nils-herrmann/clean_scirepeval huggingface dataset:
Detectability of perfusion defect in gated dynamic cardiac SPECT images

Recently we developed an image reconstruction procedure aimed to unify gated imaging and dynamic imaging in nuclear cardiac imaging. It can yield a single image sequence to show simultaneously both cardiac motion and tracer distribution change over the course of imaging. In this work, we further develop and investigate the feasibility of our gated dynamic imaging procedure for perfusion defect detection in cardiac SPECT imaging, where the challenge is even greater without using fast camera rotations. We study the saliency of temporal kinetic information derived from the reconstructed dynamic images for differentiating defects from normal cardiac perfusion. We also propose several metrics to characterize the salient kinetic information in gated dynamic images. The proposed development was demonstrated using simulated gated cardiac imaging with the NCAT phantom and Tc99m-Teboroxime as the imaging agent.
Removal of compression artifacts using projections onto convex sets and line process modeling

We present a new image recovery algorithm to remove, in addition to blocking, ringing artifacts from compressed images and video. This new algorithm is based on the theory of projections onto convex sets (POCS). A new family of directional smoothness constraint sets is defined based on line processes modeling of the image edge structure. The definition of these smoothness sets also takes into account the fact that the visibility of compression artifacts in an image is spatially varying. To overcome the numerical difficulty in computing the projections onto these sets, a divide-and-conquer (DAC) strategy is introduced. According to this strategy, new smoothness sets are derived such that their projections are easier to compute. The effectiveness of the proposed algorithm is demonstrated through numerical experiments using Motion Picture Expert Group based (MPEG-based) coders-decoders (codecs).
Motion-compensated dynamic image reconstruction for gated cardiac SPECT

In this paper we propose a unified image reconstruction procedure for dynamic images from traditional gated SPECT acquisition. We divide the cardiac cycle into a number of gate intervals as in gated SPECT, but treat the tracer distribution for each gate as a time-varying signal. By using dynamic expectation maximization and motion-compensated temporal regularization, our reconstruction procedure can produce an image sequence that shows both cardiac motion and time-varying tracer distribution simultaneously. We simulated gated cardiac perfusion imaging using the gated mathematical cardiac-torso (gMCAT) phantom with Tc99m-Teboroxime dynamics. Our experimental results show that the proposed methods can produce more accurate reconstruction of gated dynamic images than independent reconstruction of individual gate frames with spatial smoothness alone
Total-variation regularized motion estimation in a periodic image sequence

Recently we investigated the use of a Fourier harmonic model for determining the optical flow in a periodic image sequence, the goal of which was to exploit the temporal continuity and periodicity in the underlying motion field. In this work, we further develop this optical flow model by incorporating a spatially piecewise smoothness constraint (in the form of total variation) in order to better accommodate the discontinuity of the motion field at an object boundary. In the experiments, we demonstrate its benefits for noise reduction in motion-compensated 4D reconstruction of cardiac gated images. Our results show that it could lead to more accurate reconstruction of the myocardium in spite of strong imaging noise.
Image segmentation for detection of soft plaques in multidetector CT images

In this paper we aim to develop a computationally- efficient image-segmentation procedure for detection and quantification of soft plaques in coronary arteries from multidetector CT images. The proposed method consists of three steps: extraction of the arterial lumen centerline, segmentation of the lumen and arterial wall separately with locally adaptive region growing, and detection of soft plaques based on effective cross- section areas of the lumen and of the wall. Preliminary results using clinical acquisitions are presented to demonstrate the effectiveness of the proposed method.
Regularized Fully 5D Reconstruction of Cardiac Gated Dynamic SPECT Images

In our recent work, we proposed an image reconstruction procedure aimed to unify gated imaging and dynamic imaging in nuclear cardiac imaging. With this procedure the goal is to obtain an image sequence from a single acquisition which shows simultaneously both cardiac motion and tracer distribution change over the course of imaging. In this work, we further develop and demonstrate this procedure for fully 5D (3D space plus time plus gate) reconstruction in gated, dynamic cardiac SPECT imaging, where the challenge is even greater without the use of multiple fast camera rotations. For 5D reconstruction, we develop and compare two iterative algorithms: one is based on the modified block sequential regularized EM (BSREM-II) algorithm, and the other is based on the one-step late (OSL) algorithm. In our experiments, we simulated gated cardiac imaging with the NURBS-based cardiac-torso (NCAT) phantom and Tc99m-Teboroxime as the imaging agent, where acquisition with the equivalent of only three full camera rotations was used during the course of a 12-minute postinjection period. We conducted a thorough evaluation of the reconstruction results using a number of quantitative measures. Our results demonstrate that the 5D reconstruction procedure can yield gated dynamic images which show quantitative information for both perfusion defect detection and cardiac motion.
Improving perfusion defect detection with respiratory motion compensation in cardiac SPECT

