nils-herrmann/clean_scirepeval huggingface dataset:
Physiological-Model-Constrained Noninvasive Reconstruction of Volumetric Myocardial Transmembrane Potentials

Personalized noninvasive imaging of subject-specific cardiac electrical activity can guide and improve preventive diagnosis and treatment of cardiac arrhythmia. Compared to body surface potential (BSP) recordings and electrophysiological information reconstructed on heart surfaces, volumetric myocardial transmembrane potential (TMP) dynamics is of greater clinical importance in exhibiting arrhythmic details and arrythmogenic substrates inside the myocardium. This paper presents a physiological-model-constrained statistical framework to reconstruct volumetric TMP dynamics inside the 3-D myocardium from noninvasive BSP recordings. General knowledge of volumetric TMP activity is incorporated through the modeling of cardiac electrophysiological system, and is used to constrain TMP reconstruction. This physiological system is reformulated into a stochastic state-space representation to take into account model and data uncertainties, and nonlinear data assimilation is developed to estimate volumetric myocardial TMP dynamics from personal BSP data. Robustness of the presented framework to practical model and data errors is evaluated. Comparison of epicardial potential reconstructions with classical regularization-based approaches is performed on computational phantom regarding right bundle branch blocks. Further, phantom experiments on intramural focal activities and an initial real-data study on postmyocardial infarction demonstrate the potential of the framework in reconstructing local arrhythmic details and identifying arrhythmogenic substrates inside the myocardium.
Normalization of Functional Magnetic Resonance Images by Classified Cerebrospinal Fluid Cluster

For functional magnetic resonance imaging (fMRI) time series data, traditional intensity normalization techniques may introduce negative correlation with the neurological stimulation in non-activated voxels, and hence may cause incorrect identification of the activated/deactivated region. In this study, we present a modified proportional scaling method for intensity normalization using segmented specific tissue. In particular, the mean intensity across the classified cerebrospinal fluid (CSF) cluster, instead of the one across the entire intracerebral voxels, is used for the rescaling of all voxel intensity of a particular image frame. The usefulness of the method is demonstrated on block design fMRI data, which shows that the approach can avoid the negative shift in Z statistics quite well. In addition, this strategy can also be applicable to the analysis of positron emission tomography (PET), single photon emission computed tomography (SPECT) and other functional imaging modalities
Meshfree cardiac motion analysis using composite material model and total Lagrangian formulation

We address three of the key issues in biomechanics-based cardiac motion analysis: the representation and computation platform, the realistic material models, and the proper deformation formulation. In particular, we incorporate the myocardial tissue fiber structure information, formulate the finite nonlinear deformation situations, and adopt the meshfree particle paradigm which exhibits certain advantages for problems involving large deformation, anisotropic materials, and material/kinematics discontinuity. We demonstrate the ability of our effort through synthetic data and cardiac magnetic resonance sequence.
Neighbor-constrained active contours without edges

To achieve robustness against different images, a novel region-based geometric deformable model framework employing neighboring information constraints is proposed. The fundamental power of this strategy makes uses of the image information at the support domain around each point of interest, thus effectively enlarges the capture range of each point to have a better regional understanding of the information within its local neighborhood. In other words, we establish the Mumford-Shah energy functional on each image point with its local neighborhood in a way such that it is capable of providing sufficient information to define a desired segmentation which is robust against intensity inhomogeneity and noise impact. The resulting partial differential equation is solved numerically by the finite differences schemes on pixel-by-pixel domain. Experimental results on synthetic and real images demonstrate its superior performance.
A unified system identification approach to dynamic PET parametric imaging

Dynamic PET is becoming more popular and preferable in bioimaging community nowadays. It owns powerful capability of estimating physiological parameters with tracer kinetics modeling in a non-invasive manner. There are three main parts of this task: design of a priori identifiable tracer kinetic model, parameter estimation methods and parameter sensitivity analysis. In this paper, we proposed a unified approach to dynamic PET parametric imaging based on system identification theory to address the tasks mentioned above. The continuous compartmental physiological model is transformed into a discrete state-space model which can be converted into an autoregressive moving average model (ARMAX) by similarity transformation. Then the parameters of this ARMAX model can be estimated from the input-output dynamic PET data using prediction error method (PEM) with robust and computational efficient gradient based algorithm. The prior information of physiological system is then combined with parameters of ARMAX model to come up with a compact representation of polynomial equations whose solutions are the physiological parameters and the a priori identiflability of physiological system is identical to the uniqueness of the solutions. We use a 2-tissue compartmental model to demonstrate our method and test the parameter sensitivity using computer simulations. Our preliminary results show the effectiveness and validity of proposed framework for parameter estimation task. With the sensitivity analysis the critical parameters can also be discriminated.
Using human experts' gaze data to evaluate image processing algorithms

