nils-herrmann/clean_scirepeval huggingface dataset:
Longitudinal intensity normalization in the presence of multiple sclerosis lesions

This paper proposes a longitudinal intensity normalization algorithm for T1-weighted magnetic resonance images of human brains in the presence of multiple sclerosis lesions, aiming towards stable and consistent longitudinal segmentations. Unlike previous longitudinal segmentation methods, we propose a 4D intensity normalization that can be used as a preprocessing step to any segmentation method. The variability in intensities arising from the relapsing and remitting nature of the multiple sclerosis lesions is modeled into an otherwise smooth intensity transform based on first order autoregressive models, resulting in smooth changes in segmentation statistics of normal tissues, while keeping the lesion information unaffected. We validated our method on both simulated and real longitudinal normal subjects and on multiple sclerosis subjects.
HARP TRACKING REFINEMENT USING SEEDED REGION GROWING

Tagged magnetic resonance (MR) imaging makes it possible to image the motion of tissues such as the muscles found in the heart and tongue. The harmonic phase (HARP) method largely automates the process of tracking points within tagged MR images. It works by finding spatial points in successive images that retain the same two harmonic phase values throughout the entire image sequence. Given a set of tracked points, many interesting and useful motion properties such as regional displacement or rotation, elongation, strain, and twist, can be computed. When there is a large motion between successive image frames, HARP tracking can fail, and this results in mistracked points and erroneous motion estimates. In this paper, we present a novel HARP refinement method based on seeded region growing that addresses this problem. Starting from a given seed point which is determined by the user to be correctly tracked throughout the entire sequence, this method can reliably track the motion of the whole tissue. A novel cost function is used in the region growing to assure that points that can be most reliably tracked are tracked first. Experimental results on tagged MR images of the tongue demonstrate very reliable tracking
Tag and contour detection in tagged MR images of the left ventricle

Tracking magnetic resonance tags in myocardial tissue promises to be an effective tool for the assessment of myocardial motion. The authors describe a hierarchy of image processing steps which rapidly detects both the contours of the myocardial boundaries of the left ventricle and the tags within the myocardium. The method works on both short axis and long axis images containing radial and parallel tag patterns, respectively. Left ventricular boundaries are detected by first removing the tags using morphological closing and then selecting candidate edge points. The best inner and outer boundaries are found using a dynamic program that minimizes a nonlinear combination of several local cost functions. Tags are tracked by matching a template of their expected profile using a least squares estimate. Since blood pooling, contiguous and adjacent tissue, and motion artifacts sometimes cause detection errors, a graphical user interface was developed to allow user correction of anomalous points. The authors present results on several tagged images of a human. A fully automated run generally finds the endocardial boundary and the tag lines extremely well, requiring very little manual correction. The epicardial boundary sometimes requires more intervention to obtain an acceptable result. These methods are currently being used in the analysis of cardiac strain and as a basis for the analysis of alternate tag geometries.
A PDE approach for measuring tissue thickness

We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries. Thickness is defined as the length of trajectories which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) are then solved and combined to yield length without requiring the explicit construction of the trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an upwinding condition. The behavior and performance of the method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification.
Model-Based Tomographic Reconstruction of Objects Containing Known Components

The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.
Reduced-dimensionality matching for 3-D reconstruction of prostate brachytherapy implants from incomplete data

X-ray fluoroscopy is widely used for intra-operative dosimetry in prostate brachytherapy. Three-dimensional locations of the implanted radioactive seeds can be calculated from multiple X-ray images upon resolving the correspondence of seeds. This is usually modeled as an assignment problem that is NP-hard. We propose an algorithm that allows us to derive an equivalent problem of reduced dimensionality based on practical observation that the optimal solution has almost zero cost if the C-arm pose is known. The reduced problem is efficiently solved by linear programming in polynomial time. Additionally, our method solves the hidden seeds problem. Simulation results demonstrate that the implanted seeds can be localized with a matching rate of ≥ 98.8 % and reconstruction error of ≤ 0.37 mm using three images with hidden seeds in a few seconds when the pose of the C-arm is known.
An active contour model for mapping the cortex

A new active contour model for finding and mapping the outer cortex in brain images is developed. A cross-section of the brain cortex is modeled as a ribbon, and a constant speed mapping of its spine is sought. A variational formulation, an associated force balance condition, and a numerical approach are proposed to achieve this goal. The primary difference between this formulation and that of snakes is in the specification of the external force acting on the active contour. A study of the uniqueness and fidelity of solutions is made through convexity and frequency domain analyses, and a criterion for selection of the regularization coefficient is developed. Examples demonstrating the performance of this method on simulated and real data are provided.
Automated reliable labeling of the cortical surface

