A5057163590- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Neonatal and fetal brain pathology
- Sleep and Wakefulness Research
- Radiomics and Machine Learning in Medical Imaging
- Neurogenesis and neuroplasticity mechanisms
- Medical Image Segmentation Techniques
- Epilepsy research and treatment
- Infant Health and Development
- COVID-19 diagnosis using AI
- Cerebrovascular and Carotid Artery Diseases
- Cardiovascular Health and Disease Prevention
- Diet and metabolism studies
- Lung Cancer Diagnosis and Treatment
- Sparse and Compressive Sensing Techniques
- Neuroscience of respiration and sleep
- Public Health in Brazil
- Dental Radiography and Imaging
- Neural Networks and Applications
- Laser Applications in Dentistry and Medicine
- Dental Anxiety and Anesthesia Techniques
- Functional Brain Connectivity Studies
- Cardiac Imaging and Diagnostics
- Neonatal Respiratory Health Research
- Healthcare Regulation
Universidade Estadual de Campinas (UNICAMP)
2017-2024
Massachusetts General Hospital
2024
Harvard University
2024
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate MRI acquisition process. Nevertheless, scientific community lacks appropriate benchmarks assess quality of high-resolution images, and evaluate how these proposed algorithms will behave in presence small, but expected data distribution shifts. The multi-coil (MC-MRI) challenge provides a benchmark that aims at addressing issues, using large dataset high-resolution,...
The hypothalamus is a small brain structure that plays essential roles in sleep regulation, body temperature control, and metabolic homeostasis. Hypothalamic structural abnormalities have been reported neuropsychiatric disorders, such as schizophrenia, amyotrophic lateral sclerosis, Alzheimer's disease. Although mag- netic resonance (MR) imaging the standard examination method for evaluating this region, hypothalamic morphological landmarks are unclear, leading to subjec- tivity high...
The aim of this study was to test two low-level laser therapy protocols by evaluating pain control, swelling and trismus in the postoperative period lower third molar surgeries. This a randomized, double-blind, placebo-controlled, crossover trial. Patients presenting symmetrically impacted mandibular molars were included. One side randomly assigned for LLLT applied immediately after surgery (T1) then 24 (T2) 48 hours (T3) (Protocol A). other received placebo B). given intraoral application...
Hypothalamus is a small structure of the brain with an important role in sleep, appetite, body temperature regulation and emotion. Some neurological diseases, such as Schizophrenia, Alzheimer Amyotrophic Lateral Sclerosis (ALS) may be related to hypothalamic volume variation. However, morphological landmarks are not always clear on magnetic resonance (MR) images manual segmentation can become variable, leading inconsistent findings literature. In this work, we propose fully automatic method,...
The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of patients, However, applications real-world scenarios are still needed. We describe the development deployment decision support system. A partnership with a Brazilian radiologist consortium, gave us access 1000s labeled computed tomography (CT) X-ray images São Paulo Hospitals. system used EfficientNet EfficientDet networks, state-of-the-art convolutional...
Purpose: To develop a method for automated segmentation of hypothalamus subregions informed by ultra-high resolution ex vivo magnetic resonance images (MRI), which generalizes across MRI sequences and resolutions without retraining. Materials Methods: We trained our deep learning method, H-synEx, with synthetic derived from label maps built scans, enables finer-grained manual when compared 1mm isometric in images. validated this retrospective study using 1535 six datasets sequences. The...
Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it prerequisite for different analyses such volumetry or shape analysis. Automated segmentation facilitates the study in larger cohorts when compared with manual segmentation, which time-consuming. However, development most automated methods relies large and manually annotated datasets, limits generalizability these methods. Recently, new techniques using synthetic images have...
Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it prerequisite for different analyses such volumetry or shape analysis. Automated segmentation facilitates the study in larger cohorts when compared with manual segmentation, which time-consuming. However, development most automated methods relies large and manually annotated datasets, limits generalizability these methods. Recently, new techniques using synthetic images have...
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based synthetic data, generalizable machine learning brain MRI analysis. Central to this framework is the concept domain randomization, which involves training neural...
Introdução: As estatinas são uma classe de medicamentos utilizados para reduzir os níveis colesterol no sangue, principalmente o LDL, diminuindo assim risco doenças cardiovasculares. Embora seu uso seja amplamente difundido em adultos, sua administração crianças ainda é limitada, sendo mais comum casos hipercolesterolemia familiar. Objetivo: Analisar estudos existentes sobre e eficácia na prevenção eventos Método: Foi produzida Revisão Integrativa da Literatura, com base questão pesquisa:...
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate MRI acquisition process. Nevertheless, scientific community lacks appropriate benchmarks assess quality of high-resolution images, and evaluate how these proposed algorithms will behave in presence small, but expected data distribution shifts. The Multi-Coil Magnetic Resonance Image (MC-MRI) Reconstruction Challenge provides a benchmark that aims at addressing issues, using...
The Coronavirus Disease 2019 (COVID-19) pandemic that affects the world since 2020 generated a great amount of research interest in how to provide aid medical staff on triage, diagnosis, and prognosis. This work proposes an automated segmentation model over Computed Tomography (CT) scans, segmenting lung COVID-19 related findings at same time. Manual is time-consuming complex task, especially when applied high-resolution CT resulting lack gold standards annotation. Thanks data provided by...
Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with basic concepts. This tutorial paper aims provide overview fundamental concepts imaging, a focus on Magnetic Resonance Computerized Tomography. We will also discuss deep learning algorithms, tools, frameworks used for tasks, suggest best practices method development Our includes sample tasks using public data,...
Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging non-invasive method that can be used analyze the carotid artery detect signs atherosclerosis. Most MR methods acquire high contrast, static images. These methods, however, are sensitive artifacts from cardiac motion, produce time-averaged images, do not allow distensibility analysis. Carotid an important, systematic measure vascular health. Cine...