Comparative analysis of various brain imaging techniques

Abstract

The bio-imaging techniques have widespread applications from diagnosing diseases to investigating the body tissues at the cells level. Traditionally, these techniques were used mainly in the orthopedic treatment. However, with the development of infrared cameras, ultrasound, and radio wave technology, they are used in different medical fields such as cardiovascular analysis, neurological treatment and infant care. This paper reviews the common bio-imaging techniques used in the brain imaging and compares them based on resolution, contrast, biological risks involved, and price.

A Cognitive-Semiotic Approach to Agency: Assessing Ideas from Cognitive Science and Neuroscience

“…The Technology, its Problems and Potentials Cognitive neuroscience has grown exponentially in the past two decades, largely due to the invention of a diversity of advanced techniques for brain imaging like computer tomography, positron emission tomography, ultrasound, and magnetic resonance imaging (Agnihotri et al, 2010;Bradshaw, 1989). Unlike the others, however, functional magnetic resonance imaging (fMRI) is commonly applied in studies that are not part of clinical research and the technique most widely used in the neuroscience of agency (David, 2012).…”

Time frequency analysis of functional MR images

“…Air, which has very low x-ray attenuation coefficient, appears black in the image; Bone, because of its high attenuation coefficient appears white and soft tissue appears somewhere in between. Radiation dosages are generally high in case of CT scan, hence they are not recommended for children and pregnant women [1,98]. For patients with implants (e.g., intracranial aneurysm clip) who are not allowed to enter the MR (Magnetic Resonance) room, CT scan is the best imaging alternative [98].…”

“…Temporal autocorrelation in voxel time-series has been modeled earlier with different types of models e.g. autoregressive (AR) models such as AR(1) model, AR (1) model with additional white noise component, AR(p) model and autoregressive moving average (ARMA) model in time-domain [16,62,64,85,112,115] and 1/f noise model in frequency domain [120]. In a recent study on resting state fMRI datasets, simpler models, such as AR(1) model, have been found to be inadequate in modeling the temporal autocorrelation structure present in voxel time-series [31].…”

“…is used to find time-series related to the spatial components that match the target In yet another application, we use CRF principle for efficient regularization of AR (1) values in model-based pre-whitening methods. In this method, residuals of voxel time-series after regression analysis, are fitted with an AR(1) model to calculate autocorrelation coefficients.…”

“…shows the activation detection results for AR(1) and AR(2) datasets, for MSA method (with and without CRF), and also the corresponding results of GLM method. The true image is also shown for comparison.…”