nils-herrmann/clean_scirepeval huggingface dataset:
Tomosynthesis-based radioactive seed localization in prostate brachytherapy using modified distance map images

We have developed a tomosynthesis-based radioactive seed localization method for prostate brachytherapy. In contrast to the projection image-based matching approach, our method does not involve explicit segmentation of seeds and can recover hidden seeds. Modified distance map images are computed from a limited number of x-ray projection images, and are backprojected to reconstuct a 3-D volume of interest. Candidate seed locations are extracted from the reconstructed volume and false positive seeds are eliminated by solving an optimal geometry coverage problem. The simulation results indicate that the implanted seed locations can be estimated from three or four images depending on the number of seeds if the pose of a C-arm is known. The algorithm was validated using phantom and clinical patient data.
A phantom validation of the FastHARP pulse sequence

The FastHARP magnetic resonance imaging pulse sequence, coupled with fast processing, has the potential to measure and display cardiac function in real-time during clinical dobutamine stress tests. In this paper, we present a preliminary validation of the tracking capability of material points using this imaging sequence. Points lying on a mesh embedded in a moving gel phantom are tracked manually using high-resolution images and are simultaneously tracked using HARP tools. The experiment proves that for this particular type of motion actuation, the points tracked in images using the FastHARP sequence followed by fast and automated HARP tracking tools are comparable to results obtained from images acquired in much longer times with very tedious analysis involving manual intervention.
Snakes, shapes, and gradient vector flow

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
Cortical surface reconstruction using a topology preserving geometric deformable model

Accurate reconstruction of the cortical surface of the brain from magnetic resonance images is an important objective in biomedical image analysis. Parametric deformable surface models are usually used because they incorporate prior information, yield subvoxel accuracy, and automatically preserve topology. These algorithms are very computationally costly, however, particularly if self-intersection prevention is imposed. Geometric deformable surface models, implemented using level set methods, are computationally fast and are automatically free from self-intersections, but are unable to guarantee the correct topology. This paper describes both a new geometric deformable surface model which preserves topology and an overall strategy for reconstructing the inner, central, and outer surfaces of the brain cortex. The resulting algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self intersections. We ran the algorithm on 21 data sets and show detailed results for a typical data set. We also show a preliminary validation using landmarks manually placed as a truth model on six of the data sets.
Fully automatic segmentation of the dentate nucleus using diffusion weighted images

We propose a fully automatic method to segment the dentate nucleus using diffusion weighted images (DWI). Initialization of the dentate nucleus is produced by combining the information from tractography results on the diffusion tensor images (reconstructed from DWI) and b0 images. A geometric de-formable model (GDM) with generalized gradient vector flow (GGVF) is then applied on the b0 image to generate the segmentation by evolving the level set function. Experiments have been carried out on real data and quantitative comparison shows that our segmentation results agree well with expert manual delineations and produce accurate results.
Random forest FLAIR reconstruction from T1, T2, and PD-weighted MRI

Inversion Recovery (FLAIR) is a commonly acquired pulse sequence for multiple sclerosis (MS) patients. MS white matter lesions appear hyperintense in FLAIR images and have excellent contrast with the surrounding tissue. Hence, FLAIR images are commonly used in automated lesion segmentation algorithms to easily and quickly delineate the lesions. This expedites the lesion load computation and correlation with disease progression. Unfortunately for numerous reasons the acquired FLAIR images can be of a poor quality and suffer from various artifacts. In the most extreme cases the data is absent, which poses a problem when consistently processing a large data set. We propose to fill in this gap by reconstructing a FLAIR image given the corresponding T1-weighted, T2-weighted, and PD-weighted images of the same subject using random forest regression. We show that the images we produce are similar to true high quality FLAIR images and also provide a good surrogate for tissue segmentation.
Imaging heart motion using harmonic phase MRI

Describes 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 spatial modulation of magnetization (SPAMM)-tagged magnetic resonance images. The authors call the calculated angle of the complex image corresponding to one of these peaks a harmonic phase (HARP) image and show that HARP images can be used to synthesize conventional tag lines, reconstruct displacement fields for small motions, and calculate two-dimensional (2-D) strain. The performance of this new approach is demonstrated using both real and simulated tagged MR images. Potential for use of HARP images in fast imaging techniques and three-dimensional (3-D) analyses are discussed.
C-arm pose estimation using a set of coplanar ellipses in correspondence

C-arms are increasingly being used to assist in a large number of surgical procedures. Fairly accurate and fast pose estimates are needed for non-encoded c-arms that are commonly available in most operating rooms in order to attain quantitative feedback from the x-ray images. We propose the use of an image-based fiducial composed of a set of coplanar ellipses to track the c-arm. We adopt an existing method for planar homography and propose a variation consisting of three modifications: including a weighting scheme for the linear system used, orthonormalizing the vectors pertaining to the rotation component of the transformation, and fine tuning the estimates using a constrained optimization step. We show that these variations make the approach more robust to noise that typically arises in fluoroscopy imaging and guarantee the orthonormality of the estimated rotation. The performance of the modified algorithm is demonstrated using realistic x-ray simulations. We also run sensitivity analysis for segmentation and calibration errors that are likely to occur in a practical setting. Preliminary results show mean tracking accuracy within 0.5° and 0.9 mm for segmentation error variance up to 2 pixels squared. The algorithm also proves to be robust to calibration errors up to 1 cm.
Vessel centerline tracking and boundary segmentation in coronary MRA with minimal manual interaction

Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering proximal to mid segments of coronary arteries necessitates automatic analysis tools requiring minimal manual interactions to robustly measure cross-sectional area along the three-dimensional track of the arteries in under-sampled and non-isotropic datasets. In this work, we present a modular approach based on level set methods to track the vessel centerline, segment the vessel boundaries, and measure transversal area using two user-selected endpoints in each coronary of interest. Arterial area and vessel length are measured using our method and compared to the standard Soap-Bubble reformatting and analysis tool in in-vivo non-contrast enhanced coronary MRA images.
Diffusion Tensor Estimation by Maximizing Rician Likelihood

Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. This can cause quantities derived from these tensors - e.g., fractional anisotropy and apparent diffusion coefficient - to diverge from their true values, potentially leading to artifactual changes that confound clinically significant ones. This paper presents a novel maximum likelihood approach to tensor estimation, denoted diffusion tensor estimation by maximizing Rician likelihood (DTEMRL). In contrast to previous approaches, DTEMRL considers the joint distribution of all observed data in the context of an augmented tensor model to account for variable levels of Rician noise. To improve numeric stability and prevent non-physical solutions, DTEMRL incorporates a robust characterization of positive definite tensors and a new estimator of underlying noise variance. In simulated and clinical data, mean squared error metrics show consistent and significant improvements from low clinical SNR to high SNR. DTEMRL may be readily supplemented with spatial regularization or a priori tensor distributions for Bayesian tensor estimation.
Reconstruction of the human cerebral cortex from magnetic resonance images

Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. Here the authors address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.
Convexity analysis of active contour problems

A general active contour formulation is considered and a convexity analysis of its energy function is presented. Conditions under which this formulation has a unique solution are derived; these conditions involve both the active contour energy potential and the regularization parameters. This analysis is then applied to four particular active contour formulations, revealing important characteristics of their convexity, and suggesting that external potentials involving center of mass computations may be better behaved than the usual potentials based on image gradients. Most importantly, our analysis provides an explanation for the poor convergence behavior at concave boundaries and suggests an alternate algorithm for approaching these types of boundaries.
Tomographic reconstruction of 3-D vector fields

Inner product probe measurements are defined for tomographic reconstruction of 3-D vector fields. General conditions and formulas for reconstruction of an arbitrary vector field are given, as are the conditions and formulas for fields known to be either irrotational or solenoidal. Formulas to extract the irrotational or solenoidal component of an arbitrary vector field using special inner product probe measurements are also developed. It is shown that one set of measurements is required to reconstruct an irrotational field, that two are required to reconstruct a solenoidal field, and that special probes are required to reconstruct the components of an arbitrary field.<<ETX>>
A frequency domain performance analysis of Horn and Schunck's optical flow algorithm for deformable motion

A frequency domain performance analysis of Horn and Schunck's (1981) optical flow (HSOF) algorithm for estimation of deformable motion is presented. Noise sources in the algorithm are modeled using the discrete Fourier transform of the brightness pattern. This noise model along with the estimation error covariance function derived in previous work is used to derive an expression for the expected performance of the optical flow estimate that is valid for an arbitrary discrete brightness pattern. Simulation results are presented that demonstrate the validity of our methods and show that HSOF is more accurate that the optical flow estimate of Anandan for certain low-frequency patterns.
Stochastic models for DIV-CURL optical flow methods

We consider Suter's (see Proc. CVPR94, Seattle, p.939-948, 1994) DIV-CURL optical flow methods, wherein the problem of computing a velocity field from an image sequence is regularized using smoothness conditions based on the divergence and curl of the field. In particular, we develop stochastic formulations of DIV-CURL splines using the linear smoothing theory of Adams, Willsky, and Levy. Our models are shown to be well posed and thus can be used in both simulating and estimating velocity fields having known stochastic properties. As a special case, our stochastic model reduces to that developed by Rougee, Levy, and Willsky (1984) for the classical Horn and Schunck's (1981) optical flow.
Reconstruction of vector fields in bounded domain vector tomography

This paper considers the problem of vector tomography on an arbitrary bounded domain in three dimensions. Previous work has given the formulas for the reconstructed scalar and vector potential functions in relation to the vector field values inside the domain and on the boundary of the domain. Using these formulas. It is shown that the curl-free component can be reconstructed using only one probe measurement, and the divergence-free component can be reconstructed using only two probe measurements. No boundary measurements are necessary.
Hierarchical reconstruction using geometry and sinogram restoration

