nils-herrmann/clean_scirepeval huggingface dataset:
Optical flow estimation in gated cardiac SPECT

In cardiac gated SPECT, image reconstruction suffers from several degrading factors including limited data counts, depth-dependent blur, attenuation, scatter, and motion blur. In our recent work it was demonstrated that use of motion-compensated temporal processing could lead to improved accuracy in 4D reconstruction. In this work we investigate how different optical flow estimation methods can affect the reconstruction in 4D, and in particular, we examine how much room is still left in optical flow estimation toward improving the reconstructed image quality. In our experiments we conducted a thorough, quantitative evaluation using simulated imaging study with multiple noise realizations from the NCAT phantom. As an upper bound, the known motion in NCAT was used for reconstruction. The results show that 4D reconstruction with a periodic optical flow model can achieve results almost matching that from the known motion.
4D reconstruction of cardiac images using temporal fourier basis functions

In cardiac imaging the data acquisition is often gated according to the ECG signal at the expense of signal to noise ratio in order to deal with cardiac motion. In this work, we propose a spatio-temporal reconstruction procedure by using a Fourier harmonic representation model for the temporal image activities. To further combat the increased imaging noise, we also include a spatial smoothing prior into the reconstruction process. We then reconstruct the different harmonics in the representation model by using both a penalized least-squares approach and a maximum a posteriori approach. In our experiments, the proposed approach was demonstrated using simulated cardiac SPECT perfusion imaging based on the NURBS-based cardiac-torso (NCAT) phantom.
Optimizing motion correction in reconstruction of respiratory-gated spect

Respiratory-gated acquisition with amplitude binning in cardiac SPECT can reduce the extent of motion blur, but the acquired data can exhibit high variability among both gate intervals and acquisition angles. We investigate whether we can improve the reconstruction accuracy by optimizing the motion correction among respiratory gates in the presence of such variability. To account for the differing noise characteristics among respiratory gates, we develop a joint motion-estimation and image-reconstruction approach, in which the respiratory motion is estimated simultaneously along with the source distribution. In the experiments, we demonstrated this joint estimation-reconstruction approach with both quantitative simulated NCAT data and a set of clinical acquisition. We also explored its robustness with reduced imaging dose. The results show that the proposed approach can further improve the reconstructed myocardium over a pre-reconstruction motion-estimation approach.
A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications

In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (A/sub z/=0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (A/sub z/=0.80).
Improving SVM classifier with prior knowledge in microcalcification detection1

This work aims to explore whether we can improve the accuracy of an SVM classifier for microcalcification (MC) detection by incorporating prior knowledge of MCs in mammograms. Based on the fact that MCs are inherently invariant to their spatial orientation in a mammogram, we consider two different techniques for incorporating rotation invariance into SVM, of which one is virtual support vector SVM (VSVM) and the other is tangent vector SVM (TV-SVM). The experiment results show that both techniques can improve the performance in discriminating MCs from the image background, and TV-SVM achieved the best performance. In particular, the sensitivity was 96.3% for TV-SVM, compared to 94.5% for SVM, when the false positive rate was at 0.5%.
Online learning of relevance feedback from expert readers for mammogram retrieval

In content-based image retrieval (CBIR) relevance feedback schemes have been studied as a means to boost the retrieval performance in recent years. Despite the efforts in development of efficient algorithms for retrieving desired images from image databases, there often remains a gap between low-level image features and high-level semantic understanding in CBIR systems. In this paper, we investigate a technique based on online learning by relevance feedback for retrieval of mammogram images that contain perceptually similar lesions with clustered microcalcifications. Our approach applies support vector machine (SVM) regression for supervised learning and employs the concept of incremental learning to incorporate relevance feedback online. The proposed approach is demonstrated using a database of 200 mammogram images with clustered microcalcifications scored by experienced radiologists.
Projection-based spatially adaptive reconstruction of block-transform compressed images

At the present time, block-transform coding is probably the most popular approach for image compression. For this approach, the compressed images are decoded using only the transmitted transform data. We formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. Numerical experiments are shown that demonstrate that the proposed algorithms work better than both the JPEG deblocking recommendation and our previous projection-based image decoding approach.
Reconstruction of respiratory-binned cardiac spect using a robust smoothing prior

Cardiac images in single photon emission computed tomography (SPECT) are known to suffer from loss of resolution associated with respiratory motion. In this work, we investigate the use of a spatially adaptive smoothing prior in a motion-compensated reconstruction framework for SPECT, wherein respiratory binned data are incorporated into the reconstruction of the myocardium with respect to a reference respiratory bin. In the experiments, we evaluated this approach with both simulated imaging data and two sets of clinical acquisitions. The results show that the proposed approach can be effective for improving the heart wall in terms of both the noise level and spatial resolution. The proposed approach was also demonstrated to be robust when the imaging dose was reduced.
Reconstruction with angular compensation in respiratory-gated cardiac SPECT

In respiratory-gated cardiac SPECT with amplitude binning, the acquisition time can vary greatly both among respiratory gates and among acquisition angles within each gate. If not properly accounted for, this uneven distribution in acquired data statistics will lead to limited-angle artifacts in reconstruction, which in turn can impact on the accuracy of respiratory motion correction. We investigate a compensation scheme for this uneven distribution by directly taking into account in the imaging model the actual acquisition time at different projection angles. In the experiment, we demonstrated this approach with simulated NCAT imaging data, for which quantitative results were obtained on both the reconstructed myocardium and estimated motion; we also tested the proposed approach on a set of clinical acquisition. The results show that the proposed approach could effectively suppress the limited-angle artifacts and improve the reconstruction in terms of both image accuracy and lesion detectability.
Temporal regularization in fully 5D reconstruction of cardiac gated dynamic SPECT images

Recently we developed two image reconstruction approaches aimed to unify gated imaging and dynamic imaging in nuclear cardiac imaging, the goal being to obtain an image sequence from a single acquisition which shows both cardiac motion and tracer distribution change over the course of imaging. While both approaches exploit the similarity among the different cardiac gate intervals, they differ in the form of temporal regularization imposed during reconstruction. In this work, we provide a quantitative comparison of these two approaches in terms of reconstruction accuracy of the myocardium using several quantitative measures, including signal-to-noise ratio, time activity curves, and ability for perfusion defect detection. In our experiments, we simulated gated cardiac imaging with the NURBS-based cardiac-torso phantom and Tc99m-Teboroxime as the imaging agent. The results show that both approaches could yield similar overall accuracy in the myocardium, and the B-spline approach could achieve slightly better estimation of the time activities compared to the dynamic EM approach.
Edge-preserving reconstruction of compressed images using projections and a divide-and-conquer strategy

In this paper we present a new non-linear image recovery algorithm which is based on the theory of projections onto convex sets (POCS) to reconstruct compressed images. We introduce a new family of convex smoothness constraint sets. These sets are based on the concept of the line process which models explicitly the edge structure of images and thus can eliminate both ringing and blocking coding artifacts. We also introduce a divide-and-conquer (DAC) strategy to compute the projections onto the new smoothness sets efficiently. Finally, we present experiments that demonstrate the effectiveness of the new smoothness constraint sets.<<ETX>>
Preliminary investigation of a multiple-image radiography method

Conventional radiography seeks to produce, at each location in the image plane, a measure of the X-ray absorption of the object. In this paper, we present a method of determining a much more complete object description at each point, namely the intensity distribution (as a function of angle) of the emerging X-ray beam. From this distribution one can compute many different images, depicting the refraction, absorption, and scattering characteristics of the object. The method is based on acquisition of multiple X-ray images using monochromator and analyzer crystals, followed by a deconvolution operation (over angle) at each pixel. Parameters of the deconvolved angular intensity function are measured to obtain the various images of interest. We present experimental results obtained using X-rays produced by a synchrotron, along with simulated images showing the effect of noise in a hypothetical photon-limited imaging situation.
Wide-band smart antenna design using vector space projection methods