In cardiac perfusion SPECT, respiratory motion can lead to non-uniform blur in the reconstructed myocardium. In addition, due to the irregularity in respiratory patterns, the acquired data with magnitude-binning for respiratory correction can exhibit high variability among both bin intervals and acquisition angles. In this study, we investigate the benefit of a motion correction scheme with acquisition time compensation for the task of perfusion defect detection in clinical acquisitions. We evaluated the detectability of perfusion defects in reconstructed images by using the total perfusion deficit scores obtained from a clinical model observer. The results show that with motion correction the area under the ROC curve is improved from 0.798 (without correction) to 0.835 (p-value < 0.001). We also evaluated the reconstruction scheme with imaging dose reduced to 50%, 25%, and 12.5% of standard dose, and similar improvement is obtained with the correction scheme.
Spatio-temporal MAP reconstruction of gated cardiac images using DFT basis functions

Recently we proposed a reconstruction approach for gated cardiac images based on use of DFT basis functions, in which the temporal activity at each spatial location was regulated by a Fourier representation. In this work we further develop this approach in the context of single photon emission computed tomography (SPECT) which is often hampered by the increased noise due to gating. We will consider both attenuation and scattering correction, which are two important degrading factors in SPECT data acquisitions. For reconstruction we apply a maximum a posteriori (MAP) approach to take into account both the data statistics and a Gibbs smoothing prior. The proposed approach is demonstrated using simulated cardiac perfusion imaging based on the 4D NURBS-based cardiac-torso (NCAT) volumetric phantom with TC-99 labeled sestamibi as the imaging agent.
Case-adaptive decision rule for detection of clustered microcalcifications in mammograms

Microcalcification (MC) detection in mammograms can be hampered by a number of factors ranging from imaging noise to inhomogeneity in breast tissue. Consequently, owning to the variability among subjects in their mammograms, the detection accuracy often varies from case to case even for a well-developed MC detector. To account for this variability, we propose to use a Bayes' risk approach to define the decision rule in the detector output, for which the probability model of the false-positives (FPs) is determined directly from the image under consideration. In the experiment, we demonstrated the proposed approach on a set of 408 mammograms. The results show that it could reduce the FP rate by as much as 44.16% with true-positive rate at 85%.
Effects of piecewise smoothing on cardiac SPECT reconstruction

In this work we investigate the use of total variation (TV) regularization in the context of single photon emission computed tomography (SPECT), which is currently widely used for detection and evaluation of coronary artery diseases. Owing to its piecewise smoothing property, TV regularization is known to preserve discontinuities in the resulting images in inverse problems. Our goal is to study whether such a property will be useful for improving the reconstruction accuracy of perfusion defects in cardiac SPECT, which is an important clinical task. In our evaluation study we simulated cardiac gated SPECT imaging with Tc-99m labeled sestamibi. Our results demonstrate that, compared with a traditional quadratic regularization approach, use of TV regularization could lead to improved accuracy of perfect defect detection even when perfection defects are subtle.
A reversible jump Markov chain Monte Carlo algorithm for analysis of functional neuroimages

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. Using a phantom derived from a neuroimaging study, we demonstrate that the proposed method can estimate more accurately the activation pattern from traditional approaches.
Content-based image retrieval for digital mammography

In this work, we explore the use of a learning-based framework for retrieval of relevant mammogram images from a database, for purposes of aiding diagnoses. A fundamental issue is how to characterize the notion of similarity between images for use in assessing relevance of images in the database. We investigate the use of several learning algorithms, namely, neural networks and support vector machines, in a two-stage hierarchical learning network for predicting the perceptual similarity from similarity scores collected in human-observer studies. The proposed approach is demonstrated using microcalcification clusters extracted from a database consisting of 76 mammograms. Initial results demonstrate that the proposed two-stage hierarchical learning network outperforms a single-stage learning network.
Fully 5D reconstruction of gated dynamic cardiac SPECT images

