Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of CNNs. For its validation, reproducibility and further comparison against other methods, we tested the method on a big multi-field expert annotated LGE-MRI database including healthy and diseased cases. Results and Conclusion: In an exhaustive comparison against nine reference algorithms, the proposal achieved state-of-the-art segmentation performances and showed to be the only method agreeing in volumetric scar quantification with the expert delineations. Moreover, the method was able to reproduce the intra- and inter-observer variability ranges. It is concluded that the method could suitably be transferred to clinical scenarios.Comment: Submitted to IEE

Zirconia-titania-doped tantala optical coatings for low mechanical loss Bragg mirrors

The noise caused by internal mechanical dissipation in the high refractive index amorphous thin films in dielectric mirrors is an important limitation for gravitational wave detection. The objective of this study is to decrease this noise spectral density, which is linearly dependent on such dissipation and characterized by the loss angle of the Young’s modulus, by adding zirconia to titania-doped tantala, from which the current mirrors for gravitational wave detection are made. The purpose of adding zirconia is to raise the crystallization temperature, which allows the material to be more relaxed by raising the practical annealing temperature. The Ta, Ti and Zr oxides are deposited by reactive magnetron sputtering in an Ar:O2 atmosphere using radio-frequency and high power impulse plasma excitation. We show that thanks to zirconia, the crystallization temperature rises by more than 150◦C, which allows one to obtain a loss angle of 2.5 × 10−4 , that is, a decrease by a factor of 1.5 compared to the current mirror high-index layers. However, due to a difference in the coefficient of thermal expansion between the thin film and the silica substrate, cracks appear at high annealing temperature. In response, a silica capping layer is applied to increase the temperature of crack formation by 100◦C

(Mal)adaptive cognitions as predictors of statistics anxiety

A vast majority of social science students experience statistics anxiety in their statistics class, a course often perceived as the most difficult one of their academic paths. The present study examines the role of attitudes towards statistics, cognitive emotion regulation strategies, and satisfaction of psychological needs in the prediction of statistics anxiety as well as the contribution of gender onto statistics anxiety. Two hundred forty-two undergraduate social sciences students in Canada completed the study. Positive attitude towards statistics, fewer maladaptive emotion regulation strategies, and satisfaction of psychological needs were related to less statistics anxiety; adaptive emotion regulation strategies, however, were not. Furthermore, women experienced more statistics anxiety than men. Results provide insight about individual differences that may impact experiences of statistics anxiety and overall learning in the context of a statistics course

Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction from cardiac cine MRI sequences.

International audienceA statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliability of each method and its degree of automation. The results showed that the most accurate estimates of the ejection fraction were obtained using manual segmentations, followed by the semiautomatic methods, while the methods with the least user input yielded the least accurate ejection fraction estimates. These results were consistent with the expected performance of the estimation methods, suggesting that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available

Nonsupervised Ranking of Different Segmentation Approaches: Application to the Estimation of the Left Ventricular Ejection Fraction From Cardiac Cine MRI Sequences

International audienceA statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eightmethods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the ejection fraction were obtained using manual segmentations. The robustness of the ranking was demonstrated when at least three methods were compared. These results suggest that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available

Improved estimation of the left ventricular ejection fraction using a combination of independent automated segmentation results in cardiovascular magnetic resonance imaging

—This work aimed at combining different segmenta-tion approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by focusing on the left ventricular ejection fraction (LVEF) estimate resulting from the LV contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations, were studied, and sixteen combinations of the five automated methods were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates of the LVEF than individual automated segmentation methods. In addition, LVEF obtained with STAPLE were within inter-expert variability. Overall, combining different automated segmentation methods improved the reliability of the segmenta-tion result compared to that obtained using an individual metho

Utility of Cardiac Magnetic Resonance to assess association between admission hyperglycemia and myocardial damage in patients with reperfused ST-Segment Elevation Myocardial Infarction

International audienceAbstract: Aims: to investigate the association between admission hyperglycemia and myocardial damage in patients with ST-segment elevation myocardial infarction (STEMI) using Cardiac Magnetic Resonance (CMR). Methods: We analyzed 113 patients with STEMI treated with successful primary percutaneous coronary intervention. Admission hyperglycemia was defined as a glucose level >= 7.8 mmol/l. Contrast-enhanced CMR was performed between 3 and 7 days after reperfusion to evaluate left ventricular function and perfusion data after injection of gadolinium-DTPA. First-pass images (FP), providing assessment of microvascular obstruction and Late Gadolinium Enhanced images (DE), reflecting the extent of infarction, were investigated and the extent of transmural tissue damage was determined by visual scores. Results: Patients with a supramedian FP and DE scores more frequently had left anterior descending culprit artery (p = 0.02 and < 0.001), multivessel disease (p = 0.02 for both) and hyperglycemia (p < 0.001). Moreover, they were characterized by higher levels of HbA(1c) (p = 0.01 and 0.04), peak plasma Creatine Kinase (p < 0.001), left ventricular end-systolic volume (p = 0.005 and < 0.001), and lower left ventricular ejection fraction (p = 0.001 and < 0.001). In a multivariate model, admission hyperglycemia remains independently associated with increased FP and DE scores. Conclusion: Our results show the existence of a strong relationship between glucose metabolism impairment and myocardial damage in patients with STEMI. Further studies are needed to show if aggressive glucose control improves myocardial perfusion, which could be assessed using CMR

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch