Computational analysis of non-invasive deep brain stimulation based on interfering electric fields.

Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive deep brain stimulation (NDBS) via temporally interfering electric fields has emerged recently as a non-invasive strategy for electrically stimulating deep regions in the brain. The objective of this study is to provide insight into the fundamental mechanisms of this strategy and assess the potential uses of this method through computational analysis. Analytical and numerical methods are used to compute the electric potential and field distributions generated during NDBS in homogeneous and inhomogeneous models of the brain. The computational results are used for specifying the activated area in the brain (macroscopic approach), and quantifying its relationships to the stimulation parameters. Two automatic algorithms, using artificial neural network (ANN), are developed for the homogeneous model with two and four electrode pairs to estimate stimulation parameters. Additionally, the extracellular potentials are coupled to the compartmental axon cable model to determine the responses of the neurons to the modulated electric field in two developed models and to evaluate the precise activated area location (microscopic approach). Our results show that although the shape of the activated area was different in macroscopic and microscopic approaches, it located only at depth. Our optimization algorithms showed significant accuracy in estimating stimulation parameters. Moreover, it demonstrated that the more the electrode pairs, the more controllable the activated area. Finally, compartmental axon cable modeling results verified that neurons can demodulate and follow the electric field modulation envelope amplitude (MEA) in our models. The results of this study help develop the NDBS method and eliminate some limitations associated with the nonautomated optimization algorithm.

Nonrigid image registration in digital subtraction angiography using multilevel B-spline.

We address the problem of motion artifact reduction in digital subtraction angiography (DSA) using image registration techniques. Most of registration algorithms proposed for application in DSA, have been designed for peripheral and cerebral angiography images in which we mainly deal with global rigid motions. These algorithms did not yield good results when applied to coronary angiography images because of complex nonrigid motions that exist in this type of angiography images. Multiresolution and iterative algorithms are proposed to cope with this problem, but these algorithms are associated with high computational cost which makes them not acceptable for real-time clinical applications. In this paper we propose a nonrigid image registration algorithm for coronary angiography images that is significantly faster than multiresolution and iterative blocking methods and outperforms competing algorithms evaluated on the same data sets. This algorithm is based on a sparse set of matched feature point pairs and the elastic registration is performed by means of multilevel B-spline image warping. Experimental results with several clinical data sets demonstrate the effectiveness of our approach.

Calculation of Computational Complexity for Radix-2 (p) Fast Fourier Transform Algorithms for Medical Signals.

Owing to its simplicity radix-2 is a popular algorithm to implement fast fourier transform. Radix-2(p) algorithms have the same order of computational complexity as higher radices algorithms, but still retain the simplicity of radix-2. By defining a new concept, twiddle factor template, in this paper, we propose a method for exact calculation of multiplicative complexity for radix-2(p) algorithms. The methodology is described for radix-2, radix-2 (2) and radix-2 (3) algorithms. Results show that radix-2 (2) and radix-2 (3) have significantly less computational complexity compared with radix-2. Another interesting result is that while the number of complex multiplications in radix-2 (3) algorithm is slightly more than radix-2 (2), the number of real multiplications for radix-2 (3) is less than radix-2 (2). This is because of the twiddle factors in the form of which need less number of real multiplications and are more frequent in radix-2 (3) algorithm.

A promising method of enhancement for early detection of ischemic stroke.

BACKGROUND: Computed Tomography (CT) scan without contrast is the modality of choice for diagnosis of stroke. However, routine brain CT scan, with linear processing has some limitations in early diagnosis of ischemic stroke. The aim of this study was to evaluate and compare the sensitivity and specificity of processed CT images with conventional ones in early diagnosis of cerebrovascular attack (CVA). PATIENTS AND METHODS: This was a self-controlled study conducted in a university referal hospital from 2010 to 2011. Seventy CT scans underwent a process using Laplacian Pyramid transform. Thirty five of participants were diagnosed with CVA while others had only headache and no ischemic stroke diagnosis based on the first and follow-up CT scans. A neuroradiologist made diagnosis with and without the help of processed CT scans. The McNemar and Wilcoxon analysis were used to compare the sensitivity, specificity, positive and negative predictive values of two methods. RESULTS: The sensitivity (% 65.7 vs. %31.4, P value = 0.001), positive predictive value (% 85.2 vs. % 61, P value = 0.03) and negative predictive value (% 73.9% vs. %49, P value = 0.01) of the processed method were significantly higher than the routine one, while no difference was seen in specificity (% 88.6 vs. %77.1, P value = 0.15). Moreover, the accuracy of the processed method was significantly better than the linear one (P value < 0.001). CONCLUSIONS: It was concluded that nonlinear modified Laplacian Pyramid method can composed CT scans which can be more helpful in early detection of ischemic stroke.