Understanding the capabilities of the human visual system with respect to image understanding, in order to inform image processing, remains a challenge. Visual attention deployment strategies of experts can serve as an objective measure to help us understand their learned perceptual and conceptual processes. Understanding these processes will inform and direct image the selection and use of image processing algorithms, such as the dermatological images used in our study. The goal of our research is to extract and utilize the tacit knowledge of domain experts towards building a pipeline of image processing algorithms that could closely parallel the underlying cognitive processes. In this paper we use medical experts' eye movement data, primarily fixations, as a metric to evaluate the correlation of perceptually-relevant regions with individual clusters identified through k-means clustering. This test case demonstrates the potential of this approach to determine whether a particular image processing algorithm will be useful in identifying image regions with high visual interest and whether it could be a component of a processing pipeline.
Segmentation of myocardium using velocity field constrained front propagation

We present a velocity-constrained front propagation approach for myocardium segmentation from magnetic resonance intensity image (MRI) and its matching phase contrast velocity (PCV) images. Our curve evolution criterion is dependent on the prior probability distribution of the myocardial boundary and the conditional boundary probability distribution, which is constructed from the MRI intensity gradient, the PCV magnitude, and the local phase coherence of the PCV direction. A two-step boundary finding strategy is employed to facilitate the computation. For the first image frame, a gradient-only fast marching/level set step is used to approach the boundary, and a narrowband is formed around the curve. The initial boundary is then refined using the full information from priors and all three image sources. For the other frames, the resulting contours from the previous frames are used as the initialization contours, and only refinement step is needed. Experiment results from canine MRI sequence are presented, and are compared to results from gradient-only segmentation.
Electrophysiological properties under heart failure conditions in a human ventricular cell: A modeling study

Heart failure (HF) is one of the major diseases across the world. During HF the electrophysiology of the failing heart is remodeled, which renders the heart more susceptible to ventricular arrhythmias. In this study, we quantitatively analyze the effects of electrophysiological remodeling of the major currents of human ventricular myocytes on the dynamics of the failing heart. We develop a HF model using a modified version of a recently published model of the human ventricular action potential, the O'Hara-Virag-Varro-Rudy (OVVR) model. The proposed HF model incorporates recently available HF clinical data. It can reproduce most of the action potential (AP) properties of failing myocytes, including action potential duration (APD), amplitude (APA), notch (APN), plateau (APP), resting membrane potential (RMP), and maximum upstroke velocity (dV/dtmax). In addition, the model reproduces the behavior of the [Na+], concentration and [Ca2+]i dynamics. Moreover, the HF model exhibits alternans with a fast pacing frequency and can induce early afterdepolarizations (EADs). Additionally, blocking the late sodium current shortens the APD and suppresses EADs, in agreement with experimental findings. The dynamics of the proposed model are assessed through investigating the rate dependence of the AP and the dynamics of the major currents. The steady-state (S-S) and S1-S2 restitution curves along with accommodation to an abrupt change in cycle length were evaluated. Our study should help to elucidate the roles of alterations in electrophysiological properties during HF. Also, this HF cellular model could be used to study HF in a realistic geometry and could be embedded into a model of HF electromechanics to investigate electrical and mechanical properties simultaneously during HF.
Image Registration Based on Fuzzy Similarity

In this paper, a novel image attribute, fuzzy edge field (FEF) is defined and used to strengthen the similarity between images. As the feature of images, FEF could be extracted automatically without any manual interaction. A new registration algorithm based on fuzzy edge similarity (FES) was presented. Generalized fuzzy operator (GFO) is utilized to detect edge. Gaussian function is explored to expand the edge to the neighborhood region. A fuzzy similarity measure is chosen as the registration function. We evaluate the effectiveness of the EMP-MI approach by applying it to the simulated and real brain image data (CT, MR, PET, and SPECT). Experimental results indicate that the function is less sensitive to low sampling resolution and noise, do not contain incorrect global maxima that are sometimes found in the mutual information (MI) function, and interpolation-induced local minima can be reduced.
Maximum a Posteriori Strategy for the Simultaneous Motion and Material Property Estimation of the Heart