Accurate and reliable labeling of regions of interest (ROIs) within structural magnetic resonance images (MRIs) is an important step in group analysis. Group registration does not always yield accurate alignment of homologous regions. We present an approach that distinguishes itself from other algorithms by being concerned with a label which is of the highest fidelity, while leaving ambiguous regions unlabeled. Regions that are not deemed to be reliably labeled are not included in group statistics. We will present results showing that our method is an improvement over traditional multi-atlas voting schemes. We conclude with a pilot study of longitudinal trends of cortical thickness in normal aging.
Region of interest cone beam tomography with prior CT data

Recent so-called very short-scan fan-beam reconstruction algorithms make it possible to reconstruct objects within the convex hull of the source path provided that the object is completely contained within the filed of view in all projections. In this work, we extend this result to the truncated cone-beam projection scenario that is typical of mobile C-arms in surgery under the assumption that a prior computed tomography scan is available and that the anatomy has only changed due to surgery within the convex hull of the source path. Practical algorithms and simulation results are given.
Self super-resolution for magnetic resonance images using deep networks

High resolution magnetic resonance (MR) imaging (MRI) is desirable in many clinical applications; however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution (slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution (LR) images to high resolution (HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution (SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.
Real-Time Monitoring of Cardiac Regional Function Using FastHARP MRI and Region-of-Interest Reconstruction

Cardiovascular stress test imaging assists in the diagnosis and monitoring of cardiovascular disease. The procedure can be carried out in a magnetic resonance (MR) scanner using pharmacological agents that mimic the effects of natural exercise. In order to provide real time indication of ischemia, thereby assisting in diagnosis and helping to assure patient safety, it is desirable to have real time monitoring of the myocardial regional function. This paper presents an algorithm for the real time myocardium region-of-interest reconstruction and myocardial strain computation using data acquired from a real time pulse sequence that has been previously reported. The chirp Fourier transform is used for efficient computation, enabling a real-time continuous strain map at a rate of 25 frames/s. Coupled with a real time data path from the scanner to a laptop computer, this algorithm enables real time continuous monitoring of cardiac strain and is targeted for use in the early detection and quantification of ischemia during MR stress tests.
Robust Statistical Fusion of Image Labels

Image labeling and parcellation (i.e., assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability
MIND Demons: Symmetric Diffeomorphic Deformable Registration of MR and CT for Image-Guided Spine Surgery

Intraoperative localization of target anatomy and critical structures defined in preoperative MR/CT images can be achieved through the use of multimodality deformable registration. We propose a symmetric diffeomorphic deformable registration algorithm incorporating a modality-independent neighborhood descriptor (MIND) and a robust Huber metric for MR-to-CT registration. The method, called MIND Demons, finds a deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the integrated velocity fields, a modality-insensitive similarity function suitable to multimodality images, and smoothness on the diffeomorphisms themselves. Direct optimization without relying on the exponential map and stationary velocity field approximation used in conventional diffeomorphic Demons is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, normalized MI (NMI) Demons, and MIND with a diffusion-based registration method (MIND-elastic). The method yielded sub-voxel invertibility (0.008 mm) and nonzero-positive Jacobian determinants. It also showed improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.7 mm compared to 11.3, 3.1, 5.6, and 2.4 mm for MI FFD, LMI FFD, NMI Demons, and MIND-elastic methods, respectively. Validation in clinical studies demonstrated realistic deformations with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.
Angle images for measuring heart motion from tagged MRI

This paper introduces a new image processing technique for rapid analysis and visualization of tagged cardiac magnetic resonance (MR) images. The method is based on the use of isolated spectral peaks in SPAMM-tagged magnetic resonance images. The authors call the phase of an image corresponding to just one of these peaks an angle image, and show that except for a phase-wrapping artifact, an angle image is linearly related to a component of the three-dimensional motion. Using one or more angle images, the authors show how to synthesize conventional tag lines, reconstruct displacement fields for small motions, calculate the optical flow between successive temporal images, and calculate two-dimensional strain. The authors demonstrate the performance of this approach on both real and simulated tagged MR images.
A summary of geometric level-set analogues for a general class of parametric active contour and surface models

Geometric active contours (GAC) and surfaces (GAS) implemented via level set techniques enjoy many advantages over parametric active contours (PAC) and surfaces (PAS), such as computational stability and the ability to change topology during deformation. While many capabilities of earlier PAC and PAS have been reproduced by various GAC and GAS, and while relationships have been discussed for a variety of specific cases, a comprehensive accounting of the connections between these two worlds (particularly regarding rigid forces) has not been consolidated thus far. We present the precise mathematical relationships between the two for an extensive family of both active contour and surface models, encompassing spatially varying coefficients, both tension and rigidity, and both conservative and non-conservative external forces. The result is a very general geometric formulation for which the intuitive design principles of PAC and PAS can be applied. We also point out which type of PAC and PAS methodologies cannot be adapted to the geometric level set framework. We conclude by demonstrating several geometric adaptations of specific PAC and PAS in several simulations.
Limited-angel tomography using constrained sinogram restoration

