ROBERTO NERY DANTAS JUNIOR
Projetos de Pesquisa
Unidades Organizacionais
LIM/65, Hospital das Clínicas, Faculdade de Medicina
3 resultados
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Agora exibindo 1 - 3 de 3
- Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR(2023) GRAVES, Catharine V.; REBELO, Marina F. S.; MORENO, Ramon A.; DANTAS-JR, Roberto N.; JR, Antonildes N. Assuncao; NOMURA, Cesar H.; GUTIERREZ, Marco A.Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.
- A combined deep-learning approach to fully automatic left ventricle segmentation in cardiac magnetic resonance imaging(2019) MORENO, Ramon A.; REBELO, Marina F. S. de Sa; CARVALHO, Talles; ASSUNCAO-JR, Antonildes N.; JR, Roberto N. Dantas; VAL, Renata do; MARIN, Angela S.; BORDIGNOM, Adriano; NOMURA, Cesar H.; GUTIERREZ, Marco A.In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of function and structure of the left ventricle (LV). However, the quantification of LV parameters in all frames, even when performed by experienced radiologists, is very time consuming mainly due to the inhomogeneity of cardiac structures within each image, the variability of the cardiac structures across subjects and the complicated global/regional temporal deformation of the myocardium during the cardiac cycle. In this work, we employed a combination of two convolutional neural networks (CNN) to develop a fully automatic LV segmentation method for Short Axis CMR datasets. The first CNN defines the region of interest (ROI) of the cardiac chambers based on You Only Look Once (YOLO) network. The output of YOLO net is used to filter the image and feed the second CNN, based on U-Net network, which segments the myocardium and the blood pool. The method was validated in CMR exams of 59 individuals from an institutional clinical protocol. Segmentation results, evaluated by metrics Percentage of Good Contours, Dice Index and Average Perpendicular distance, were 98,59% +/- 4,28%, 0,93 +/- 0,06 and 0,72 mm +/- 0,62 mm, respectively, for the LV epicardium, and 94,98% +/- 14,04%, 0,86 +/- 0,13 and 1,19 mm +/- 1,29 mm, respectively, for the LV endocardium. The combination of two CNNs demonstrated good performance in terms of the evaluated metrics when compared to literature results.
- Clinical evaluation of left ventricular function and morphology using an accelerated k-t sensitivity encoding method in cardiovascular magnetic resonance(2019) ASSUNCAO- JR., Antonildes Nascimento; DANTAS- JR., Roberto Nery; VAL, Renata Margarida do; GIANOTTO, Priscilla; MARIN, Angela dos Santos; GOLDEN, Mark; GUTIERREZ, Marco Antonio; PARGA, Jose Rodrigues; NOMURA, Cesar HigaObjectivesTo provide clinical validation of a recent 2D SENSE-based accelerated cardiovascular magnetic resonance (CMR) sequence (accelerated k-t SENSE), investigating whether this technique accurately quantifies left ventricle (LV) volumes, function, and mass as compared to 2D cine steady-state free precession (2D-SSFP).MethodsHealthy volunteers (n=16) and consecutive heart failure patients (n=26) were scanned using a 1.5T MRI system. Two LV short axis (SA) stacks were acquired: (1) accelerated k-t SENSE (5-6 breath-holds; temporal/spatial resolution: 37ms/1.82x1.87mm; acceleration factor = 4) and (2) standard 2D-SSFP (10-12 breath-holds; temporal/spatial resolution: 49ms/1.67x1.87mm, parallel imaging). Ascending aorta phase-contrast was performed on all volunteers as a reference to compare LV stroke volumes (LVSV) and validate the sequences. An image quality score for SA images was used, with lower scores indicating better quality (from 0 to 18).ResultsThere was a high agreement between accelerated k-t SENSE and 2D-SSFP for LV measurements: bias (limits of agreement) of 2.4% (-5.4% to 10.1%), 6.9mL/m(2) (-4.7 to 18.6mL/m(2)), -1.5 (-8.3 to 5.2mL/m(2)), and -0.2g/m(2) (-11.9 to 12.3g/m(2)) for LV ejection fraction, end-diastolic volume index, end-systolic volume index, and mass index, respectively. LVSV by accelerated k-t SENSE presented good agreement with aortic flow. Interobserver and intraobserver variabilities for all LV parameters were also high.ConclusionThe accelerated k-t SENSE CMR sequence is clinically feasible and accurately quantifies LV volumes, function, and mass, with short acquisition time and good image quality.