In addition to its technical merits as a challenging nonrigid motion and structural integrity analysis problem, quantitative estimation of cardiac regional functions and material characteristics has significant physiological and clinical value. We developed a stochastic finite-element framework for the simultaneous recovery of myocardial motion and material parameters from medical image sequences with an extended Kalman filter approach, and we have shown that this simultaneous estimation strategy achieves more accurate and robust results than separated motion and material estimation efforts. In this paper, we present a new computational strategy for the framework based upon the maximum a posteriori estimation principles, realized through the extended Kalman smoother, that produces a sequence of kinematics state and material parameter estimation of the entire myocardium, including the endocardial, epicardial, and midwall tissues. The system dynamics equations of the heart are constructed using a biomechanical model with stochastic parameters, and the tissue material and deformation parameters are jointly estimated from the periodic imaging data. Noise-corrupted synthetic image sequences with known kinematics and material parameters are used to assess the accuracy and robustness of the framework. Experiments with canine magnetic resonance tagging and phase-contrast image sequences have been conducted with very promising results, as validated through comparison to the histological staining of postmortem myocardium.
Image Understanding from Experts' Eyes by Modeling Perceptual Skill of Diagnostic Reasoning Processes

Eliciting and representing experts' remarkable perceptual capability of locating, identifying and categorizing objects in images specific to their domains of expertise will benefit image understanding in terms of transferring human domain knowledge and perceptual expertise into image-based computational procedures. In this paper, we present a hierarchical probabilistic framework to summarize the stereotypical and idiosyncratic eye movement patterns shared within 11 board-certified dermatologists while they are examining and diagnosing medical images. Each inferred eye movement pattern characterizes the similar temporal and spatial properties of its corresponding segments of the experts' eye movement sequences. We further discover a subset of distinctive eye movement patterns which are commonly exhibited across multiple images. Based on the combinations of the exhibitions of these eye movement patterns, we are able to categorize the images from the perspective of experts' viewing strategies. In each category, images share similar lesion distributions and configurations. The performance of our approach shows that modeling physicians' diagnostic viewing behaviors informs about medical images' understanding to correct diagnosis.
Constrained Nonlinear Estimation of FMRI Hemodynamic Response Parameters

We present a constrained nonlinear optimization approach to the estimation of hemodynamic response (HR) shape from fMRI images. The controlled random search (CRS) based strategy limits the HR parameters within a physically/physiologically plausible phase space, and thus may produce more meaningful results than the traditional unconstrained methods. Block-design and event-related fMRI experiments have been conducted to examine the validity of the framework.
Performance Results of BGO Block Detectors Based on Flat Panel PS-PMT for PET

We have developed a new detector composed of a 20times20 array of 2times2times20 mm3 Bi4Ge3O12 (BGO) scintillators and a novel flat panel position sensitive photomultiplier tube(FP-PS-PMT) - Hamamatsu R8400-00-M256. The performance of the block detector was evaluated in terms of applicability to positron emission tomography (PET). The energy resolution was about 23% FWHM for 511 keV gamma-rays. The spatial resolution of the detector was evaluated by measuring the detector response functions (DRFs) for each BGO segment and by measuring the coincidence response functions (CRFs). The average FWHM values of the DRFs were 2.0 mm. For the CRFs performance, the average FWHM values were 2.82 mm at 0deg and 5.20 mm at 30deg.
Sampled-Data ηα Filtering for Robust Kinematics Estimation: Applications to Biomechanics-Based Cardiac Image Analysis

A sampled-data Hinfin filtering strategy is proposed for cardiac kinematics estimation from periodic medical image sequences. Stochastic multi-frame filtering frameworks are constructed to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data in a coordinated fashion. As robustness is of paramount importance in cardiac motion estimation, this mini-max Hinfin strategy is particularly powerful for real-world problems where the types and levels of model uncertainties and data disturbances are not available a priori. For the hybrid cardiac analysis system with continuous dynamics and discrete measurements, the state estimates are predicted according to the continuous-time state equation between observation time points, and updated with the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results for the continuously evolving cardiac dynamics. The strategy is validated through synthetic data experiments to illustrate its advantages and on canine MR phase contrast images to show its clinical relevance
Regularity and Complexity of Human Electroencephalogram Dynamics: Applications to Diagnosis of Alzheimers Disease

