nils-herrmann/clean_scirepeval huggingface dataset:
MRI-based prostate brachytherapy seed localization

A magnetic resonance imaging (MRI) pulse sequence and a corresponding image processing algorithm to localize prostate brachytherapy seeds during or after therapy are presented. Inversion- Recovery with ON-resonant water suppression (IRON) is an MRI methodology that generates positive contrast in regions of magnetic field susceptibility, as created by prostate brachytherapy seeds. Phantoms comprising of several materials found in brachytherapy seeds were created to assess the usability of the IRON pulse sequence for imaging seeds. Resulting images show that seed materials are clearly visible with high contrast using IRON, agreeing with theoretical predictions. A seed localization algorithm to process IRON images demonstrates the potential of this imaging technique for seed localization and dosimetry.
Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy (1999, 2000) recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.
Robust maximum likelihood estimation in Q-space MRI

Q-space imaging is an emerging diffusion weighted MR imaging technique to estimate molecular diffusion probability density functions (PDF's) without the need to assume a Gaussian distribution. We present a robust M-estimator, Q-space Estimation by Maximizing Rician Likelihood (QEMRL), for diffusion PDFs based on maximum likelihood. PDFs are modeled by constrained Gaussian mixtures. In QEMRL, robust likelihood measures mitigate the impacts of imaging artifacts. In simulation and in vivo human spinal cord, the method improves reliability of estimated PDFs and increases tissue contrast. QEMRL enables more detailed exploration of the PDF properties than prior approaches and may allow acquisitions at higher spatial resolution.
A hierarchical algorithm for limited-angle reconstruction

The authors describe and demonstrate a hierarchical reconstruction algorithm for use in noisy and limited-angle or sparse-angle tomography. The algorithm estimates the object's mass, center of mass, and convex hull from the available projections, and uses this information, along with fundamental mathematical constraints, to estimate a full set of smoothed projections. The mass and center of mass are estimated using a maximum-likelihood (ML) estimator derived from the principles of consistency of the Radon transform. The convex hull estimate is produced by first estimating the positions of support lines of the object from each available projection and then estimating the overall convex hull using ML or maximum a posteriori (MAP) techniques. The position of two support lines from a single projection is estimated using either a generalized likelihood ratio technique for estimating jumps in linear systems or a support-width penalty method that uses Akaike's model-order estimation technique.<<ETX>>
An Iterative Approach To Sinogram Restoration

We present a projection-space method for reconstruction from limited-angle, sparse-angle, and noisy projections. A variational problem, formulated in projection-space; incorporates consistency of the projections using constraints, convex support as a penalty on sinogram values, and sinogram smoothness through spatially varying horizontal and vertical smoothing coefficients. Specification of the vertical smoothing coefficient requires the convex support and an a priori expectation of the object's profile within its convex support. The horizontal smoothing coefficient is chosen heuristically to help account for the absence of observed projections.
Covariance shrinking in active shape models with application to gyral labeling of the cerebral cortex

Active shape models (ASMs) have been widely used in segmentation tasks in medical image analysis. Complex structures and a limited number of training samples can, however, result in the failure to capture the complete range of shape variations. Various modifications to the point distribution model (PDM) have been proposed to increase the flexibility of the model. Still model parameters are often determined empirically without respect to the underlying data structure. We explore shrinkage covariance estimation in building a PDM by combining the sample covariance matrix with a target covariance matrix estimated from a low-dimensional constrained model. Instead of using a global shrinkage intensity, we apply a spatially varying shrinkage intensity field to better adapt to the spatially varying characteristic of a complex shape. The parameters of the constrained model and the amount of shrinkage are determined in a data-driven fashion, so that the resulting distribution is optimized in representing the underlying data. The PDM, which we call SC-PDM, shows an increased flexibility in fitting new shapes and at the same time, is robust to noise. We demonstrate the effectiveness of using SC-PDM to label gyral regions on the human cerebral cortex.
On div-curl regularization for motion estimation in 3-D volumetric imaging

We consider the classical optical flow algorithm due to Horn and Schunck (1981) for estimating the motion of brightness patterns between image pairs. We use a modified smoothness condition based on the divergence and curl of the velocity field. In previous work, we have developed well-posed stochastic state-space models for these optical flow methods in two dimensions. This paper extends our results to 3-D. We first show that by using the first order div-curl spline, it is not possible to obtain a first order linear differential well-posed model in 3-D. Next, we employ the second order div-curl spline smoothness condition and develop well-posed state-space models.
Fast tracking of cardiac motion using 3D-HARP