In our previous work we proposed a unified image reconstruction approach for dynamic images from traditional gated SPECT acquisition. In this approach we divide the cardiac cycle into a number of gate intervals as in gated SPECT, but treat the tracer distribution for each gate as a time-varying signal. In this work we extend this approach to fully five-dimensional (5D) image reconstruction, which can produce a volumetric dynamic image sequence that shows both cardiac motion and time-varying tracer distribution simultaneously. We simulated gated cardiac perfusion imaging using the NURBS-based cardiac-torso (NCAT) phantom with Tc99m-Teboroxime as the imaging agent. Our results show that use of motion-compensated temporal regularization in the proposed approach can lead to much improved reconstruction than using spatial smoothness alone.
Adaptive kernel learning for detection of clustered microcalcifications in mammograms

Adaptive kernel learning is a Bayesian learning technique developed recently, which can be viewed as a variant of the well known relevance vector machine (RVM). The purpose of adaptive kernel learning is to automatically optimize the parameters associated with the kernel basis functions in a predictive model. In this paper, we explore the use of adaptive kernel learning for detection of clustered microcalcifications in mammograms, which is formulated as a two-class classification problem. The proposed approach is tested using a set of clinical mammograms, and compared with an RVM classifier developed previously. It is demonstrated that the adaptive kernel learning classifier can achieve better detection performance than the RVM classifier; it also yields a much sparser model with lower computational complexity.
Towards personalized injected patient doses for cardiac perfusion SPECT imaging: A retrospective study

Reduction of the injected radionuclide dose in myocardial perfusion imaging (MPI) has become a major effort among clinicians and researchers alike. The aim of this study was to retrospectively determine the relationship between injected radioactivity and total projection counts in the heart region of clinically acquired Tc-99m sestamibi patients in an effort to formulate a strategy to better calculate individual and personalized doses prior to injection using easily measured metrics From a pool of 601 patients (304 female) 101 patients that undergone exercise stress testing and were read as having a normal perfusion distribution or small perfusion defects were included for the initial investigation. For all patients, injected activity at the time of the stress myocardial perfusion imaging (MPI) were determined. All patient BMI's were calculated and circumference measurements of the chest, abdomen and hips obtained. For all patients, list mode acquisitions were available and attenuation maps were derived from cone-beam CT acquisitions (BrightView SPECT/CT scintillation camera, Philips, Cleveland, OH). Total projection counts in the heart region were determined using a fixed region-of-interest (ROI) and counts per injected activity plotted against either BMI or chest circumference. Correlations were calculated. Using this information, 25 patients' list mode data were resampled randomly to create acquisition with only a fraction of the counts in the original study such that the heart region counts in all of the patients were equal to ∼750K. Correlations with BMI were again determined using the reduced radioactive dose. The proposed method reduced the radioactive dose by between 29% and 66% while maintaining image fidelity. Therefore, we established an initial strategy to calculate an individual and personalized injected dose for every patient prior to showing up at the MPI clinic using the correlation between BMI and count level in the heart region.
A signal-detection approach for analysis of functional neuroimages

We propose a 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) procedure. The RJMCMC method produces samples from the posterior distribution, which can be used to determine the mode of the distribution. The reversible jumps allow the estimation of a varying number of activation sites, and thus a parameter vector of varying length. Using a phantom derived from a neuroimaging study, we demonstrate that the proposed method can accurately estimate the activation pattern.
Learning of perceptual similarity from expert readers for mammogram retrieval

Image retrieval relies critically on the similarity measure used to compare a query image to a target image in a database. In this work, we explore a similarity measure for mammogram retrieval based on supervised learning from expert readers. This approach is evaluated using data collected from an observer study with a set of clinical mammograms. Our results demonstrate that the proposed machine learning approach can be used to model the notion of similarity as judged by expert readers in their interpretation of mammogram images and that it can outperform alternative similarity measures derived from unsupervised learning
Mammogram Retrieval by Similarity Learning from Experts

A key in content-based image retrieval is the definition of similarity measure for comparing a query image with images in a database. In this work, we explore a similarity measure based on supervised learning from expert readers for mammogram retrieval. We evaluate the approach using an observer study with a set of clinical mammograms. Our results demonstrate that the proposed supervised learning approach can be used to model the notion of similarity by expert readers in their interpretation of mammogram images, and can outperform alternative similarity measures derived from unsupervised learning.
Reversible jump Markov chain Monte Carlo for brain activation detection

