Marcelo A. C. Vieira

Pulmonary vein stenosis complicating catheter ablation of focal atrial fibrillation.

Pulmonary Vein Stenosis. Introduction: A recently described focal origin of atrial fibrillation, mainly inside pulmonary veins, is creating new perspectives for radiofrequency catheter ablation. However, pulmonary venous stenosis may occur with uncertain clinical consequences. This report describes a veno‐occlusive syndrome secondary to left pulmonary vein stenosis after radiofrequency catheter ablation.

Associative memory used for trajectory generation and inverse kinematics problem

Proposes a neural network system to perform trajectory generation and inverse kinematics. Such a system is composed of two neural network blocks based on associative memory principles. The first block is formed by the model called temporal multidirectional associative memory (TMAM). This block is responsible for producing a desired spatial trajectory given part of it. The second block includes a radial basis function (RBF) model that provides a set of joint angles associated with the trajectory. The system has a fast training stage, is able to interpolate and extrapolate points to a trained trajectory is able to deal with multiple trajectories, and is able to produce viable joint angles even if the spatial position slightly violates the robot constraints. So far, the RBF model was tested only for single trajectories.

Filtering of poisson noise in digital mammography using local statistics and adaptive wiener filter

A novel image denoising algorithm has been proposed for quantum noise reduction in digital mammography. The method uses the Anscombe transformation to stabilize noise variance and convert the signal-dependent Poisson noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is removed through an adaptive Wiener filter, whose parameters are obtained considering local image statistics. Thus, the method does not require any a priori knowledge about the original signal, because all the necessary parameters are estimated directly from the noisy image. The method was applied on synthetic mammograms generated based upon an anthropomorphic software breast phantom with different levels of simulated quantum noise. The evaluation of the proposed method was performed by calculating the peak signal-to-noise ratio (PSNR) and the mean structural similarity index (MSSIM) before and after denoising. Results show that the proposed algorithm improves image quality by reducing image noise without significantly affecting image sharpness.

Metformin, but not glimepiride, improves carotid artery diameter and blood flow in patients with type 2 diabetes mellitus

OBJECTIVE: To compare the effects of glimepiride and metformin on vascular reactivity, hemostatic factors and glucose and lipid profiles in patients with type 2 diabetes. METHODS: A prospective study was performed in 16 uncontrolled patients with diabetes previously treated with dietary intervention. The participants were randomized into metformin or glimepiride therapy groups. After four months, the patients were crossed over with no washout period to the alternative treatment for an additional four-month period on similar dosage schedules. The following variables were assessed before and after four months of each treatment: 1) fasting glycemia, insulin, catecholamines, lipid profiles and HbA1 levels; 2) t-PA and PAI-1 (antigen and activity), platelet aggregation and fibrinogen and plasminogen levels; and 3) the flow indices of the carotid and brachial arteries. In addition, at the end of each period, a 12-hour metabolic profile was obtained after fasting and every 2 hours thereafter. RESULTS: Both therapies resulted in similar decreases in fasting glucose, triglyceride and norepinephrine levels, and they increased the fibrinolytic factor plasminogen but decreased t-PA activity. Metformin caused lower insulin and pro-insulin levels and higher glucagon levels and increased systolic carotid diameter and blood flow. Neither metformin nor glimepiride affected endothelial-dependent or endothelial-independent vasodilation of the brachial artery. CONCLUSIONS: Glimepiride and metformin were effective in improving glucose and lipid profiles and norepinephrine levels. Metformin afforded more protection against macrovascular diabetes complications, increased systolic carotid artery diameter and total and systolic blood flow, and decreased insulin levels. As both therapies increased plasminogen levels but reduced t-PA activity, a coagulation process was likely still ongoing.

Mammography Images Restoration by Quantum Noise Reduction and Inverse MTF Filtering

This work proposes a new restoration method to improve mammographic images by using Anscombe Transform and Wiener Filter to quantum noise reduction. Besides, it is performed an image enhancement by using a restoration inverse filter, calculated based on the image system modulation transfer function (MTF). This pre-processing technique were used for a set of mammographic phantom images in order measure the number of micro calcifications correctly detected by a computer-aided detection (CAD) algorithm. Results showed that the proposed method improved breast images quality by overcome the acquisition process constrains and reducing noise.The performance of the breast microcalcification CAD was improved when using the restored images set in comparison to the original one.

