nils-herrmann/clean_scirepeval huggingface dataset:
Enforcing stochastic inverse consistency in non-rigid image registration and matching

This paper presents a new method to enforce inverse consistency in nonrigid image registration and matching. Conventional approaches assume diffeomorphic transformation, implicitly or explicitly. However, the inherent smoothness constraint discourages discontinuity consideration. We propose a post-processing algorithm that integrates the input forward and backward fields, which are output by existing registration/matching algorithms, to produce more robust results. Given such a pair of input fields, our algorithm alternately refines the fields by tensor belief propagation, and enforces inverse consistency in stochastic sense by generalized total least squares fitting. To show the efficacy of our stochastic inverse consistency approach, we first present results on very noisy fields. We then demonstrate improvement on existing stereo matching where occlusion is naturally handled by localizing violations of inverse consistency. Finally, we propose a novel application on image stitching, where stochastic inverse consistency is employed in structure deformation, in order to seamlessly align overlapping images with severe misalignment in structure and intensity.
Complexity Analysis of fMRI Time Sequences

In this paper, we propose a novel strategy to explore population complexity and regularity from functional magnetic resonance imaging (fMRI) time series. The analysis is based on the sample entropy (SampEn), which performs a unbiased assessment of the complexity and regularity of time series dynamics. A fMRI scanning series from a healthy subject in resting state was analyzed to prove the effectiveness of this method. Moreover, the framework also includes a similarity test for particular populations. It can be considered as a valuable complementary method to classical fMRI analysis, and it could improve the understanding of complexity human brain functions.
Joint Estimation for Nonlinear Dynamic System from FMRI Time Series

There is an increasing interest in the interactions of factors more directly related to the neutral activity in hemodynamic response (HR) with respect to different experimental conditions. In this work, we present a state-space approach, based on the Balloon model for blood oxygenation level dependent (BOLD) responses, which allows the estimation of the hidden state variables and parameters of the hemodynamic response at the same time. It offers an alternative strategy for understanding the interactions of indirectly observed factors and exploring the changes of biophysical model parameters in variant experimental conditions.
Physiological Fusion of Functional and Structural Images for Cardiac Deformation Recovery

The recent advances in meaningful constraining models have resulted in increasingly useful quantitative information recovered from cardiac images. Nevertheless, as most frameworks utilize either functional or structural images, the analyses cannot benefit from the complementary information provided by the other image sources. To better characterize subject-specific cardiac physiology and pathology, data fusion of multiple image sources is essential. Traditional image fusion strategies are performed by fusing information of commensurate images through various mathematical operators. Nevertheless, when image data are dissimilar in physical nature and spatiotemporal quantity, such approaches may not provide meaningful connections between different data. In fact, as different image sources provide partial measurements of the same cardiac system dynamics, it is more natural and suitable to utilize cardiac physiological models for the fusions. Therefore, we propose to use the cardiac physiome model as the central link to fuse functional and structural images for more subject-specific cardiac deformation recovery through state-space filtering. Experiments were performed on synthetic and real data for the characteristics and potential clinical applicability of our framework, and the results show an increase of the overall subject specificity of the recovered deformations.
Domain partitioning level set surface for topology constrained multiobject segmentation

The key feature of the geometric deformable models (GDMs) is that they naturally allow topological changes during the curve evolution process. However, for multiple objects with extremely small spatial separations, these GDMs would not be able to form separate final contour for each of the object because of the spatial limitation imposed by the finite difference computational grid. Further, a GDM contour could possibly leak out from the weaker edges of the target object and evolve towards nearby stronger edges of another object. In this paper, we present a topology constraining geometrical deformable model scheme to address these two situations. Utilizing the prior knowledge on the number of targets and then-rough spatial positioning, we add an additional domain partitioning level set surface (DPLSS) which seeks between-object gaps and hence constrains the each boundary finding level set surface (BFLSS) to reside within its own designated region and evolve towards the respective object boundary. Relying on adaptive mesh-free particle representations of the analysis domains for DPLSS and BFLSSs, the relative spatial separation between each BFLSS and DPLSS can be flexibly and effectively magnified. Further, the evolution of the DPLSS and BFLSSs is processed in piecewise continuous fashion, through moving lease squares (MLS) approximations, to ensure high accuracy. Experiment results with synthetic and real images show great promise of this effort.
Computational complexity reduction via mode superposition: Application to biomechanics-based nonlinear cardiac deformation recovery