The authors describe and demonstrate a hierarchical reconstruction algorithm for use in noisy and limited-angle or sparse-angle tomography. The algorithm estimates an 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 estimates are made using a least squares 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 prior shape information. Estimating the position of two support lines from a single projection is accomplished using a generalized likelihood ratio technique for estimating jumps in linear systems. Results for simulated objects in a variety of measurement situations are shown, and several possible extensions to this work are discussed.
Watersheds on the cortical surface for automated sulcal segmentation

The human cortical surface is a highly complex, folded structure. Cortical sulci, the spaces between the folds, define location on the cortex and provide a parcellation into functionally distinct areas. A topic that has recently received increased attention is the segmentation of these sulci from magnetic resonance (MR) images, with most work focusing on the extraction of the sulcal spaces between the folds. Unlike these methods, the authors propose a technique that extracts actual regions of the cortical surface that surround sulci which the authors call "sulcal regions". The method is based on a watershed algorithm applied to a geodesic distance transform on the cortical surface. A well known problem with the watershed algorithm is a tendency towards oversegmentation. To address this problem, the authors propose a post-processing algorithm that merges appropriate segments from the watershed algorithm.
Digital Topology in Brain Imaging

Modeling topology in medical image processing algorithms has emerged as a powerful technique for computing structural representations that are consistent with the underlying anatomy. When applied to high resolution images of the brain, these methods have proven to be extremely beneficial to neuroscientific studies in generating mathematical representations of the cerebral cortex and other brain structures, improving the analysis and visualization of functional activity, and allowing for group comparisons of brain geometry. Topological properties help model the global connectivity of structures without placing a bias on shape. In addition to providing anatomical consistency, topology-preserving algorithms also exhibit an improved robustness to noise. We provide an introduction to the main concepts in digital topology on which these algorithms are based and review their use in the segmentation of magnetic resonance (MR) brain images. Advances in the incorporation of topology constraints within medical image processing algorithms now allow computationally efficient segmentations that are consistent with the underlying anatomy. Modeling of topology complements commonly used models of local smoothness, such as Markov random fields, and statistical models of shape. We believe topological models will eventually reach similar levels of adoption into image processing algorithms as those other models.
Brain image registration based on curve mapping

A new two-stage approach for brain image registration is proposed. In the first stage, an active contour algorithm is used to establish a length-preserving, one-to-one mapping between the cortical and the ventricular boundaries in the two images to be registered. This mapping is used in the second step by a two-dimensional transformation which is based on an elastic body deformation. This method was tested by registering magnetic resonance images to both photographic pathology images and atlas images.<<ETX>>
Adaptive active contour algorithms for extracting and mapping thick curves

Two new adaptive active contour algorithms for the extraction and mapping of the skeleton of a thick curve are described. They are based on conditions which guarantee uniqueness and fidelity of the solution. Both algorithms modify the regularization constant K/sub o/ in an attempt to maintain convexity of the energy function while simultaneously improving the fidelity of the result. The first algorithm changes K/sub o/ over time while the second adapts K/sub o/ spatially. Both algorithms are evaluated on experiments with synthetic curves; both demonstrate an improved performance compared to a fixed-parameter active contour algorithm.<<ETX>>
Convolution backprojection formulas for 3-D vector tomography with application to MRI

Vector tomography is the reconstruction of vector fields from measurements of their projections. In previous work, it has been shown that the reconstruction of a general three-dimensional (3-D) vector field is possible from the so-called inner product measurements. It has also been shown how the reconstruction of either the irrotational or solenoidal component of a vector field can be accomplished with fewer measurements than that required for the full field. The present paper makes three contributions. First, in analogy to the two-dimensional (2-D) approach of Norton (1988), several 3-D projection theorems are developed. These lead directly to new vector field reconstruction formulas that are convolution backprojection formulas. It is shown how the local reconstruction property of these 3-D reconstruction formulas permits reconstruction of point flow or of regional flow from a limited data set. Second, simulations demonstrating 3-D reconstructions, both local and nonlocal, are presented. Using the formulas derived herein and those derived in previous work, these results demonstrate the reconstruction of the irrotational and solenoidal components, their potential functions, and the field itself from simulated inner product measurement data. Finally, it is shown how 3-D inner product measurements can be acquired using a magnetic resonance scanner.
FAST MOTION IMAGING USING REDUCED FIELD OF VIEW PARTIAL FOURIER MRI

Fast MR imaging techniques often exploit the redundancy present in an underlying MR image time series to compensate for k-space undersampling. When imaging motion using techniques like HARP, DENSE, and phase contrast (PC), it is the phase in static regions that is constant and therefore redundant. In this paper, we present a technique that first estimates the phase in the static portion of an undersampled MR image time series and then uses a partial Fourier reconstruction technique to combine phase in the static portion and undersampled data to reconstruct the full image time series. The technique is illustrated using a computational phantom undergoing simulated cardiac motion and imaged using the HARP protocol. Results demonstrate a gradual degradation of accuracy with loss of data due to undersampling, indicating that a 25% speedup in imaging time is possible for an image time series in which 50% of the pixels correspond to the object that do not move over time.
A topology preserving deformable model using level sets

Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models, the ability to automatically handle topology changes, turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology. This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.