Magnetic resonance (MR) tagging is capable of accurate, noninvasive quantification of regional myocardial function. Routine clinical use, however, is hindered by cumbersome and time-consuming postprocessing procedures. We propose a fast, semiautomatic method for tracking three-dimensional (3-D) cardiac motion from a temporal sequence of short- and long-axis tagged MR images. The new method, called 3-D-HARmonic Phase (3D-HARP), extends the HARP approach, previously described for two-dimensional (2-D) tag analysis, to 3-D. A 3-D material mesh model is built to represent a collection of material points inside the left ventricle (LV) wall at a reference time. Harmonic phase, a material property that is time-invariant, is used to track the motion of the mesh through a cardiac cycle. Various motion-related functional properties of the myocardium, such as circumferential strain and left ventricular twist, are computed from the tracked mesh. The correlation analysis of 3D-HARP and FINDTAGS + Tag Strain(E) Analysis (TEA), which are well-established tag analysis techniques, shows that the regression coefficients of circumferential strain (E/sub CC/) and twist angle are r/sup 2/=0.8605 and r/sup 2/=0.8645, respectively. The total time required for tracking 3-D cardiac motion is approximately 10 min in a 9 timeframe tagged MRI dataset and has the potential to be much faster.
Fuzzy c-means with variable compactness

Fuzzy c-means (FCM) clustering has been extensively studied and widely applied in the tissue classification of biomedical images. Previous enhancements to FCM have accounted for intensity shading, membership smoothness, and variable cluster sizes. In this paper, we introduce a new parameter called "compactness" which captures additional information of the underlying clusters. We then propose a new classification algorithm, FCM with variable compactness (FCMVC), to classify three major tissues in brain MRIs by incorporating the compactness terms into a previously reported improvement to FCM. Experiments on both simulated phantoms and real magnetic resonance brain images show that the new method improves the repeatability of the tissue classification for the same subject with different acquisition protocols.
Quantitative ischemia detection during cardiac MR stress testing

Because ECG alterations due to ischemia cannot be reliably detected in the high-field MRI environment, detection of wall motion abnormalities are often the only method to ensure patient safety. In this study, we investigate the use of real-time Harmonic Phase (HARP) MRI for the quantitative, operator-independent detection of the onset of ischemia during acute coronary occlusion. Six mongrel dogs underwent acute coronary artery ischemia of 2 minutes' duration while continuous HARP MR images were acquired followed by 5 minutes of reperfusion. During a second ischemic episode, conventional cine wall motion images were acquired. The time from occlusion to the detection of ischemia by each MR technique, as well as ECG ischemic alterations, was determined. In 5 of 6 animals, the onset of ischemia was detected significantly earlier by HARP than by cine MRI (11 /spl plusmn/ 5 s HARP vs. 34 /spl plusmn/ 14.8 s cine, P<0.03). HARP ischemia detection preceded ECG changes, on average, by 66 seconds. Cine MRI did not detect ischemia significantly earlier than when ECG changes were apparent (P=0.11). The rapid acquisition and detection of ischemia using HARP MRI shows promise as a nonsubjective method to diagnose significant coronary lesions in patients while ensuring patient safety during stress testing.
A JOINT REGISTRATION AND SEGMENTATION APPROACH TO SKULL STRIPPING

Extraction of the cerebrum, cerebellum, and brain stem from structural magnetic resonances images (MRIs) is an important initial step in neuroimaging. We present an automated algorithm that solves this difficult problem, often referred to as skull stripping, which is novel for its use of registration, segmentation, and morphological operations. Our algorithm is also concerned with an accurate representation of the grey matter boundary, which is a unique feature. We also present results demonstrating the accuracy of this approach
Thalamic parcellation from multi-modal data using random forest learning

The thalamus sub-cortical gray matter structure consists of contiguous nuclei, each individually responsible for communication between various cerebral cortex and midbrain regions. These nuclei are differentially affected in neurodegenerative diseases such as multiple sclerosis and Alzheimer's. However thalamic parcellation of the nuclei, manual or automatic, is difficult given the limited contrast in any particular magnetic resonance (MR) modality. Several groups have had qualitative success differentiating nuclei based on spatial location and fiber orientation information in diffusion tensor imaging (DTI). In this paper, we extend these principles by combining these discriminating dimensions with structural MR and derived information, and by building random forest learners on the resultant multi-modal features. In training, we form a multi-dimensional feature per voxel, which we associate with a nucleus classification from a manual rater. Learners are trained to differentiate thalamus from background and thalamic nuclei from other nuclei. These learners inform the external forces of a multiple object level set model. Our cross-validated quantitative results on a set of twenty subjects show the efficacy and reproducibility of our results.
A multi-compartment segmentation framework with homeomorphic level sets

The simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed; however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In this paper, a framework for segmenting multiple objects that permits different forces to be applied to different boundaries while maintaining object topology and relationships is presented. Because of this framework, the segmentation of multiple objects each with multiple compartments is supported, and no overlaps or vacuums are generated. The computational complexity of this approach is independent of the number of objects to segment, thereby permitting the simultaneous segmentation of a large number of components. The properties of this approach and comparisons to existing methods are shown using a variety of images, both synthetic and real.
On optimal brightness functions for optical flow

In the typical optical flow problem, the brightness function is an inherent part of the underlying physical process. In the case of magnetic resonance imaging of the heart, however, it is possible to modulate the brightness function using tagging techniques. Experimental evidence suggests that when a sinusoidal brightness function is used, there is an optimum frequency. The reasons for an optimal frequency in one dimension are investigated by formulating the optical flow problem as an optimal linear smoothing problem. A method based on minimizing the theoretical smoothed error variance is proposed for finding the optimal frequency a priori. Simulations are presented for a Wiener process velocity that show the existence of an optimal frequency and the ability of the proposed method to determine this frequency a priori.<<ETX>>
Compressed sensing based intensity non-uniformity correction

We present a compressed sensing based approach to remove gain field from magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in the radio-frequency (RF) coil appears as shading artifact in the intensity image. The inhomogeneity poses problem in any automatic algorithm that uses intensity as a feature. It has been shown that at low field strength, the shading can be assumed to be a smooth field that is composed of low frequency components. Thus most inhomogeneity correction algorithms assume some kind of explicit smoothness criteria on the field. This sometimes limits the performance of the algorithms if the actual inhomogeneity is not smooth, which is the case at higher field strength. We describe a model-free, nonparametric patch-based approach that uses compressed sensing for the correction. We show that these features enable our algorithm to perform comparably with a current state of the art method N3 on images acquired at low field, while outperforming N3 when the image has non-smooth inhomogeneity, such as 7T images.
Octree-Based Topology-Preserving Isosurface Simplification

Isosurface generation has many important applications in medical imaging. Standard isosurface algorithms generate very large triangle meshes when high resolution volumetric data is available, which increases rendering time and storage requirements. Most existing mesh simplification algorithms either do not guarantee non-intersecting meshes or require large cost to prevent self-intersection. We present an octree-based isosurface generation and simplification method that preserves topology, guarantees no selfintersections, and generates a surface that approximates the true isosurface of the underlying data. Rather than focusing on directly simplifying the surface mesh, the new strategy is to generate an octree grid from the original volumetric grid in a way that guarantees these desired properties of the generated isosurface. The new method demonstrates savings of 70% in mesh nodes for real 3D medical data with highly complicated shapes such as the human brain cortex and the pelvis. The simplified surface stays within a userspecified distance bound from the original finest resolution surface, preserves the original topology and has no selfintersections.
Unsupervised partial volume estimation in single-channel image data

Partial volume effects are present in nearly all medical imaging data. These artifacts blur the boundaries between different regions, making accurate delineation of anatomical structures difficult. Here, the authors propose a method for unsupervised estimation of partial volume effects in single-channel image data. Based on a statistical image model, an algorithm is derived for estimating both partial volumes and the means of the different tissue classes in the image. To compensate for the ill-posed nature of the estimation problem, the authors employ a Bayesian approach that places a prior probability model on the parameters. They demonstrate on simulated and real images that the new algorithm is superior in several respects to the fuzzy and Gaussian clustering algorithms that have previously been used for modeling partial volume effects.
A model-based vision system for object recognition with synthetic aperture radar data

An overview of the architecture and implementation of a model-based vision system developed for object recognition with SAR (synthetic aperture radar) data is presented. The initial implementation of the system is currently being used to develop experimental results to guide refinements and enhancements. The system uses detailed analytic prediction models and capable description algorithms, with all knowledge and uncertainty consistently represented. The prediction component automatically generates integrated hierarchical representations of both structural and appearance information, and represents an important step toward automatic object recognition. The recognition system architecture features modular computational agents that support distributed, localized control, with the ability to extract and use object-specific knowledge from the prediction database. The system will serve as a testbed for model-based vision research to allow experimentation with new algorithms and alternative recognition approaches.<<ETX>>
Motion estimation from tagged MR image sequences

