Mohammad Alipoor

Particle Swarm Optimization based design of disturbance rejection PID controllers for time delay systems

In this paper a new method to design PID controllers for time delay systems is presented. Particle Swarm Optimization (PSO) technique is used to obtain optimal parameters of a two-degree-of-freedom (2-DOF) PID controller. Set point tracking is an objective that is to be achieved in presence of disturbance. At first a PD controller as a disturbance rejection controller is designed, and then in the outer loop the main PID controller for tracking the input signal is placed. Since disturbance rejection and set point tracking should be satisfied alongside each other, some considerations are taken into account to choose the most appropriate controller. Using this method, a better response can be achieved in comparison with genetic algorithm.

Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi‐group comparison

A large number of mathematical models have been proposed to describe the measured signal in diffusion‐weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the ‘White Matter Modeling Challenge’ during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three‐quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion‐based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non‐Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal‐predicting strategies, such as bootstrapping or cross‐validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.

Icosahedral gradient encoding scheme for an arbitrary number of measurements

The icosahedral gradient encoding scheme (GES) is widely used in diffusion MRI community due to its uniformly distributed orientations and rotationally invariant condition number. The major drawback with this scheme is that it is not available for arbitrary number of measurements. In this paper (i) we propose an algorithm to find the icosahedral scheme for any number of measurements. Performance of the obtained GES is evaluated and compared with that of Jones and traditional icosahedral schemes in terms of condition number, standard deviation of the estimated fractional anisotropy and distribution of diffusion sensitizing directions; and (ii) we introduce minimum eigenvalue of the information matrix as a new optimality metric to replace condition number. Unlike condition number, it is proportional to the number of measurements and thus in agreement with the intuition that more measurements leads to more robust tensor estimation. Furthermore, it may independently be maximized to design GESs for different diffusion imaging techniques.

Determinant of the information matrix: A new rotation invariant optimality metric to design gradient encoding schemes

Minimum condition number (CN) gradient encoding scheme was introduced to diffusion MRI community more than a decade ago. Its computation requires tedious numerical optimization which usually leads to sub-optimal solutions. The CN does not reflect any benefits in acquiring more measurements, i.e. its optimal value is constant for any number of measurements. Further, it is variable under rotation. In this paper we (i) propose an accurate method to compute minimum condition number scheme; and (ii) introduce determinant of the information matrix (DIM) as a new opti-mality metric that scales with number of measurements and does reflect what one would gain from acquiring more measurements. Theoretical analysis shows that DIM is rotation invariant. Evaluations on state-of-the-art encoding schemes proves the relevance and superiority of the proposed metric compared to condition number.

On high order tensor-based diffusivity profile estimation

Diffusion weighted magnetic resonance imaging (dMRI) is used to measure, in vivo, the self-diffusion of water molecules in biological tissues. High order tensors (HOTs) are used to model the apparent diffusion coefficient (ADC) profile at each voxel from the dMRI data. In this paper we propose: (i) A new method for estimating HOTs from dMRI data based on weighted least squares (WLS) optimization; and (ii) A new expression for computing the fractional anisotropy from a HOT that does not suffer from singularities and spurious zeros. We also present an empirical evaluation of the proposed method relative to the two existing methods based on both synthetic and real human brain dMRI data. The results show that the proposed method yields more accurate estimation than the competing methods.

Optimal Experiment Design for Monoexponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging

The monoexponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics. The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for monoexponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (D-optimal design). In contrast to previous methods, D-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, and range of b-values). Using Monte Carlo simulations we show that the D-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.

K-Optimal Gradient Encoding Scheme for Fourth-Order Tensor-Based Diffusion Profile Imaging

The design of an optimal gradient encoding scheme (GES) is a fundamental problem in diffusion MRI. It is well studied for the case of second-order tensor imaging (Gaussian diffusion). However, it has not been investigated for the wide range of non-Gaussian diffusion models. The optimal GES is the one that minimizes the variance of the estimated parameters. Such a GES can be realized by minimizing the condition number of the design matrix (K-optimal design). In this paper, we propose a new approach to solve the K-optimal GES design problem for fourth-order tensor-based diffusion profile imaging. The problem is a nonconvex experiment design problem. Using convex relaxation, we reformulate it as a tractable semidefinite programming problem. Solving this problem leads to several theoretical properties of K-optimal design: (i) the odd moments of the K-optimal design must be zero; (ii) the even moments of the K-optimal design are proportional to the total number of measurements; (iii) the K-optimal design is not unique, in general; and (iv) the proposed method can be used to compute the K-optimal design for an arbitrary number of measurements. Our Monte Carlo simulations support the theoretical results and show that, in comparison with existing designs, the K-optimal design leads to the minimum signal deviation.

Optimal diffusion tensor imaging with repeated measurements.

Several data acquisition schemes for diffusion MRI have been proposed and explored to date for the reconstruction of the 2nd order tensor. Our main contributions in this paper are: (i) the definition of a new class of sampling schemes based on repeated measurements in every sampling point; (ii) two novel schemes belonging to this class; and (iii) a new reconstruction framework for the second scheme. We also present an evaluation, based on Monte Carlo computer simulations, of the performances of these schemes relative to known optimal sampling schemes for both 2nd and 4th order tensors. The results demonstrate that tensor estimation by the proposed sampling schemes and estimation framework is more accurate and robust.

Designing edge detection filters using Particle Swarm Optimization

This paper presents a novel edge detection method based on Particle Swarm Optimization. Unlike classical filters that are set by intuitive knowledge, a new filter is proposed on the basis of evolutionary computation. A proper synthetic training image and its edge map are used to find an optimum edge filter. The advantage of this method is that an effective edge detection filter can be easily constructed. Provided results certify that our proposed method outperforms commonly used edge detection algorithms.

A novel biomarker discovery method on protemic data for ovarian cancer classification

In this paper a novel combinational feature selection method on high throughput SELDI-TOF mass-spectroscopy data for ovarian cancer classification is developed. The proposed method includes 3 steps: dataset normalization, dimensionality reduction using feature filtering, selecting the most informative features utilizing binary particle swarm optimization. Indeed, the method employs a combination of filter and wrapper feature selection methods to find features with high discriminatory power. The algorithm is successfully validated using a well-known ovarian cancer proteomic dataset. Results of applying the method are superior to state of the art methods in proteomic pattern recognition. It reduces extremely high dimensionality of proteomic data to 3 dimensional and linearly separable data. Therefore, proposed system clearly outperforms previous works in both respects of accuracy and number of required features; witch may lead in high accuracy and high speed diagnosis procedure.