Free-breathing myocardial T2 measurements at 1.5T

POSTER PRESENTATIONInternational audienc

Étude rétrospective des déclarations d'évènements indésirables graves suite à l'utilisation de vaccin chez le chien

Les vaccins canins sont des médicaments utilisés pour protéger les chiens de maladies graves. Mais, des événements indésirables graves peuvent être observés suite à leur utilisation. Cette étude rétrospective fait le bilan de ces événements déclarés au département de pharmacovigilance de l’Agence Nationale du Médicament Vétérinaire entre 2012 et 2016. Ils se révèlent rares à très rares en fonction des vaccins avec en moyenne 1 effet grave tous les 27 000 chiens vaccinés et 1 mort tous les 180 500 chiens vaccinés. Les réactions d’hypersensibilité de type I prédominent. Les chiens de moins d’un an et les petites races y semblent prédisposés. Le rôle du vétérinaire est d’informer le propriétaire sur ces événements indésirables et d’adapter le choix des valences et du protocole aux modes de vie des chiens. La vaccination doit rester raisonnée et apporter une protection efficace en évitant les injections inutiles. La balance bénéfice/risque reste largement en faveur de la vaccination

Data driven background estimation in HEP using Generative Adversarial Networks

Data-driven methods are widely used to overcome shortcomings of Monte Carlo simulations (lack of statistics, mismodeling of processes, etc.) in experimental high energy physics. A precise description of background processes is crucial to reach the optimal sensitivity for a measurement. However, the selection of the control region used to describe the background process in a region of interest biases the distribution of some physics observables, rendering the use of such observables impossible in a physics analysis. Rather than discarding these events and/or observables, we propose a novel method to generate physics objects compatible with the region of interest and properly describing the correlations with the rest of the event properties. We use a generative adversarial network (GAN) for this task, as GANs are among the best generator models for various applications. We illustrate the method by generating a new misidentified photon for the $\gamma + \mathrm{jets}$ background of the $\mathrm{H}\to\gamma\gamma$ analysis at the CERN LHC, and demonstrate that this GAN generator is able to produce a coherent object correlated with the different properties of the rest of the event

Obesidade infantil: projeto de intervenção para promover hábitos alimentares e estilos de vida saudáveis aos alunos de 1ª e 2ª série da Escola Estadual de Ensino Fundamental do bairro Carapebus – Serra – ES

No mundo em geral e no Brasil em particular, o problema da obesidade infantil tem se revelado como um novo desafio para a saúde pública, uma vez que sua incidência e prevalência têm crescido de forma alarmante nos últimos 30 anos. Grande parte estaria relacionada à má alimentação (95%, exógena), enquanto, apenas 5% seriam decorrentes de fatores endógenos. A falta de informação sobre a obesidade infantil e seus riscos e as ideias erradas com relação à alimentação no seio da família e na própria equipe de saúde, são o maior problema a ser superado para conseguir uma boa adesão ao tratamento. O presente trabalho objetiva desenhar um projeto de intervenção a ser realizado na escola de ensino fundamental e na UBS do bairro Carapebus, onde trabalharemos o conhecimento e a autoestima dos participantes com o intuito de promover a adesão ao tratamento nutricional e sensibilizar responsáveis e crianças sobre a importância da aquisição de hábitos nutricionais saudáveis. O plano operativo propõe oficinas de qualificação aos trabalhadores da UBS e professor de educação física da escola; questionário aos responsáveis das crianças com sobrepeso e obesidade sobre fatores associados à obesidade (hábitos de lazer, sedentarismo, atividades físicas, frequência qualitativa de alimentos, nível socioeconômico...); consulta médica e nutricional com avaliação antropométrica; oficinas de educação em saúde sobre alimentação saudável e atividade física; avaliação do impacto das ações realizadas. O projeto está estruturado para ser realizado em 4 etapas com uma duração total de 12 meses e a metodologia a aplicar será a pesquisa-ação

Deep Learning Convolutional Neural Network Reconstruction and Radial k-Space Acquisition MR Technique for Enhanced Detection of Retropatellar Cartilage Lesions of the Knee Joint