To systematically couple images and physiological models according to their respective merits, state-space filtering frameworks have been proposed for cardiac deformation recovery with promising results. Nevertheless, as thousands of forward simulations are required in every filtering step, the computational complexity is too high to be practical. To reduce the computational complexity without a significant loss of accuracy, we have adopted the mode superposition approach which transforms the cardiac system dynamics to a mathematically equivalent space of much lower dimensions. With the corresponding filtering procedures and components proposed, nonlinear cardiac deformation recovery can be performed in the transformed space with largely reduced computational complexity. Experiments were performed on synthetic data to evaluate the computational complexity and accuracy, and on human data for the clinical relevance.
A Novel Nonlocal QuadraticMRF Prior Model for Positron Emission Tomography

Bayesian approaches, or maximum a posteriori (MAP) methods, have been accepted as an effective solution to overcome the ill-posed problem of such image reconstructions as positron emission tomography (PET) image reconstruction. Based on Bayesian theory, prior information of the objective image is imposed on image reconstruction to suppress noise. Generally, the information in most of prior models is from a simply weighted differences between the pixel densities in a small local neighborhood, so it can only provide limit prior information for reconstruction. In this paper, a novel nonlocal Markov random fields (MRF) prior, which is able to exploit global information in image using large neighborhoods and a new weighting method, is proposed. Relevant experiments about the proposed prior's application in PET are illustrated. Results and comparisons with other priors proved the proposed nonlocal prior's good performance in both lowering noise effect and preserving edges
Local weak form geometric active contours for medical image segmentation

We present a local weak form geometric active contour segmentation framework, which naturally unifies the multi-scale parametric and geometric deformable models. This approach makes use of a local weak form formulation of the level set methods, hence inherently allows topological changes during curve evolution. Further, by adaptively selecting the local integration domain for each point of interests, it achieves the strengths of the parametric models of robust boundary detection from noisy or broken edges. Experiment results on synthetic and real medical images provide insights into the superior ability and performance of this strategy.
Bayesian Reconstruction Using A Novel Nonlocal MRF Prior for PET Transmission Tomography

In positron emission tomography, transmission scans can be performed to estimate attenuation correction factors (ACF) which are in turn used to correct the emission scans. Such an attenuation correction is crucial for quantitatively accurate PET reconstructions. Long scan times, although alleviating the noise effect of the count-limited scans, are blamed for patient uncomfortableness and movement. So transmission tomography often suffers from the noise effect because of the short scan time. Thus, reconstruction which is capable of overcoming the noise effect is highly needed. In this article, based on Markov random fields (MRF) theory, we put forward a novel nonlocal prior for PET transmission tomography. Comparing to the traditional local prior, the proposed prior can exploit the nonlocal information of the objective images and impose an effective regularization on the reconstruction problem for PET transmission tomography.
A Fast 3-D Medical Image Registration Algorithm Based on Equivalent Meridian Plane

For the rigid registration of multi-modality medical images, mutual information (MI) technique is unsuitable to clinical diagnose because of high computational cost and low robustness. In this paper, a new concept of equivalent meridian plane (EMP) is proposed, and the EMP and other two normal feature planes are determined using principal component analysis (PCA); the rough registrations of those 2D planes are to be realized at six freedom degree; finally, the refine registrations can be completed using MI in a small neighboring region. This method is called as EMP based MI registration technique. The accuracy and robustness of EMP-MI approach can be verified by applying it to the simulated and real brain image data (CT, MR, PET, and SPECT). The experimental results indicate that the proposed algorithm reduces computational time distinctly and is a global optimal strategy.
State-Space Analysis of Cardiac Motion With Biomechanical Constraints

Quantitative estimation of nonrigid motion from image sequences has important technical and practical significance. State-space analysis provides powerful and convenient ways to construct and incorporate the physically meaningful system dynamics of an object, the image-derived observations, and the process and measurement noise disturbances. In this paper, we present a biomechanical-model constrained state-space analysis framework for the multiframe estimation of the periodic cardiac motion and deformation. The physical constraints take the roles as spatial regulator of the myocardial behavior and spatial filter/interpolator of the data measurements, while techniques from statistical filtering theory impose spatiotemporal constraints to facilitate the incorporation of multiframe information to generate optimal estimates of the heart kinematics. Physiologically meaningful results have been achieved from estimated displacement fields and strain maps using in vivo left ventricular magnetic resonance tagging and phase contrast image sequences, which provide the tag-tag and tag-boundary displacement inputs, and the mid-wall instantaneous velocity information and boundary displacement measures, respectively
A Novel Method of Correcting the Sinogram Data for Positrion Emission Tomography

In the problems of statistical reconstruction of PET images, Bayesian reconstruction, or maximum a posteriori (MAP) method, has proved its superiority over others among all the regularization methods. To further improve the reconstruction, this paper presents a novel statistical image reconstruction method based on coupled feedback (CF) iterative model for positron emission tompgraphy (PET). This CF iterative algorithm updates the noisy emission sinogram (the measurements of the detectors) using the latest reconstructed image. The experiments and the performance analysis confirm the virtue of the new method.
Level set active contours on unstructured point cloud

We present a novel level set representation and front propagation scheme for active contours where the analysis/evolution domain is sampled by unstructured point cloud. These sampling points are adaptively distributed according to both local data and level set geometry, hence allow extremely convenient enhancement/reduction of local front precision by simply putting more/fewer points on the computation domain without grid refinement (as the cases in finite difference schemes) or remeshing (typical infinite element methods). The front evolution process is then conducted on the point-sampled domain, without the use of computational grid or mesh, through the precise but relatively expensive moving least squares (MLS) approximation of the continuous domain, or the faster yet coarser generalized finite difference (GFD) representation and calculations. Because of the adaptive nature of the sampling point density, our strategy performs fast marching and level set local refinement concurrently. We have evaluated the performance of the method in image segmentation and shape recovery applications using real and synthetic data.
Uncertainty penalized weighted least squares framework for PET reconstruction under uncertain system models

In positron emission tomography (PET), an optimal estimate of the radio activity concentration is obtained from the measured emission data under some criteria. So far, all the well-known reconstruction algorithms require exact known system probability matrix a priori, where the quality of such system model largely determines the quality of the reconstructed images, especially for the least-squares strategies. In this paper, we propose an algorithm for PET reconstruction for the real world case where the PET system model is subject to uncertainties. The method is based on the formulation of PET reconstruction as a regularization problem and the image estimation is achieved with the aid of an uncertainty-weighted least squares framework. The performance of our work is evaluated using the Shepp-Logan simulated phantom data, where it yields significant improvement in image quality over the conventional least-squares reconstruction efforts.
ROBUST RECONSTRUCTION OF PHYSIOLOGICAL PARAMETERS FROM DYNAMIC PET DATA

The primary goal of dynamic positron emission tomography (PET) is to quantify the physiological and biological processes through tracer kinetics analysis. However, accurate quantification of such functional processes with PET is difficult to achieve because of the fundamental difficulties related to uncertainties in the imaging system and the measurement data. We introduce a novel and efficient strategy whereby compartmental tracer model parameters can be identified based on the H infin principles. The system equation is constructed from particular tracer kinetic models, with the number and relationship between tissue compartments dictated by the physiological and biochemical properties of the process under study. And the observation equation on measurement data is formed based on the PET imaging mechanism. Once we have the state space representation for the PET dynamic system, a robust system identification paradigm is adopted to estimate the tracer kinetics parameters from PET sinogram data directly. It is derived and extended from the Hinfin filtering principles and is particularly powerful for real-world situations where the types and levels of the disturbances are unknown. Specifically, we show the results of applying this strategy to synthetic phantom data for accuracy assessment
H/sub /spl infin// filtering and physical modeling for robust kinematics estimation

A robust H/sub /spl infin// algorithm for object kinematics estimation from image sequences is presented. The framework relies on both the physical modeling of the object structure and behavior, and the minimization of the worst-case error filtering criterion. By employing the finite element method, the system dynamics of the object is constructed as a set of physically meaningful partial differential equations, which are then converted into continuous- and discrete-time state space representations. In contrast to the popular Kalman filtering strategy which produces the minimum-mean-square-error estimates, the mini-max H/sub /spl infin// filter is adopted which assumes no prior statistics knowledge on the external disturbances. A series of experiments are performed using synthetic data of various noise types and levels to assess the accuracy and robustness of the H/sub /spl infin// filtering framework, and to make comparisons to the Kalman filtering results. Practical applications to magnetic resonance image sequences of the heart are also presented.
Simultaneous Reconstruction and Segmentation of Dynamic PET via Low-Rank and Sparse Matrix Decomposition

Although of great clinical value, accurate and robust reconstruction and segmentation of dynamic positron emission tomography (PET) images are great challenges due to low spatial resolution and high noise. In this paper, we propose a unified framework that exploits temporal correlations and variations within image sequences based on low-rank and sparse matrix decomposition. Thus, the two separate inverse problems, PET image reconstruction and segmentation, are accomplished in a simultaneous fashion. Considering low signal to noise ratio and piece-wise constant assumption of PET images, we also propose to regularize low-rank and sparse matrices with vectorial total variation norm. The resulting optimization problem is solved by augmented Lagrangian multiplier method with variable splitting. The effectiveness of proposed approach is validated on realistic Monte Carlo simulation datasets and the real patient data.
Cardiac motion and material properties analysis using data confidence weighted extended Kalman filter framework