An algorithm that calculates the maximum a posteriori estimate of the complete sinogram has been developed. It uses prior knowledge of the smoothness of the sinogram, fundamental mathematical constraints on the Radon transform, and a complete probabilistic characterization of the observation noise. The object is reconstructed using convolution backprojection applied to the restored sinogram. The observation that many objects of interest tend to have smooth sinograms, although the objects themselves may not be smooth, has been incorporated by defining a Markov random field prior probability on full sinograms, rather than on objects. The Markov random field used is of the simplest kind-nearest neighbor with quadratic potential terms-although more elaborate models can be used. Using a known noise model (zero-mean, Gaussian), the maximum a posteriori solution to the sinogram restoration problem can be formulated. The solution to this problem is a constrained optimization algorithm, and because of the simple form of both the prior and the observation noise, it was possible to develop an iterative primal-dual algorithm that converges quite rapidly too the desired solution.<<ETX>>
A vector Wiener filter for dual-radionuclide imaging

The routine use of a single radionuclide for patient imaging in nuclear medicine can be complemented by studies employing two tracers to examine two different processes in a single organ, most frequently by simultaneous imaging of both radionuclides in two different energy windows. In addition, simultaneous transmission/emission imaging with dual-radionuclides has been described, with one radionuclide used for the transmission study and a second for the emission study. There is thus currently considerable interest in dual-radionuclide imaging. A major problem with all dual-radionuclide imaging is the "crosstalk" between the two radionuclides. Such crosstalk frequently occurs, because scattered radiation from the higher energy radionuclide is detected in the lower energy window, and because the lower energy radionuclide may have higher energy emissions which are detected in the higher energy window. The authors have previously described the use of Fourier-based restoration filtering in single photon emission computed tomography (SPECT) and positron emission tomography (PET) to improve quantitative accuracy by designing a Wiener or other Fourier filter to partially restore the loss of contrast due to scatter and finite spatial resolution effects. The authors describe here the derivation and initial validation of an extension of such filtering for dual-radionuclide imaging that simultaneously 1) improves contrast in each radionuclide's "direct" image, 2) reduces image noise, and 3) reduces the crosstalk contribution from the other radionuclide. This filter is based on a vector version of the Wiener filter, which is shown to be superior [in the minimum mean square error (MMSE) sense] to the sequential application of separate crosstalk and restoration filters.
Shortest Path Refinement for Motion Estimation From Tagged MR Images

Magnetic resonance tagging makes it possible to measure the motion of tissues such as muscles in the heart and tongue. The harmonic phase (HARP) method largely automates the process of tracking points within tagged MR images, permitting many motion properties to be computed. However, HARP tracking can yield erroneous motion estimates due to 1) large deformations between image frames, 2) through-plane motion, and 3) tissue boundaries. Methods that incorporate the spatial continuity of motion-so-called refinement or flood-filling methods-have previously been reported to reduce tracking errors. This paper presents a new refinement method based on shortest path computations. The method uses a graph representation of the image and seeks an optimal tracking order from a specified seed to each point in the image by solving a single source shortest path problem. This minimizes the potential errors for those path dependent solutions that are found in other refinement methods. In addition to this, tracking in the presence of through-plane motion is improved by introducing synthetic tags at the reference time (when the tissue is not deformed). Experimental results on both tongue and cardiac images show that the proposed method can track the whole tissue more robustly and is also computationally efficient.
Segmentation of the complete superior cerebellar peduncles using a multi-object geometric deformable model

The superior cerebellar peduncles (SCPs) are white matter tracts that serve as the major efferent pathways from the cerebellum to the thalamus. With diffusion tensor images (DTI), tractography algorithms or volumetric segmentation methods have been able to reconstruct part of the SCPs. However, when the fibers cross, the primary eigenvector (PEV) no longer represents the primary diffusion direction. Therefore, at the crossing of the left and right SCP, known as the decussation of the SCPs (dSCP), fiber tracts propagate incorrectly. To our knowledge, previous methods have not been able to segment the SCPs correctly. In this work, we explore the diffusion properties and seek to volumetrically segment the complete SCPs. The non-crossing SCPs and dSCP are modeled as different objects. A multi-object geometric deformable model is employed to define the boundaries of each piece of the SCPs, with the forces derived from diffusion properties as well as the PEV. We tested our method on a software phantom and real subjects. Results indicate that our method is able to the resolve the crossing and segment the complete SCPs with repeatability.