In this paper, we evaluate the complexity and regularity of human electroencephalogram (EEG) dynamics using approximate entropy (ApEn), and the results are used to distinguish Alzheimer's disease (AD) patients from healthy subjects. From the 10-channel EEG time series recordings of 20 healthy subjects and 14 AD patients with closed eyes, our analysis has shown that AD patients have lower ApEn values than healthy subjects. These results support the previous hypothesis that greater regularity corresponds to greater component autonomy and isolation in many complex systems. We believe that our effort provides a valuable complementary framework to the classical EEG analysis, and it could help revealing the complexity of the human brain functions
Joint analysis of heart geometry and kinematics with spatiotemporal active region model

We present a unified cardiac image analysis framework which has the capability to simultaneously recover the geometry and kinematics of the entire left ventricle of the heart, including the endo-, epi-, and mid-wall myocardium. Robust segmentation and motion results are achieved at the same time through the tracking of the spatiotemporal evolution of a mechanics-motivated active region model (ARM). The external forces applied to the ARM are derived from node-dependent imaging data and image-driven measures, statistical priors of the cardiac boundaries, and cyclic motion models of the myocardial tissue elements. The strategy is posed as an energy minimization problem and is solved with finite element representation and iterative procedures. Promising experiment results with canine magnetic resonance phase contrast imaging sequence are presented and discussed.
Towards integrated noninvasive personalization of cardiac electromechanics

Personalization of cardiac functions and properties from subject-specific noninvasive clinical observations has been an active area with increasing clinical significance. The personalized cardiac information can quantitatively reflect the pathophysiology of an individual heart and further be used to assist heart diagnosis and treatment planning. Due to the electromechanically integrated nature of the heart, it is important to use an integrative approach to elucidate cardiac functions and properties of a specific individual. In this paper, we first introduce our earlier works on noninvasive recovery of cardiac electrophysiology and cardiac mechanics from body surface potential maps (BSPMs) and medical image sequences, respectively. Recognizing BSPMs complements medical image sequences in providing insights into an individual heart, we present works on mechanics-guided personalization of cardiac electrophysiology and electrophysiology-driven personalization of cardiac mechanics. Furthermore, working towards using an integrative approach to elucidate a subject-specific cardiac system, we propose an integrated framework for noninvasive personalization of cardiac electromechanics by combining an integrated cardiac physiome model and multi-modality observations (BSPMs and medical image sequences).
Unsupervised Segmentation of Medical Image Based on FCM and Mutual Information

In the scope of medical image processing, segmentation is important and difficult. This paper presents a novel algorithm for segmentation of medical image. Our algorithm is formulated by combining the fuzzy c-means clustering (FCM) algorithm with the mutual information (MI) technique. The initial threshold can be chosen using FCM algorithm, and in the iteration process, an optimal threshold will be determined by maximizing the MI between the original volume and the thresholded volume. We evaluate the effectiveness of the proposed approach by applying it to the medical images, including magnetic resonance imaging (MRI), microphotographic image. The experimental results indicate that the proposed method has not only visually better or comparable segmentation effect but also, more favorably, removal ability for noise.
Interregional Functional Connectivity via Pattern Synchrony

In this study, we propose a novel approach for functional connectivity analyses, in which cross-sample entropy (c-SampEn) measures the probability of finding similar patterns in the fMRI time sequence of distinct brain regions, instead of the time synchronization of the signals. In application to two simulated data, c-SampEn algorithm show that provide a clear group distinctions. Furthermore, we also present a realistic fMRI dataset analysis in steady state, this result is contrasted with the conventional linear correlation method and exhibits a decided difference between them. We argue that c-SampEn has not only more extensive applicability than conventional linear methods, but also can provide a new, valuable complementary insight to the understanding of interregional variations across the brain
Segmentation Through Point-based Multiscale Deformable Model

We propose a local weak form formulation for the geometric deformable model (GDM) which is capable of achieving robust segmentation results for noisy and broken edges. The local weak form partial differential equation (PDE) is solved on the analysis/evolution domain which is adaptively sampled by unstructured point cloud for improved accuracy and/or reduced computational cost. The fundamental power of this strategy rests with the explicit integration of neighboring information when constructing inter-point constraint and image-derived evolution force for each front point within a local influence domain adaptively determined by geometry and image information. As a result, this local weak form GDM naturally unifies the essences of the geometric and parametric snakes through automatic local scale selection at each contour point, and exhibits their respective fundamental strengths of allowing stable boundary detection when the edge information is weak and possibly discontinuous while maintaining the abilities to handle topological changes during front evolution. In particular, this paper presents an implementation of the method through local integration of the level set function and the evolution forces. Experimental results on synthetic and real images demonstrate its superior performance