segmental strain values were compared to each other regarding their post-processing approach and analysis software package. RESULTS: We screened healthy volunteers studied at 1.5 or 3.0 T and included 67 (age 44.3 ± 16.3 years, 31 females). Circumferential and radial(SAX) strain values were different between a full coverage approach vs. three short slices (- 17.6 ± 1.8% vs. - 19.2 ± 2.3% and 29.1 ± 4.8% vs. 34.6 ± 7.1%). Different analysis software calculated significantly different strain values. Within the same vendor, different field strengths (- 17.0 ± 2.1% at 1.5 T vs. - 17.0 ± 1.7% at 3 T, p = 0.845) did not influence the calculated global longitudinal strain (GLS), and were similar in gender (- 17.4 ± 2.0% in females vs. - 16.6 ± 1.8% in males, p = 0.098). Circumferential and radial strain were different in females and males (circumferential strain - 18.2 ± 1.7% vs. - 17.1 ± 1.8%, p = 0.029 and radial strain 30.7 ± 4.7% vs. 27.8 ± 4.6%, p = 0.047). CONCLUSIONS: Myocardial deformation assessed by feature tracking depends on segmentation procedure and type of analysis software. Circumferential(SAX) and radial(SAX) depend on the number of slices used for feature tracking analysis. As known from other imaging modalities, GLS seems to be the most stable parameter. During follow-up studies, standardized conditions should be warranted

Translating principles of quality control to cardiovascular magnetic resonance: assessing quantitative parameters of the left ventricle in a large cohort

Cardiac magnetic resonance (CMR) examinations require standardization to achieve reproducible results. Therefore, quality control as known as in other industries such as in-vitro diagnostics, could be of essential value. One such method is the statistical detection of long-time drifts of clinically relevant measurements. Starting in 2010, reports from all CMR examinations of a high-volume center were stored in a hospital information system. Quantitative parameters of the left ventricle were analyzed over time with moving averages of different window sizes. Influencing factors on the acquisition and on the downstream analysis were captured. 26,902 patient examinations were exported from the clinical information system. The moving median was compared to predefined tolerance ranges, which revealed an overall of 50 potential quality relevant changes ("alerts") in SV, EDV and LVM. Potential causes such as change of staff, scanner relocation and software changes were found not to be causal of the alerts. No other influencing factors were identified retrospectively. Statistical quality assurance systems based on moving average control charts may provide an important step towards reliability of quantitative CMR. A prospective evaluation is needed for the effective root cause analysis of quality relevant alerts

Oxidized low density lipoprotein regulates apoptosis and growth factor production in macrophages

Macrophages and oxidized LDL (oxLDL) both play key roles in the pathogenesis of atherogenesis. This thesis is focused on the effects of oxLDL on macrophage cytokine secretion and macrophage survival. The first project defines some of the mechanisms by which oxLDL increases secretion of vascular endothelial growth factor (VEGF), a pro-inflammatory growth factor known to be involved in atherogenesis. We show that both the protein and lipid components of oxLDL contribute to induction of VEGF secretion. Disruption of the genes for CD36, SR-A, or LOX-1 scavenger receptors had no effect. The atypical protein kinase c (PKCζ) was activated by oxLDL, and this activation was essential for the induction of VEGF. OxLDL could be atherogenic through increasing macrophage number within the plaque. Our group has previously shown that oxLDL induces growth and inhibits apoptosis in macrophages. In the second project, we sought to determine if members of the scavenger receptor family were required for the prosurvival effect of oxLDL in macrophages. We used mouse strains lacking different scavenger receptors and found that oxLDL-mediated survival is not dependent on CD36, SR-A, LOX-1, TLR4, its signaling partner CD14, or FcγRIIb. Significant inhibition of oxLDL uptake by a combined inactivation of CD36 and SR-A did not reduce the prosurvival effect of oxLDL. In the third project, we sought to characterize the oxidative modification of LDL that is responsible for the prosurvival effect. We found that both protein and lipid components of oxLDL can induce growth in macrophages. This seems to be mediated by modification of amino groups in apoB or in phosphatidylethanolamine by lipid peroxidation products. Further characterization of these oxidation products suggested that unfragmented hydroperoxide or endoperoxide-containing oxidation products of linoleic acid and arachidonic acid derivatize amino groups. When LDL or other proteins are modified in this fashion, they acquire the ability to induce pro-survival signaling pathways in macrophages. HPLC-MSMS studies showed that some of the arachidonic acid-derived lysine adducts are isolevuglandin (isoLG)-derived adducts that contain lactam and hydroxylactam rings. MSMS analysis of linoleic acid autoxidation adducts was consistent with 5 or 6 membered nitrogen-containing heterocycles derived from unfragmented oxidation products.Medicine, Faculty ofMedicine, Department ofExperimental Medicine, Division ofGraduat

Oxidized LDL-mediated macrophage survival involves elongation factor-2 kinase

OBJECTIVE— Macrophage survival and proliferation is believed to be a contributing factor in the development of early atherosclerotic lesions. Oxidized low density lipoprotein (oxLDL), a key mediator in the pathogenesis of this disease, has been shown to block apoptosis in macrophages deprived of growth factor. In this report, we investigate the mechanism of oxLDL-mediated macrophage survival. METHODS AND RESULTS— OxLDL, but not native LDL (nLDL), induces an immediate and oscillatory increase in intracellular calcium ([Ca2+]i). We also show that the calcium/calmodulin dependent kinase, eukaryotic elongation factor-2 kinase (eEF2 kinase), is activated in response to oxLDL, an effect that can be blocked by inhibiting calcium mobilization. Furthermore, selective inhibition of eEF2 kinase reverses the prosurvival effect of oxLDL and results in cellular apoptosis. p38 MAP kinase, a negative regulator of eEF2 kinase, is activated on growth factor withdrawal, a response that can be inhibited by oxLDL. Finally, we show that oxLDL, by activating eEF2 kinase, phosphorylates and therefore inhibits eEF2, resulting in an overall decrease in protein synthesis. CONCLUSION— These results indicate a novel signaling pathway in which oxLDL can block macrophage apoptosis by mobilizing calcium and activating eEF2 kinase.Johnny H. Chen, Maziar Riazy, Ewan M. Smith, Christopher G. Proud, Urs P. Steinbrecher, Vincent Duroni