A biomechanical model constrained stochastic finite element framework has been developed to estimate jointly myocardium kinematics and material parameters from medical image sequences. In an extended Kalman filter formulation, we have observed that the augmented state error covariance matrix must be carefully chosen in order to avoid divergence (Shi, P. and Liu, H., MICCAI, pp.634-41, 2002). We incorporate confidence measures of the input imaging and imaging-derived data into the initialization of the state error covariance matrix. These confidence measures come from the shape-matching process of boundary points and from the local phase coherence of the magnetic resonance velocity images. Experiments with two types of imaging inputs have shown vastly improved filtering efficiency and physiologically meaningful results.
Sensitivity Analysis for Biomedical Models

This article discusses the application of sensitivity analysis (SA) in biomedical models. Sensitivity analysis is widely applied in physics, chemistry, economics, social sciences and other areas where models are developed. By assigning a prior probability distribution to each model variable, the SA framework appeals to the posterior probabilities of the model to evaluate the relative importance of these variables on the output distribution based on the principle of general variance decomposition. Within this framework, the SA paradigm serves as an objective platform to quantify the contributions of each model factor relative to their empirical range. We present statistical derivations of variance-based SA in this context and discuss its detailed properties through some practical examples. Our emphasis is on the application of SA in the biomedical field. As we show, it may provide a useful tool for model quality assessment, model reduction and factor prioritization, and improve our understanding of the model structure and underlying mechanisms. When usual approaches for calculating sensitivity index involve the employment of Monte Carlo analysis, which is computationally expensive in the large-sampling paradigm, we develop two effective numerical approximate methods for quick SA evaluations based on the unscented transformation (UT) that utilize a deterministic sampling approach in place of random sampling to calculate posterior statistics. We show that these methods achieve an excellent compromise between computational burden and calculation precision. In addition, a clear guideline is absent to evaluate the importance of variable for model reduction, we also present an objective statistical criterion to quantitatively decide whether or not a descriptive parameter is nominal and may be discarded in ensuing model-based analysis without significant loss of information on model behavior.
Exploiting MR venography segmentation for the accurate model estimation of BOLD signal

The amplitude of the BOLD signal depends heavily upon the resting blood volume fraction (V0). However, most existing haemodynamic data assimilation studies pretermit such concern through assigning arbitrarily the value in a physiological plausible range. In this work, we presented the first exploration of the influence of the fraction V0 to model estimation, where the exact value of V0 was calibrated by MR angiography experiment. Our results show this process may greatly increase the accuracy of the data assimilation procedure. Furthermore, they also suggest that the result of traditional intensity-based activation inference would displace spatially from the realistic activated locus, the estimated neuronal efficiency parameter ε can accurately label the activated region, thus is a better candidate to evaluate activation level.
Efficient point clouds based scatter correction for fully 3D PET

A novel way to perform scatter correction for Positron Emission Tomography by using point clouds representation is presented. The scatter correction method is based on single scatter simulation. The strategy of scatter point sampling during scatter calculation will affect the accuracy of scatter correction directly and the number of sampled scatter points determines the computation complexity. Compared with regular voxel based scatter point sampling, the new method, which has been greatly reduced in size, adopts content-adaptive point clouds structure. The point clouds method can capture both anatomical and functional information, and cover the critical regions with limited number of points. Reduced sampled points significantly bring down the computation cost. Experiments with Monte Carlo simulations prove that such process can reduce the time consumption while at the same time maintain the reconstruction accuracy at the same level.
SIMULTANEOUS RECOVERY OF LEFT VENTRICULAR MOTION AND INPUT FORCES FROM MEDICAL IMAGE SEQUENCES

Since the introduction of biomechanical models into cardiac image analysis, there have been a number of efforts to estimate patient-specific cardiac kinematic functions and material properties, assuming the unobservable driving forces (a.k.a. the input forces) as known or constructible from boundary conditions. In this paper, we present a multiframe estimation framework which simultaneously recovers the cardiac motion parameters and the input forces from periodic medical image sequences. It is realized through a Kalman filter which generates the residual innovation sequences, followed by an on-line recursive least-squares filter that uses the residual innovation sequences to compute the values of input forces and to correct the estimations of cardiac kinematic functions. We demonstrate the ability of the framework on synthetic data and magnetic resonance image sequences