A new signal-detection approach for detecting brain activations from PET or fMRI images in a two-state ("on-off") neuroimaging study is proposed. The activation pattern is modeled as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. Also, the number of these functions and their parameters is determined by maximum a posteriori (MAP) estimation. To maximize the posterior distribution, a reversible-jump Markov-chain Monte-Carlo (RJMCMC) algorithm is used. 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. Using a phantom derived from a neuroimaging study, it is demonstrated that the proposed method can estimate more accurately the activation pattern from traditional approaches. Results obtained from real fMRI data are also shown.
Direct reconstruction of parametric images from cardiac gated dynamic spect data

In this work, we propose a direct reconstruction approach to determine a sequence of kinetic parametric images from a single gated acquisition, of which the goal is to provide information simultaneously for both tracer kinetics and cardiac wall motion. The parameter images of the different gates are determined jointly using maximum a posteriori (MAP) estimation from all the available image data in order to exploit the similarity among the different gates. 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 results show that use of gated temporal smoothing could effectively improve the reconstruction accuracy of kinetic parameters.
An image-retrieval aided diagnosis system for clustered microcalcifications

In computer-aided diagnosis (CADx), displaying a set of lesions similar to the one being evaluated has the potential to improve radiologists' diagnostic accuracy. In this work, we investigate whether an automated retrieval CADx system can effectively assist radiologists in diagnosis of clustered microcalcifications (MCs). We first develop a retrieval system for relevant cases by taking into account both perceptually similar image features and the likelihood of malignancy of the lesion under consideration. We then conduct an observer study with a group of 12 breast radiologists to evaluate the diagnostic value of the proposed retrieval system on a set of 100 test cases (50 malignant, 50 benign). Based on receiver-operating characteristic analysis, the results demonstrate that the proposed retrieval CADx system can significantly improve the readers' diagnostic accuracy of MC lesions in terms of both likelihood of malignancy and BI-RAD assessment.
4D reconstruction for gated cardiac SPECT using Fourier basis functions

A challenge in gated cardiac single photon emission computed tomography (SPECT) is the presence of increased imaging noise owing to gated data acquisition. In this study we propose a joint reconstruction approach for gated SPECT in which the different gate frames are reconstructed in a collective fashion by taking advantage of the statistics of the acquired data. Besides spatial smoothing, we use Fourier basis functions to regulate the time activities at each spatial location across the different gates, which are periodic owning to the periodic cardiac motion. We demonstrate the proposed approach by simulating gated Tc-99m labeled sestamibi imaging based on the NURBS-based cardiac-torso (NCAT) phantom.
Tomographic Reconstruction of Gated Data Acquisition Using DFT Basis Functions

In image reconstruction gated acquisition is often used in order to deal with blur caused by organ motion in the resulting images. However, this is achieved almost inevitably at the expense of reduced signal-to-noise ratio in the acquired data. In this work, we propose a reconstruction procedure for gated images based upon use of discrete Fourier transform (DFT) basis functions, wherein the temporal activity at each spatial location is regulated by a Fourier representation. The gated images are then reconstructed through determination of the coefficients of the Fourier representation. We demonstrate this approach in the context of single photon emission computed tomography (SPECT) for cardiac imaging, which is often hampered by the increased noise due to gating and other degrading factors. We explore two different reconstruction algorithms, one is a penalized least-square approach and the other is a maximum a posteriori approach. In our experiments, we conducted a quantitative evaluation of the proposed approach using Monte Carlo simulated SPECT imaging. The results demonstrate that use of DFT-basis functions in gated imaging can improve the accuracy of the reconstruction. As a preliminary demonstration, we also tested this approach on a set of clinical acquisition.
Motion-compensated reconstruction of cardiac images

In this paper we present a new approach for estimating the dynamics (uptake and washout) of a radio-pharmaceutical in the myocardium. To account for the heart motion, the cardiac cycle is divided into a number of intervals as in gated SPECT. Within each gated frame, the time evolution at a pixel is modeled by a spline function. The spline control points are then reconstructed using a Bayesian framework, in which a smoothing prior is defined on all the gated frames based on the motion of the heart. A globally convergent incremental gradient algorithm is used for the optimization step. The proposed algorithm is evaluated using a dynamic version of the 4D gated mathematical cardiac torso (gMCAT) phantom to simulate a gated SPECT perfusion acquisition with Tc/sup 99m/ Teboroxime. Our results demonstrate that the use of motion compensation can improve significantly the accuracy of the reconstruction.