A method for reconstructing motion from sequences of tagged magnetic resonance (MR) images is presented. MR tagging is used to create a spatial pattern of varying magnetization so that objects which may otherwise have constant intensity are textured, which reduces the motion ambiguity associated with the aperture problem in computer vision. To compensate for the decay of the tag pattern, a new optical flow algorithm is developed and implemented. The resulting estimated velocity field is then used to recursively update the implied motion reference map over time, thereby tracking the motion of individual particles. If a segmentation of the object is known at the time the tag pattern is created, then an object may be selectively tracked, using the estimated reference map to update the object's position as time progresses. Results are shown for both simulated and actual MR phantom data.
Cardiac motion simulator for tagged MRI

Describes a computational simulator for use in cardiac imaging using tagged magnetic resonance imaging. The simulator incorporates a 13-parameter model of left-ventricular motion due to Arts et al. (1992) and applies it to a confocal prolate spherical shell, resembling the shape of the left ventricle. Using parameters determined in other work, our model can be made to assume a configuration representing one of 60 phases in the cardiac cycle. In this paper we determine the inverse motion map analytically, allowing pointwise correspondences to be made between two points at any two times. Using this mathematical relationship, we simulate the (tagged) magnetic resonance imaging process using a standard (tagged) spin-echo imaging equation. Image sequences can be synthesized at arbitrary orientations at any phase. We currently synthesize a SPAMM tag pattern with arbitrary spatial frequency, but other patterns can be readily incorporated. To accommodate two-dimensional motion estimation algorithms, we have created a two-dimensional simulator which restricts the three-dimensional motion to two dimensions. In either two or three dimensions, a true motion is output so that motion estimation algorithms can be compared against the truth. We conclude with a simple demonstration of the performance of the simulator.
REDMAPS: Reduced-Dimensionality Matching for Prostate Brachytherapy Seed Reconstruction

The success of prostate brachytherapy critically depends on delivering adequate dose to the prostate gland. Intraoperative localization of the implanted seeds provides potential for dose evaluation and optimization during therapy. A reduced-dimensionality matching algorithm for prostate brachytherapy seed reconstruction (REDMAPS) that uses multiple X-ray fluoroscopy images obtained from different poses is proposed. The seed reconstruction problem is formulated as a combinatorial optimization problem, and REDMAPS finds a solution in a clinically acceptable amount of time using dimensionality reduction to create a smaller space of possible solutions. Dimensionality reduction is possible since the optimal solution has approximately zero cost when the poses of the acquired images are known to be within a small error. REDMAPS is also formulated to address the “hidden seed problem” in which seeds overlap on one or more observed images. REDMAPS uses a pruning algorithm to avoid unnecessary computation of cost metrics and the reduced problem is solved using linear programming. REDMAPS was first evaluated and its parameters tuned using simulations. It was then validated using five phantom and 21 patient datasets. REDMAPS was successful in reconstructing the seeds with an overall seed matching rate above 99% and a reconstruction error below 1 mm in less than 5 s.
CT and MRI fusion for postimplant prostate brachytherapy evaluation

Postoperative evaluation of prostate brachytherapy is typically performed using CT, which does not have sufficient soft tissue contrast for accurate anatomy delineation. MR-CT fusion enables more accurate localization of both anatomy and implanted radioactive seeds, and hence, improves the accuracy of postoperative dosimetry. We propose a method for automatic registration of MR and CT images without a need for manual initialization. Our registration method employs a point-to-volume registration scheme during which localized seeds in the CT images, produced by commercial treatment planning systems as part of the standard of care, are rigidly registered to preprocessed MRI images. We tested our algorithm on ten patient data sets and achieved an overall registration error of 1.6 ± 0.8 mm with a running time of less than 20s. With high registration accuracy and computational speed, and no need for manual intervention, our method has the potential to be employed in clinical applications.
Dynamic range of harmonic phase magnetic resonance imaging (HARP-MRI)

Harmonic phase (HARP) magnetic resonance imaging (MRI) is an image analysis method for measuring tissue motion and strain from tagged MR images. Previous studies in cardiac tagged MRI have demonstrated its utility in the characterization of myocardial infarction, ischemia, and the reperfusion of stunned myocardium. Recently HARP has also been used in characterizing the motion of the tongue during speech. In this paper, we characterize dynamic range - limits of elongation, compression and rotation - of HARPMRI. Viewing HARP-MRI as a phase demodulation technique, a key simplifying relationship permits us to derive approximate analytic expressions for these quantities. Simulations demonstrate the validity of our analysis and also help to provide intuition for these results.