Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Alireza Talesh Jafadideh; Babak Mohammadzadeh Asl
Volume 14, Issue 2 , July 2020, , Pages 143-157
Abstract
Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors. Comparison study between ASD and typically control (TC) subjects through magnetic resonance imaging (MRI) provides valuable understanding ...
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Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by impaired social communication and restricted and repetitive behaviors. Comparison study between ASD and typically control (TC) subjects through magnetic resonance imaging (MRI) provides valuable understanding for differences in brain function. Recently, through dynamic functional connectivity (DFC) analysis, it is found that brain functional connectivity possesses dynamic nature and shows transient connectivity patterns (“states”) repeating over time. In this comparison study between ASD and TC, we employed the rest functional MRI (rfMRI) data of San Diego State University (SDSU) of ABIDE II database to examine the brain intra and inter network connectivity and also to investigate the relations of age and social responsiveness scale (SRS) score (score measuring autistic traits) to brain inter regions connectivity strength. These aims were implemented in all DFC states. The ASD subjects experienced more the state with less intra and inter network connections. Further, the DMN segregation reduction from other functional networks emerged as a common them. Furthermore, in ASD, the connection strength between auditory and visual networks was decreased by increasing the age. In ASD, the SRS had more positive relation to connectivity strength existing between cerebellar, auditory, visual networks and cognitive control network in comparison to TC. All these results demonstrate that some differences exist in brain network connection of ASD in comparison to the TC subjects and these differences can be more distinctively revealed by employing DFC analysis.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Alireza Talesh Jafadideh; Babak Mohammadzadeh Asl
Volume 10, Issue 4 , January 2017, , Pages 347-359
Abstract
Minimum variance beamformer (MVB) and its extensions are most widely used techniques in brain source localization due to their high spatial resolution. Unfortunately, beacause of using data covariance matrix, these methods often fail when the number of samples of the recorded data sequences is ...
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Minimum variance beamformer (MVB) and its extensions are most widely used techniques in brain source localization due to their high spatial resolution. Unfortunately, beacause of using data covariance matrix, these methods often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. For solving this problem, Fast Fully Adaptive (FFA) algorithm was developed a few years ago. This method is a multistage adaptive processing technique drawing its inspiration from the butterfly structure of the Fast Fourier Transform (FFT) and decreasing the data requirement significantly. Unfortunately, the high sensitivity of FFA to data partitioning sequences and also its low performance in low SNRs pose a doubt on using it as a reliable localizer for short time brain activities. In this paper, a preprocessing step is proposed to enhance the FFA method. In this step, the brain is divided into separate areas, the components of each area are determined, the data is projected to each area using components of that area. After that, FFA is applied to the projected data. The performance of the enhanced FFA is compared with FFA method by using simulated ERP and real ERF data. In all simulations, enhanced FFA shows the better performance in terms of localization error (enhancement about 2-10 mm) and spread radius (enhancement about 4-9 mm). In addition, the proposed method for real ERF data shows accurate localization result with the most concentrated power spectrum, compared to FFA approach. It is noteworthy that enhanced FFA offers less sensitivity to data partitioning sequences. Emprical results illustrate that enhanced FFA can be implemented as a reliable method for localizing brain short time activities.
Biomedical Image Processing / Medical Image Processing
Elahe Moghimirad; Ali Mahloojifar; Babak Mohammadzadeh Asl
Volume 8, Issue 3 , September 2014, , Pages 277-291
Abstract
A new implementation of a synthetic aperture focusing technique is presented in the paper. Standard medical ultrasound imaging is done using line-by-line transmission with classical Delay-and-Sum (DAS) image reconstruction. Synthetic aperture imaging, however, has a better resolution and frame rate in ...
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A new implementation of a synthetic aperture focusing technique is presented in the paper. Standard medical ultrasound imaging is done using line-by-line transmission with classical Delay-and-Sum (DAS) image reconstruction. Synthetic aperture imaging, however, has a better resolution and frame rate in cost of more computational load. To overcome this problem, block processing algorithms are used in radar and sonar which are relatively unknown in medical. To extend the methods to medical field, one should concern the parameters difference such as carrier frequency, signal band width, beam width and depth of imaging. In this paper, we extended one of these methods called wavenumber to medical ultrasound imaging with a simple model of synthetic aperture focus. We have also used chirp pulse excitation followed by matched filtering, windowing and spotlighting algorithm to compensate the effect of differences in parameters between radar and medical imaging. Computational complexity of the two reconstruction methods, wavenumber and DAS, have been calculated. Field II simulated point data has been used to evaluate the results in terms of resolution and contrast. Evaluations with simulated data show that for typical phantoms, reconstruction by wavenumber algorithm is almost 20 times faster than classical DAS while retaining the resolution.
Biomedical Image Processing / Medical Image Processing
Babak Mohammadzadeh Asl; Ali Mahloojifar
Volume 3, Issue 1 , June 2009, , Pages 33-46
Abstract
In recent years, adaptive beam forming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive beam formers. This improvement results from the fact that their weights are chosen based on the priori knowledge ...
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In recent years, adaptive beam forming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive beam formers. This improvement results from the fact that their weights are chosen based on the priori knowledge of the received data and updated using current statistics of the array signal. Most of the adaptive beam formers presented in the ultrasound imaging literature are based on the minimum variance (MV) beam former, which can improve the imaging resolution while retaining the contrast. It is desirable that the beam former could improve the resolution and contrast, at the same time. To this end, in this paper, we have used temporal averaging besides the conventional spatial averaging to estimate the more accurate covariance matrix. Moreover, we have used the coherence factor weighting combined with MV beam forming to enhance the focusing quality and hence reducing the undesired side lobes. The efficacy of the proposed adaptive beam forming approach is demonstrated via a number of simulated and experimental examples.