We use vector space projection (VSP) methods to design wide-band adaptive and self-healing arrays. Rectangular arrays are assumed but the VSP algorithm can be applied to any configuration. In the VSP method, we formulate a set of design constraints and then iteratively improve on a trial solution by operations known as "alternating projections". When all of the constraint sets are convex and the intersection of these sets is not the empty set, we can meet the design specifications in a finite dimensional setting. In our simulations, we show that reasonable design constraints are readily met. We demonstrate that VSP is useful for broad-band self-healing, i.e., the reconfiguration of the array when some broad-band elements fail to operate. Finally we compare our results with a known design procedure for broadband antenna arrays.
A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.
4D reconstruction of cardiac SPECT using a robust spatialtemporal prior

Cardiac gated images in single photon emission computed tomography (SPECT) are known to suffer from increased noise due to low data counts. In this work, we investigate a 4D reconstruction approach based on an adaptive spatiotemporal smoothing prior, which is used to exploit the common signal component among the different cardiac gates in a sequence. In the experiments, we evaluated this approach with both simulated NCAT imaging data and two sets of clinical acquisitions. The results demonstrate that the proposed 4D approach can be more effective for improving the heart wall in terms of both noise levels and spatial resolution than motion-compensated 4D reconstruction. The proposed approach was also found to be robust for noise suppression when the imaging dose was reduced.
Relevance vector machine for automatic detection of clustered microcalcifications

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique - relevance vector machine (RVM) - for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.
Gated dynamic image reconstruction using temporal B-splines

In this work we develop a fully five-dimensional (5D) reconstruction approach based on B-spline modeling for cardiac gated dynamic images. The goal is to obtain a single sequence showing simultaneously both cardiac motion and kinetic tracer distribution change over time from one acquisition. The proposed approach is demonstrated in the context of single photon emission computed tomography (SPECT), where the challenge is great due to the under-determined nature of the problem and increased imaging noise. Our evaluation results demonstrate that the 5D reconstruction procedure can lead to accurate reconstruction of gated dynamic images for both cardiac motion and perfusion defect detection.
Least-squares mesh model for image compression

In this work we explore the use of a content-adaptive mesh model for image compression. We first model the image to be compressed by a quadtree mesh representation, in which the nodal values are determined using a least squares fit. The resulting mesh structure is coded using a 4-ary tree and the mesh nodal values are coded using a hierarchical predictive coding scheme. Our experimental results demonstrate that the proposed approach can achieve good compression performance and can significantly outperform JPEG both subjectively and objectively in low bit-rate applications.
Mammogram retrieval based on incremental learning

In this work we explore a relevance feedback approach in a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. Our goal is to adapt online the learning procedure in accordance with a user's response without the need to repeat the training procedure. Toward this end we develop a relevance feedback approach based on the concept of incremental learning recently developed in the theory of support vector machines. The proposed approach is demonstrated using clustered microcalcifications extracted from a database consisting of 76 mammograms.
Compensation of acquisition variations in respiratory-gated SPECT with joint statistical reconstruction

In respiratory-gated cardiac SPECT with amplitude binning, the acquired data counts can vary greatly both between respiratory gates and among acquisition angles within each gate, which would lead to artifacts in reconstruction if not properly accounted for. In this work, we investigate a joint statistical reconstruction approach in which the different respiratory gates are described with respect to a common reference gate and the variation in the acquired data is compensated according to the acquisition time at each acquisition angle and respiratory gate. In the experiment, we demonstrated this approach with both simulated NCAT imaging (for quantitative evaluation) and a set of clinical acquisition. The results show that the proposed approach can further improve the reconstruction accuracy of the myocardium when compared to a post-reconstruction motion-compensation scheme developed previously.
Reversible jump Markov chain Monte Carlo signal detection in functional neuroimaging analysis

We propose a new signal-detection approach for detecting brain activations from PET or fMRI images in a two-state ("on-off') neuroimaging study. We model the activation pattern as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. We determine the number of these functions and their parameters by maximum a posteriori (MAP) estimation. To maximize the posterior distribution we use a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm. The main advantage of RJMCMC is that it can estimate parameter vectors of unknown length. Thus, in the model used the number of activation sites does not need to be known. We evaluate the performance of the algorithm on synthetic data using ROC curves and on real fMRI data using the NPAIRSresampling framework.
Kernel Methods for Functional Neuroimaging Analysis

We propose an approach to analyzing functional neuroimages in which: (1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data; and (2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). In an on-off design we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting).