Antonio Carlos da S. Senra Filho

Isotropic anomalous filtering in Diffusion-Weighted Magnetic Resonance Imaging

Noise is inherent to Diffusion-Weighted Magnetic Resonance Imaging (DWI) and noise reduction methods are necessary. Although process based on classical diffusion is one of the most used approaches for digital image, anomalous diffusion has the potential for image enhancement and it has not been tested for DWI noise reduction. This study evaluates Anomalous Diffusion (AD) filter as DWI enhancement method. The proposed method was applied to magnetic resonance diffusion weighted images (DW-MRI) with different noise levels. Results show better performance for anomalous diffusion when compared to classical diffusion approach. The proposed method has shown potential in DWI enhancement and can be an important process to improve quality in DWI for neuroimage-based diagnosis.Noise is inherent to Diffusion-Weighted Magnetic Resonance Imaging (DWI) and noise reduction methods are necessary. Although process based on classical diffusion is one of the most used approaches for digital image, anomalous diffusion has the potential for image enhancement and it has not been tested for DWI noise reduction. This study evaluates Anomalous Diffusion (AD) filter as DWI enhancement method. The proposed method was applied to magnetic resonance diffusion weighted images (DW-MRI) with different noise levels. Results show better performance for anomalous diffusion when compared to classical diffusion approach. The proposed method has shown potential in DWI enhancement and can be an important process to improve quality in DWI for neuroimage-based diagnosis.

Two-dimensional sample entropy analysis of rat sural nerve aging.

Entropy analysis of images are usually performed using Shannon entropy, which calculates the probability of occurrency of each gray level on the image. However, not only the pixel gray level but also the spatial distribution of pixels might be important for image analysis. On the other hand, sample entropy (SampEn) is an important tool for estimation of irregularity in time series, which calculates the probability of pattern occurrence within the series. Therefore, we propose here an extension of SampEn to a two-dimensional case, namely SampEn2D, as an entropy method for extracting features from images that accounts for the spatial distribution of pixels. SampEn2D was applied to histological segments of sural nerve obtained from young (30 days) and elderly (720 days) rats. Morphometric indexes, such as the total number of myelinated fibers and the average myelinated fibers area and perimeter were also calculated. Results show that SampEn2D can extract useful information from histological nerve images, classifying elderly rat image as more regular than young rat. As SampEn2D is related to irregularity/unpredictability, we can conclude that the proposed method is complementary to morphometric indexes. Further studies are being built to validate SampEn2D.

Anisotropic anomalous diffusion filtering applied to relaxation time estimation in magnetic resonance imaging.

Relaxometry mapping is a quantitative modality in magnetic resonance imaging (MRI) widely used in neuroscience studies. Despite its relevance and utility, voxel measurement of relaxation time in relaxometry MRI is compromised by noise that is inherent to MRI modality and acquisition hardware. In order to enhance signal to noise ratio (SNR) and quality of relaxometry mapping we propose application of anisotropic anomalous diffusion (AAD) filter that is consistent with inhomogeneous complex media. Here we evaluated AAD filter in comparison to two usual spatial filters: Gaussian and non local means (NLM) filters applied to real and simulated T2 relaxometry image sequences. The results demonstrate that AAD filter is comparatively more efficient in noise reducing and maintaining the image structural edges. AAD shows to be a robust and reliable spatial filter for brain image relaxometry.

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods: A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results: The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions: In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.

Populational brain models of diffusion tensor imaging for statistical analysis: a complementary information in common space

Abstract Introduction : The search for human brain templates has been progressing in the past decades and in order to understand disease patterns a need for a standard diffusion tensor imaging (DTI) dataset was raised. For this purposes, some DTI templates were developed which assist group analysis studies. In this study, complementary information to the most commonly used DTI template is proposed in order to offer a patient-specific statistical analysis on diffusion-weighted data. Methods : 131 normal subjects were used to reconstruct a population-averaged template. After image pre processing, reconstruction and diagonalization, the eigenvalues and eigenvectors were used to reconstruct the quantitative DTI maps, namely fractional anisotropy (FA), mean diffusivity (MD), relative anisotropy (RA), and radial diffusivity (RD). The mean absolute error (MAE) was calculated using a voxel-wise procedure, which informs the global error regarding the mean intensity value for each quantitative map.

Brain activation inhomogeneity highlighted by the Isotropic Anomalous Diffusion filter.

The visual appealing nature of the now popular BOLD fMRI may give the false impression of extreme simplicity, as if the the functional maps could be generated with the press of a single button. However, one can only get plausible maps after long and cautious processing, considering that time and noise come into play during acquisition. One of the most popular ways to account for noise and individual variability in fMRI is the use of a Gaussian spatial filter. Although very robust, this filter may introduce excessive blurring, given the strong dependence of results on the central voxel value. Here, we propose the use of the Isotropic Anomalous Diffusion (IAD) approach, aiming to reduce excessive homogenity while retaining the natural variability of signal across brain space. We found differences between Gaussian and IAD filters in two parameters gathered from Independent Component maps (ICA), identified on brain areas responsible for auditory processing during rest. Analysis of data gathered from 7 control subjects shows that the IAD filter rendered more localized active areas and higher contrast-to-noise ratios, when compared to equivalent Gaussian filtered data (Student t-test, p<;0.05). The results seem promising, since the anomalous filter performs satisfactorily in filtering noise with less distortion of individual localized brain responses.

A computational tool as support in B-mode ultrasound diagnostic quality control

INTRODUCTION: The quality control (QC) of biomedical equipment is a very important process for the quality assurance of the instruments used in diagnoses and treatments. Ultrasound diagnostic imaging is one of the most widely used techniques for diagnostic imaging in hospitals and medical clinics. However, the time required to complete several B-mode imaging QC tests in ultrasound equipment is very critical for a hospital with a high number of exams. Here, we present a computational tool to assist in the acquisition and storage of data from multiple QC tests in B-mode ultrasound diagnostic equipment to promote an efficient alternative for QC in clinical routines. METHODS: The project was planned and implemented in C++ programming language and compiled for two computing platforms: Windows and Linux. The most common QC routine tests for B-mode ultrasound were combined in a simple graphical user interface. RESULTS: After entering all of the correct QC information in the graphical user interface, a final report in PDF format was created. CONCLUSION: The proposed program has been helpful for students and diagnostic professionals and is a quick and easy application for several QC tests for B-mode ultrasound diagnostic equipment. Our program seeks to help in the dissemination and application of QC tests for B-mode ultrasound equipment in hospitals and clinics and for the technical training of ultrasound professionals.

An artificial neural network model as a tool to identify the anaerobic threshold during dynamic physical exercise

Anaerobic threshold is one of the most important parameters used in exercise physiology. It signals a power value during dynamic physical exercise where anaerobic energy formation for muscle contraction is added to the aerobic counterpart-what allows the quantification of aerobic capacity. In this study, we describe the development and validation of an artificial neural network model to identify anaerobic threshold based on electrocardiogram R-R interval time series collected during physical exercise tests applied in healthy subjects. The results showed that the artificial neural network had its best performance in gradual increasing power. Scatter plot and ROC curve was constructed showing high correlation (r = 0.93), and good accuracy (area under the ROC curve = 0.9851) when compared to autoregressive integrated moving average (ARIMA) statistical method.