A5089425504- Digital Radiography and Breast Imaging
- AI in cancer detection
- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Endometrial and Cervical Cancer Treatments
- Image and Signal Denoising Methods
- Radiomics and Machine Learning in Medical Imaging
- Infrared Thermography in Medicine
- Cervical Cancer and HPV Research
- Advanced Image Fusion Techniques
- Radiation Dose and Imaging
- Dental Radiography and Imaging
- Ovarian cancer diagnosis and treatment
- Endometriosis Research and Treatment
- Fetal and Pediatric Neurological Disorders
- Photoacoustic and Ultrasonic Imaging
- MRI in cancer diagnosis
- Cancer Risks and Factors
- Neural Networks and Applications
- Advanced Neuroimaging Techniques and Applications
- Breast Cancer Treatment Studies
- Image and Video Quality Assessment
- Genital Health and Disease
- Reproductive Biology and Fertility
- Parvovirus B19 Infection Studies
Universidade de São Paulo
2013-2024
Beneficência Portuguesa de São Paulo
2022
Hospital de Câncer de Barretos
2011-2021
Hospital São Paulo
2020
Universidade de Ribeirão Preto
1997-2009
Universidade Federal de São Carlos
2006-2009
Universidad San Pedro
1998
Associação Paulista de Medicina
1991
The purpose of this study was to evaluate the impact and fracture resistance acrylic resins: a heat-polymerized resin, high-impact resin an experimental polymethyl methacrylate with elastomer in different proportions (10, 20, 40 60%). 120 specimens were fabricated submitted conventional heat-polymerization. For test, Charpy-type tester used. Fracture assessed 3-point bending test by using mechanical testing machine. Ten used for each test. (MPa) values (J.m-1) ANOVA - Bonferroni's 5%...
Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with standard dose. Methods: The developed this consists of scaling mammogram at the radiation and adding signal‐dependent noise. algorithm accounts specific issues relevant images, such as anisotropic noise, spatial variations pixel gain, effect on detective quantum efficiency. process takes into account linearity system offset detector elements....
This paper proposes a new method of simulating dose reduction in digital breast tomosynthesis, starting from clinical image acquired with standard radiation dose. It considers both signal-dependent quantum and signal-independent electronic noise. Furthermore, the accounts for pixel crosstalk, which causes noise to be frequency-dependent, thus increasing simulation accuracy. For an objective assessment, simulated real images were compared terms deviation, signal-to-noise ratio (SNR)...
Denoising can be used as a tool to enhance image quality and enforce low radiation doses in X-ray medical imaging. The effectiveness of denoising techniques relies on the validity underlying noise model. In full-field digital mammography (FFDM) breast tomosynthesis (DBT), calibration steps like detector offset flat-fielding affect some assumptions made by most techniques. Furthermore, quantum found images is signal-dependent only treated specific filters. this work we propose pipeline for...
In breast cancer screening, the radiation dose must be kept to minimum necessary achieve desired diagnostic objective, thus minimizing risks associated with induction. However, decreasing also degrades image quality. this work we restore digital tomosynthesis (DBT) projections acquired at low doses goal of achieving a quality comparable that obtained from current standard full-dose imaging protocols. A multiframe denoising algorithm was applied low-dose projections, which are filtered...
Individual projection images in Digital Breast Tomosynthesis (DBT) must be acquired with low levels of radiation, which significantly increases image noise. This work investigates the influence a denoising algorithm and Anscombe transformation on reduction quantum noise DBT images. The is variance-stabilizing that converts signal-dependent to an approximately signalindependent Gaussian additive Thus, this allows for use conventional algorithms, designed noise, by working domain. In work, was...
The objective of this study is to evaluate the efficacy deep learning (DL) techniques in improving quality diffusion MRI (dMRI) data clinical applications. aims determine whether use artificial intelligence (AI) methods medical images may result loss critical information and/or appearance false information. To assess this, focus was on angular resolution dMRI and a trial conducted migraine, specifically between episodic chronic migraine patients. number gradient directions had an impact...
Abstract Objective To evaluate the difference between early and delayed removal of indwelling urinary catheter after radical hysterectomy (RH) or trachelectomy (RT). Methods An ambispective study was conducted in early‐stage cervical cancer patients who underwent RH RT. Delayed occurred on a postoperative day (POD) 7 retrospective group (January 2012‐November 2013), POD 1 prospective (May 2014‐June 2017). The postvoid residual (PVR) test performed both groups. Results Our sample included 47...
Sickle cell disease (SCD) is an inherited hemoglobinopathy that causes organ dysfunction, including cerebral vasculopathy and neurological complications. Hippocampal segmentation with newer advanced 7 Tesla (7T) MRI protocols has revealed atrophy in specific subregions other neurodegenerative neuroinflammatory diseases, however, there limited evidence of hippocampal involvement SCD. Thus, we explored whether SCD may be also associated abnormalities subregions. We conducted 7T imaging...
To develop a prognostic model for women who underwent surgical treatment cervical intraepithelial neoplasia.Cohort study. Patient inclusion and follow-up occurred retrospectively prospectively.Barretos Cancer Hospital, Barretos, São Paulo, Brazil.Women (n = 242) diagnosed with neoplasia were submitted to conization.Immediately prior treatment, cytology sample was collected from each individual included in the study by endocervical brushing stored preservative solution methanol. A human...
Purpose To investigate the use of an affine‐variance noise model, with correlated quantum and spatially dependent gain, for simulation in virtual clinical trials (VCT) digital breast tomosynthesis (DBT). Methods Two distinct technologies were considered: amorphous‐selenium (a‐Se) detector direct conversion a thallium‐doped cesium iodide (CsI(Tl)) indirect conversion. A VCT framework was used to generate noise‐free projections uniform three‐dimensional simulated phantom, whose geometry...
In the last few decades, significant treatment advances have changed landscape of survival in childhood cancer.[1 2][1] However, burden treatment-related complications can persist for years. Patients exposed to radiation during are prone poor reproductive outcomes.[3][2] [
Steadily increasing use of computational/virtual phantoms in medical physics has motivated expanding development new simulation methods and data representations for modelling human anatomy. This emphasized the need increased realism, user control, availability. In breast cancer research, virtual have gained an important role evaluating optimizing imaging systems. For this paper, we developed algorithm to model abnormalities based on fractal Perlin noise. We demonstrate characterize extension...
This work proposes a new restoration method to improve mammographic images by using Anscombe transform and Wiener filter quantum noise reduction. Besides, it is performed an image enhancement inverse filter, calculated based on the system modulation transfer function (MTF). pre-processing technique were used for set of phantom in order measure number micro calcifications correctly detected computer-aided detection (CAD) algorithm. Results showed that proposed improved breast quality overcome...
Early detection of breast cancer can increase treatment efficiency. Architectural Distortion (AD) is a very subtle contraction the tissue and may represent earliest sign cancer. Since it likely to be unnoticed by radiologists, several approaches have been proposed over years but none using deep learning techniques. To train Convolutional Neural Network (CNN), which neural architecture, necessary huge amount data. overcome this problem, paper proposes data augmentation approach applied...
The main purpose of this work is to study the ability denoising algorithms reduce radiation dose in Digital Breast Tomosynthesis (DBT) examinations. Clinical use DBT normally performed "combo-mode", which, addition projections, a 2D mammogram taken with standard dose. As result, patients have been exposed doses higher than used digital mammography. Thus, efforts examinations are great interest. However, decrease leads an increased quantum noise level, and related image quality. This aimed at...