OBJECTIVES To assess diagnostic performance of standard radial k-space (PROPELLER) MRI sequences and compare with accelerated acquisitions combined with a deep learning-based convolutional neural network (DL-CNN) reconstruction for evaluation of the knee joint. METHODS Thirty-five patients undergoing MR imaging of the knee at 1.5 T were prospectively included. Two readers evaluated image quality and diagnostic confidence of standard and DL-CNN accelerated PROPELLER MR sequences using a four-point Likert scale. Pathological findings of bone, cartilage, cruciate and collateral ligaments, menisci, and joint space were analyzed. Inter-reader agreement (IRA) for image quality and diagnostic confidence was assessed using intraclass coefficients (ICC). Cohen's Kappa method was used for evaluation of IRA and consensus between sequences in assessing different structures. In addition, image quality was quantitatively evaluated by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. RESULTS Mean acquisition time of standard vs. DL-CNN sequences was 10 min 3 s vs. 4 min 45 s. DL-CNN sequences showed significantly superior image quality and diagnostic confidence compared to standard MR sequences. There was moderate and good IRA for assessment of image quality in standard and DL-CNN sequences with ICC of 0.524 and 0.830, respectively. Pathological findings of the knee joint could be equally well detected in both sequences (κ-value of 0.8). Retropatellar cartilage could be significantly better assessed on DL-CNN sequences. SNR and CNR was significantly higher for DL-CNN sequences (both p < 0.05). CONCLUSIONS In MR imaging of the knee, DL-CNN sequences showed significantly higher image quality and diagnostic confidence compared to standard PROPELLER sequences, while reducing acquisition time substantially. Both sequences perform comparably in the detection of knee-joint pathologies, while DL-CNN sequences are superior for evaluation of retropatellar cartilage lesions

Optimization of quantitative susceptibility mapping for regional estimation of oxygen extraction fraction in the brain

Purpose: We sought to determine the degree to which oxygen extraction fraction (OEF) estimated using quantitative susceptibility mapping (QSM) depends on two critical acquisition parameters that have a significant impact on acquisition time: voxel size and final echo time. Methods: Four healthy volunteers were imaged using a range of isotropic voxel sizes and final echo times. The 0.7 mm data were downsampled at different stages of QSM processing by a factor of 2 (to 1.4 mm), 3 (2.1 mm), or 4 (2.8 mm) to determine the impact of voxel size on each analysis step. OEF was estimated from 11 veins of varying diameter. Inter- and intra- session repeatability were estimated for the opti-mal protocol by repeat scanning in 10 participants. Results: Final echo time was found to have no significant effect on OEF. The effect of voxel size was significant, with larger voxel sizes underestimating OEF, depending on the proximity of the vein to the superficial surface of the brain and on vein diameter. The last analysis step of estimating vein OEF values from susceptibility images had the largest dependency on voxel size. Inter- session coefficients of variation on OEF estimates of between 5.2% and 8.7% are reported, depending on the vein. Conclusion: QSM acquisition times can be minimized by reducing the final echo time but an isotropic voxel size no larger than 1 mm is needed to accurately estimate OEF in most medium/large veins in the brain. Such acquisitions can be achieved in under 4 mi

Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography

OBJECTIVE The study aims to evaluate the diagnostic performance of deep learning-based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus. MATERIALS AND METHODS Thirty-five exams (18 brachial and 17 lumbosacral plexus) of 34 patients undergoing routine clinical MR neurography at 1.5 T were retrospectively included (mean age: 49 ± 12 years, 15 female). Coronal 3D T2-weighted short tau inversion recovery fast spin echo with variable flip angle sequences covering plexial nerves on both sides were obtained as part of the standard protocol. In addition to standard-of-care (SOC) reconstruction, k-space was reconstructed with a 3D DLRecon algorithm. Two blinded readers evaluated images for image quality and diagnostic confidence in assessing nerves, muscles, and pathology using a 4-point scale. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) between nerve, muscle, and fat were measured. For comparison of visual scoring result non-parametric paired sample Wilcoxon signed-rank testing and for quantitative analysis paired sample Student's t-testing was performed. RESULTS DLRecon scored significantly higher than SOC in all categories of image quality (p < 0.05) and diagnostic confidence (p < 0.05), including conspicuity of nerve branches and pathology. With regard to artifacts there was no significant difference between the reconstruction methods. Quantitatively, DLRecon achieved significantly higher CNR and SNR than SOC (p < 0.05). CONCLUSION DLRecon enhanced overall image quality, leading to improved conspicuity of nerve branches and pathology, and allowing for increased diagnostic confidence in evaluation of the brachial and lumbosacral plexus

Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

OBJECTIVES To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. METHODS In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen's kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). RESULTS Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences (p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR (p value = 0.01) and CNR (p value = 0.02) were significantly higher for DL sequences. CONCLUSIONS The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. KEY POINTS • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning-based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint