nils-herrmann/clean_scirepeval huggingface dataset:
Particle filtering of point processes observation with application on the modeling of visual cortex neural spiking activity

Recording of neural response to specific stimulus in a repeated trial is very common in neuroscience protocol. The perstimulus time histogram (PSTH) is a standard tool for analysis of neural response. However it could not capture the non-deterministic properties of the neuron especially in higher level cortical area such as inferior temporal cortex. The stochastic state point process filter theory is used for the estimation of the conditional intensity of the point process observation as a time varying firing rate and the particle filter is used to numerically estimate this density in time. The particle filters were applied to the results of the point process observation for compensating the Gaussian assumption. The results of applying point process modeling on a real data from inferior temporal cortex of macaque monkey indicates that, based on the assessment of goodness-of-fit, the neural spiking activity and biophysical property of neuron could be captured more accurately in compare to conventional methods.
Predicting final extent of ischemic infarction using an artificial neural network analysis of multiparametric MRI in patients with stroke

In ischemic stroke, the extent of ischemic lesion recovery is one of the most important correlate of functional recovery in brain. Using a set of acute phase MR images (Diffusion-Weighted - DWI, T 1 -Weighted - T1WI, T 2 -Weighted T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a pixel-by-pixel forecast of the chronic T2WI. The ANN was trained and tested using 14 slices from 3 subjects using a K-Folding Cross-Validation (KFCV) method with 14 folds. The Area Under the Receiver Operator Characteristic Curve (AUROC) for 14 folds was used for training, testing and optimization of the ANN. After training and optimization, the ANN produced a map that was well correlated (r = 0.88, p ≪ 0.0001) with the T2WI at 3 months. To confirm that the trained ANN performed well against a new dataset, 13 slices from 4 other patients were shown to the trained ANN. The prediction made by the ANN had an excellent overall performance (AUROC = 0.82), and was very well correlated to the 3-month ischemic lesion on T2-Weighted image.
Segmentation of pelvic bone in STIR MRI with active contour

Intensity inhomogeneities often occur in STIR Sequence MR images. For this reason, segmentation of these images is challenging. In STIR images from pelvis, signal of bone marrow is similar to subcutaneous fat and adjacent muscular structures so bone marrow pathologic lesions could be hidden. In this paper, we propose a method for image segmentation of pelvic bones in STIR MR images. This method is an extension of active contour methods. In this method, radius of localization is adaptively determined for each point of the contour. We combine this local structure with RSF model and we use the edge information to reach an appropriate segmentation. The proposed model outperforms RSF model in segmenting the images that have intensity inhomogeneities. We apply our algorithm on more than 120 MRI slices of 6 subjects to show its accuracy. Finally, we compare the result of the proposed model with those of two other active contours models.
Voxel-based treatment prediction of glioblastoma multiform tumor using diffusion tensor imaging

This paper proposes a method to predict the effect of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The prediction is critical for effective treatment planning. The proposed method is developed and evaluated using Diffusion Tensor Imaging (DTI) and post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI) of 14 patients with GBM tumors gathered before and after the treatment. First, the proposed method calculates diffusion anisotropy indices (DAI) of all voxels in the brain. These diffusion anisotropy indices are Fractional Anisotropy (FA), Mean Diffusivity (MD), Relative Anisotropy (RA), and Volume Ratio (VR). Then, it registers post-treatment pc-T1-MRI and pre-treatment DAI maps to pre-treatment pc-T1-MRI. Next, it uses a thresholding method to segment the tumor from pc-T1-MRI studies. Comparing Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI, the DAIs of the tumor are labeled based on their response to the treatment. The voxels of 7 patients are randomly selected to train 4 classifiers (ANN, SVM, KNN, and ANFIS) and then all voxels of the other 7 patients are used to test them. For each classifier, four performance measures (sensitivity, specificity, positive predictive value, and accuracy) are calculated. Experimental results show that the ANFIS is more accurate than the other classifiers in predicting the treatment response.
MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.
A graph theoretical analysis of brain functional network in temporal lobe epilepsy patients

The temporal lobe epilepsy (TLE) is the most common type of sympotmatic epilepsies. However, the underlying neural cause of this disorder is not well understood yet. In this study we aimed to investigate functional brain network constructed using resting state functional Magnetic Resonance Imaging (rs-fMRI) from a graph theoretical approach with specific focus on local network measures. Moreover, we have analazed three network measures of local connectivity asymmetry, local hemispheric connectivity ratio and nodal connectivity distribution skewness in the functional brain network. To this end we recruited 10 TLE patients and 10 healthy controls. Our results showed increased segregation and a shift towards reguralized networks in the TLE patients. Furthermore we detected decreased connectivity in the temporal lobe including, the bilateral Hippocampus and left Parahippocampal gyrus. Finally, we found increased asymmetry between the left and right Inferior Temporal Cortices (FDR corrected), left and right Hippocampus and left and right Heschle gyri.
Cardiac Cine MRI using Compressive Sensing principles

MR images can be reconstructed from undersampled k-t space data to increase image acquisition speed. We propose a new method to undersample the k-space and reconstruct images based on Compressive Sensing (CS) theory. To this end, statistical features extracted from each trajectory are clustered by the fuzzy c-means (FCM) method. The resulting class labels are considered as the states of a Markov chain. A hidden Markov model (HMM) is then trained to find the transition matrix. Trajectories having more non-diagonal transition matrices are chosen to sample data along them. An iterative thresholding algorithm is then used for reconstruction of the image. The proposed method outperforms two other methods in reconstructing half sampled Cardiac Cine MRI data. The use of fuzzy clustering as an intermediate tool to study complicated phenomena by HMM, applicability to non-dynamic MRI data, robustness to noise, faster and more accurate reconstruction describe specifications of the proposed method.
Error propagation in eigenimage filtering.

Mathematical derivation of error (noise) propagation in eigenimage filtering is presented. Based on the mathematical expressions, a method for decreasing the propagated noise given a sequence of images is suggested. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the final composite image are compared to the SNRs and CNRs of the images in the sequence. The consistency of the assumptions and accuracy of the mathematical expressions are investigated using sequences of simulated and real magnetic resonance (MR) images of an agarose phantom and a human brain.
Application of DTI connectivity in lateralization of mTLE

Diffusion tensor imaging (DTI) is a noninvasive imaging method for measuring the diffusion properties of the underlying white matter tracts through which epileptiform activity is propagated. This study investigates the structural abnormalities quantified by DTI in mesial temporal lobe epilepsy (mTLE). Fiber tracts passing through 54 anatomical sites in 12 adult mTLE patients and 12 age- and gender-matched controls were identified using DTI tractography. DTI nodal degree (ND) and laterality index were then calculated. ND laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in gyrus rectus, insular cortex, precuneus and superior temporal gyrus (p<;0.025). None of these anatomical sites showed statistically significant differences in ND laterality between right and left sides of the controls. Laterality models determined by logistic regression on the ND laterality data agreed with the side of epileptogenicity as it pertained to the gyrus rectus, insular cortex, precuneus and superior temporal gyrus for 89%, 72%, 83% and 92% of the patients, respectively. Combining the laterality measures in these four anatomical sites improved the results further with correct lateralization of 100% for all patients. The proposed methodology for using DTI connectivity to investigate diffusion abnormalities related to focal epileptogenicity and propagation can provide a further means of noninvasive lateralization.
Comparison of two linear models for estimating brain deformation during surgery using finite element method

This paper presents finite element computation of brain deformation during craniotomy. Two mechanical models are compared for this purpose: linear solid-mechanic model and linear elastic model. Both models assume finite deformation of the brain after opening the skull. We use a test sphere as a model of the brain, tetrahedral finite element mesh, and function optimization that optimizes the modelspsila parameters by minimizing the distance between the resulting deformation and the supposed deformation. Based on the final value of the objective function, we conclude that the accuracy of the solid mechanic model is higher than that of the elastic model. Applications of the methods to the MR images of the brain confirm this finding.
A novel method for identification of COPD in inspiratory and expiratory states of CT images

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system.
Spatio-temporal localization of focal epileptic sources from intracranial electrocortical recordings using an independent component analysis (ICA) algorithm

The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being able to localize deep brain epileptic sources. As a means to improve the localization of epileptic activities from these subdural electrical recordings, we developed a simple source monitoring system to detect and localize these deep sources. The method is based on an independent component analysis (ICA) algorithm known as joint approximate diagonalization of eigen-matrices (JADE). This method was tested on patients with neocortical epileptic foci focusing on the interictal spikes from ECoG signals. The proposed method localizes the interictal epileptic discharges to both superficial as well as deep regions of the human neocortex and has the potential to improve the localization and surgical outcomes of patients with medically refractory epilepsy undergoing surgical resections.
Voxel-Based Morphometric Study of Brain Regions from Magnetic Resonance Images in Temporal Lobe Epilepsy

The aim of this study was to analyze the brain structures in temporal lobe epilepsy (TLE) using 3D T1-weighed magnetic resonance images (MRI). We used a method of voxel-based morphometry (VBM) with the unified segmentation framework to examine the effects of TLE on specific brain structures in 22 patients. Also, 18 healthy control subjects were included in this study for group comparison. We accomplished GM segmentation, spatial normalization, and intensity non-uniformity correction in the same model. Besides, removal of nonbrain voxels and compensation of spatial normalization effects were performed in this study. Student's t-test statistical models of differences between gray matter concentration (GMC) of patients and controls were obtained using a general linear model (GLM). Compared to controls, TLE presented GMC reduction in hippocampus, amygdala, and entorhinal cortex. Comparison of abnormal areas detected by VBM method and manual delineation of these structures confirms the obtained results.
Automated contour extraction using a multi-scale approach

Many image registration methods use surface of head or brain to estimate rotation and translation parameters. The surface is usually characterized by a set of edge or contour points extracted from cross-sectional images. Automatic extraction of contour points is complicated by discontinuity of edges in the back of eyes and ears and sometimes by an inadequate field of view. The authors have developed an automated method for contour extraction that connects discontinuities using a multi-scale pyramid. Steps of the method are: (1) Contour points are found by an edge-tracking algorithm; (2) A multi-scale pyramid of contour points is constructed; (3) Contour points of reduced images are found; (4) From the continuous contour found at the lowest scale, contour points at a higher scale are found; (5) Step 4 is repeated until contour points at the highest resolution (original image) are found. The method runs fast and has successfully been used for MRI and CT image registration. The authors illustrate the method and its performance using MRI and CT images of the human brain.<<ETX>>
SPHARM-based shape analysis of hippocampus for lateralization in mesial temporal lobe epilepsy

Spherical harmonics (SPHARM) is a powerful tool for modelling and processing of 3D connected objects of any shape. SPHARM popularity lies in its capability of revealing surfaces global discrepancies in a multi-scale manner. In medical image analysis, this capability is of great importance in diagnosis of diseases that are related to deformations in the brain structures, such as mesial temporal lobe epilepsy that is associated with the hippocampus deformation. In this paper, we present a simple and practical method for SPHARM registration, which is required for conducting shape comparisons. The method utilizes concepts of principal components and solves the challenging problem of SPHARM registration. Our method benefits from characteristics of SPHARM coefficients that have independent [x,y,z] elements; so registration is easily performed in the SPHARM feature space. Then, we propose our feature selection methods that summarize 1536 SPHARM-based features of each subject into three lateralization indices. These three indices measure the distances between left and right hippocampi of healthy and epileptic subjects to detect the epileptogenic hippocampus. This work improves the lateralization accuracy from 78% of conventional volumetric method to 85%, and also in cases where volumetric analysis is uncertain, 16% improvement is achieved. This method could be used as a compliment to other methods to decrease lateralization error.
Universal Steganalysis Based on Local Prediction Error in Wavelet Domain

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes.
Lateralization and prognosis of temporal lobe epilepsy patients by shape analysis of hippocampus via signed poisson mapping

Shape analysis of three-dimensional (3D) brain structures has facilitated the study of discrepancies between patients suffering from neurological diseases and normal subjects. Not only does shape analysis help physicians in qualitative assessments but also can be used to build a computer assisted model based on the quantification results. In this study, we focus on statistical analysis of shape features for temporal lobe epilepsy (TLE) lateralization and outcome prediction. First, we evaluate the role of an external atlas as a sharp mean reference shape for shape analysis using Signed Poisson Mapping (SPoM). Then, we extract shape features from the hippocampi of TLE patients and use them for lateralization using various machine learning methods. We also analyze surgery outcome of TLE patients based on Engel classification relative to the shape features of their hippocampi. Results showed no significant differences between statistical outcomes of the atlas based shape reference and the conventional mean shape. However, among the synthetic shapes, statistical significance level on the atlas based reference shape recovered along its principal axis was superior. In terms of machine learning methods for lateralization, linear Support Vector Machine (SVM) outshined other algorithms using wrapper and filter based feature selection schemes. Finally, surgery outcome of patients in terms of hippocampus deformations in different Engel classes showed no statistically significant results.
A novel energy mapping approach in CT-based attenuation correction of PET data using multi-energy CT imaging

A major source of potential pitfalls in CT-based attenuation correction (CTAC) of PET data is the use of integral mode of CT detectors in the presence of polychromatic x-rays resulting in limited information for determination of exact tissue content. The wide range of bone mineral contents and densities in the human body makes it difficult to map the CT number to Linear Attenuation Coefficient (LAC) at the PET energy when merely using one or two scaling factors. In this study we proposed an alternative approach in order to use energy sensitive CT imaging techniques as opposed to integrating CT imaging. The multi-energy strategy would promise significant improvements in tissue determination and leads to accurate energy mapping results in CTAC, which can be especially critical and useful in the presence of varying bone tissues. In order to accurately validate our method a novel bone model based on cortical and marrow mixtures is proposed. Furthermore, a two-step energy mapping algorithm is implemented. For validation, tomographic projections of phantom in five energy bin were acquired and reconstructed. The proposed energy mapping technique was used to estimate the LAC of different bone tissues at 511 keV. The results had 1.1% error at maximum compared to true values. To test the precision, the effect of 10% variation in effective energy was investigated. In different bone tissues, maximum errors induced by the pricewise linear and hybrid methods were 8.0% and 14.6%, respectively; whereas in the proposed multi-energy method, errors was 1.6%, at maximum.
Instantaneous fundamental frequency estimation of non-stationary periodic signals using non-linear recursive filters

This paper presents an algorithm for estimating the instantaneous fundamental frequency of a noisy non-stationary periodic signal whose components are harmonically related. To this end, the authors’ propose a harmonic state-space model for the input signal and use it to derive an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). In this model, the input signal is characterised by a time-varying fundamental frequency and amplitude which is a practical assumption for real-world periodic signals. In contrast to most of existing methods such as short-time Fourier transform, the proposed algorithm does not use any windowing technique. Therefore the trade-off between time and frequency resolutions is less controversial and so can be used for real-time frequency tracking. It also reveals some fine and continuous variations in signal pitch such as Vibrato and Glissando. Simulation results show that the proposed algorithm performs well even when most of the signal energy is contained in the higher-order harmonics. The performance of the proposed algorithm using EKF, UKF and PF is also evaluated and the results are compared in diverse conditions.