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

Purpose: This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose. Methods: The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose. Results: The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%. Conclusions: A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

Effect of denoising on the quality of reconstructed images in digital breast tomosynthesis

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 of a denoising algorithm and the Anscombe transformation on the reduction of quantum noise in DBT images. The Anscombe transformation is a variance-stabilizing transformation that converts the signal-dependent quantum noise to an approximately signalindependent Gaussian additive noise. Thus, this transformation allows for the use of conventional denoising algorithms, designed for additive Gaussian noise, on the reduction of quantum noise, by working on the image in the Anscombe domain. In this work, denoising was performed by an adaptive Wiener filter, previously developed for 2D mammography, which was applied to a set of synthetic DBT images generated using a 3D anthropomorphic software breast phantom. Ideal images without noise were also generated in order to provide a ground-truth reference. Denoising was applied separately to DBT projections and to the reconstructed slices. The relative improvement in image quality was assessed using objective image quality metrics, such as peak signal-to-noise ratio (PSNR) and mean structural similarity index (SSIM). Results suggest that denoising works better for tomosynthesis when using the Anscombe transformation and when denoising was applied to each projection image before reconstruction; in this case, an average increase of 9.1 dB in PSNR and 58.3% in SSIM measurements was observed. No significant improvement was observed by using the Anscombe transformation when denoising was applied to reconstructed images, suggesting that the reconstruction algorithm modifies the noise properties of the DBT images.

Reproducibility of pixel values for two photostimulable phosphor plates in consecutive standardized scannings

The objective of the present study was to determine the reproducibility of the pixel values obtained with the Digora system (Soredex, Finland). Exposures were standardized, with variation in exposure and scanning time of two photostimulable phosphor plates containing a stepwedge image. The smallest variation in pixel values ranged from 50 to 75%, with the widest variations being observed in less dense steps. A significant difference in pixel values was observed in terms of X-ray exposure and scanning times and between the two plates themselves (ANOVA, p < 0.01). Using the present methodology, the reproducibility of pixel values was not satisfactory for the tested white photostimulable plates. This wide variation in digitalization might be influenced by the amount of X-rays that sensitized the plates. It may be important to establish the reproducibility of the pixel values in quantitative studies using digital image.

Use of a film scanner as a microdensitometer for optical transfer function and focal spot measurements

This work presents a computational model for practical application of the transfer function method for radiographic units evaluation, in order to reduce some experimental constraints involved to its determination. With the proposed algorithm, the complete Optical Transfer Function (Modulation Transfer Function and Phase Transfer Function) can be easily determined as well as the effective focal spot sizes at any field location without using a microdensitometer. All measurements are done from a digitized slit image obtained experimentally at the field center position. The effective focal spot sizes can be calculated by using the Line Spread Function Root Mean Square (RMS) value or by the Modulation Transfer Function (MTF) first minimum. Besides, considering the variation of the effective focal spot size given by the field characteristic equations, all these parameters can also be determined at any location on the radiation field. The computer scheme was used for evaluating slit images obtained from nine different x-ray equipments. Results confirmed the possibility of using the transfer function method for quality evaluation of any radiological system in a simple and automatic way. This computer scheme replaces some of the expensive and specific devices necessary to the experimental MTF evaluation by quite more accessible and low cost equipments.

Pipeline for effective denoising of digital mammography and digital breast tomosynthesis

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 of the underlying noise model. In full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT), calibration steps like the detector offset and flat-fielding can affect some assumptions made by most denoising techniques. Furthermore, quantum noise found in X-ray images is signal-dependent and can only be treated by specific filters. In this work we propose a pipeline for FFDM and DBT image denoising that considers the calibration steps and simplifies the modeling of the noise statistics through variance-stabilizing transformations (VST). The performance of a state-of-the-art denoising method was tested with and without the proposed pipeline. To evaluate the method, objective metrics such as the normalized root mean square error (N-RMSE), noise power spectrum, modulation transfer function (MTF) and the frequency signal-to-noise ratio (SNR) were analyzed. Preliminary tests show that the pipeline improves denoising. When the pipeline is not used, bright pixels of the denoised image are under-filtered and dark pixels are over-smoothed due to the assumption of a signal-independent Gaussian model. The pipeline improved denoising up to 20% in terms of spatial N-RMSE and up to 15% in terms of frequency SNR. Besides improving the denoising, the pipeline does not increase signal smoothing significantly, as shown by the MTF. Thus, the proposed pipeline can be used with state-of-the-art denoising techniques to improve the quality of DBT and FFDM images.