nils-herrmann/clean_scirepeval huggingface dataset:
Dose optimization of SPECT-MPI reconstruction algorithms for perfusion-defect detection

In SPECT-myocardial perfusion imaging (MPI), as in any imaging modality that involves ionizing radiation, minimization of radiation dose to the patient is an important objective. This paper reports the first of a series of studies we are performing to optimize the dose for various reconstruction algorithms, the ultimate aim being to provide dose recommendations, corresponding to optimized algorithm parameters, and performance comparisons across algorithms. Dose optimization involves determination of the extent to which administered activity can be reduced without sacrificing diagnostic accuracy. An important consideration is to conduct the optimizations in a principled way so as to avoid overfitting of the algorithm parameters during optimization. Using clinical SPECT-MPI data with realistic simulated lesions we optimized each reconstruction algorithm for maximum perfusion-defect detection performance, in which each study was evaluated for the presence of perfusion defects by using the total perfusion deficit score computed by the clinically validated Quantitative Perfusion SPECT (QPS) software package. Preliminary results suggest that tracer dose (and hence radiation dose) can be greatly decreased from current clinical dose levels for ordered subsets expectation-maximization OS-EM without significant loss of diagnostic performance compared to reconstruction with filtered-back projection (FBP) and imaging at current dose levels.
New Detectors for Watermarks with Unknown Power Based on Student-t Image Priors

In this paper we present new detectors for additive watermarks when the power of the watermark is unknown. These detectors are based on modeling the image using student-t statistics. As a result, due to the generative properties of the student-t density function, such models are spatially adaptive and the Expectation-Maximization algorithm can be used to obtain maximum likelihood estimates of their parameters. Using these image models detectors based on the generalized likelihood ratio and Rao tests are derived for this problem. Numerical experiments are presented that demonstrate the properties of these detectors and compared them with previously proposed detectors.
Dct/dwt blind multiplicative watermarking through student-t distribution

In this work, which addresses issues related to the efficient hiding of watermark information in the transform domain, we propose to model the transform coefficients with the Student-t distribution through the multiplicative rule of embedding. Based on the observation that the statistical distribution of the transform coefficients has heavy tailed behavior, we design a new class of watermark detectors following the multiplicative rule of embedding. We present experimental results that compare the proposed method with known state-of-the-art multiplicative watermark detectors and demonstrate its effectiveness in terms of sensitivity and robustness.
Effects of Piecewise Spatial Smoothing in 4-D SPECT Reconstruction

In nuclear medicine, cardiac gated SPECT images are known to suffer from significantly increased noise owing to limited data counts. Consequently, spatial (and temporal) smoothing has been indispensable for suppressing the noise artifacts in SPECT reconstruction. However, recently we demonstrated that the benefit of spatial processing in motion-compensated reconstruction of gated SPECT (aka 4-D) could be outweighed by its adverse effects on the myocardium, which included degraded wall motion and perfusion defect detectability. In this work, we investigate whether we can alleviate these adverse effects by exploiting an alternative spatial smoothing prior in 4-D based on image total variation (TV). TV based prior is known to induce piecewise smoothing which can preserve edge features (such as boundaries of the heart wall) in reconstruction. However, it is not clear whether such a property would necessarily be beneficial for improving the accuracy of the myocardium in 4-D reconstruction. In particular, it is unknown whether it would adversely affect the detectability of perfusion defects that are small in size or low in contrast. In our evaluation study, we first use Monte Carlo simulated imaging with 4-D NURBS-based cardiac-torso (NCAT) phantom wherein the ground truth is known for quantitative comparison. We evaluated the accuracy of the reconstructed myocardium using a number of metrics, including regional and overall accuracy of the myocardium, accuracy of the phase activity curve (PAC) of the LV wall for wall motion, uniformity and spatial resolution of the LV wall, and detectability of perfusion defects using a channelized Hotelling observer (CHO). For lesion detection, we simulated perfusion defects with different sizes and contrast levels with the focus being on perfusion defects that are subtle. As a preliminary demonstration, we also tested on three sets of clinical acquisitions. From the quantitative results, it was demonstrated that TV smoothing could further reduce the error level in the myocardium in 4-D reconstruction along with motion-compensated temporal smoothing. In contrast to quadratic spatial smoothing, TV smoothing could reduce the noise level in the LV at a faster pace than the increase in the bias level, thereby achieving a net decrease in the error level. In particular, at the same noise level, TV smoothing could reduce the bias by about 30% compared to quadratic smoothing. Moreover, the CHO results indicate that TV could also improve the lesion detectability even when the lesion is small. The PAC results show that, at the same noise level, TV smoothing achieved lower temporal bias, which is also consistent with the improved spatial resolution of the LV in reconstruction. The improvement in blurring effects by TV was also observed in the clinical images.
Iterative projection algorithms for removing the blocking artifacts of block-DCT compressed images

Iterative projection algorithms for reconstructing visually pleasing images from block discrete cosine transform (BDCT) compressed image data are presented. Two algorithms are proposed. The first is based on the theory of projections onto convex sets (POCS). The second is motivated by the theory of POCS. In order for the POCS approach to be effective, the convex sets must be as pertinent as possible to the required objective. For the deblocking of the DCT compressed images two types of constraint sets capture most of the information pertinent to this problem: the set that contains the information of the transmitted DCT coefficients and the sets that express the between-block smoothness properties of the desired image. The proposed iterative reconstruction algorithms have been tested thoroughly on different DCT-based compression approaches for a number of 256*256 and 512*512 images. The experimental results demonstrate that the proposed algorithms yield superior images to those obtained by direct reconstruction from the compressed data only.<<ETX>>
A periodic optical flow model for cardiac gated images

For the purpose of motion-compensated processing we propose a temporal modeling approach for determining the image motion in a gated cardiac sequence, wherein the inherent image motion is periodic over time. To exploit the periodic nature of the cardiac motion, we use a Fourier harmonic representation to describe the motion field for the entire sequence. We then determine the motion field by estimating the parameters of this representation model. This joint estimation approach can take advantage of the statistics of all the available image data in the sequence. In the experiments, we applied the proposed approach to motioncompensated 4D reconstruction of gated cardiac SPECT images. Our results demonstrate that it could achieve robust estimation of the motion field and lead to improved image reconstruction despite the presence of strong imaging noise.
Bayesian Kernel Methods for Analysis of Functional Neuroimages

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). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. 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). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.
4D reconstruction for dual cardiac-respiratory gated SPECT

Cardiac gated SPECT is an important clinical tool for assessment of both myocardial perfusion and ventricular function. Spatiotemporal (aka 4D) reconstruction has been demonstrated to be effective for suppressing the increased noise in cardiac gated SPECT. In this work, we propose a joint 4D reconstruction approach to accommodate the different respiratory phases in a dual cardiac-respiratory gating scheme in order to combat the artifacts of respiratory motion in cardiac SPECT. The proposed approach is to exploit the correlation in the signal component among both the cardiac and respiratory phases in the acquired data. In our experiments we evaluated the approach using simulated SPECT imaging with the 4D NCAT phantom and Tc-99m labeled Sestamibi as the imaging agent. Our results demonstrate that the proposed approach could effectively suppress the artifacts caused by respiratory motion in the reconstruction.
Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images

The reconstruction of images from incomplete block discrete cosine transform (BDCT) data is examined. The problem is formulated as one of regularized image recovery. According to this formulation, the image in the decoder is reconstructed by using not only the transmitted data but also prior knowledge about the smoothness of the original image, which complements the transmitted data. Two methods are proposed for solving this regularized recovery problem. The first is based on the theory of projections onto convex sets (POCS) while the second is based on the constrained least squares (CLS) approach. For the POCS-based method, a new constraint set is defined that conveys smoothness information not captured by the transmitted BDCT coefficients, and the projection onto it is computed. For the CLS method an objective function is proposed that captures the smoothness properties of the original image. Iterative algorithms are introduced for its minimization. Experimental results are presented.
Recovery of speech spectral parameters using convex set projection

Previous works have demonstrated that by preserving speech spectral "dynamics" during spectral parameter quantization and/or decoding, the quality of coded speech can be improved. We explore the use of projections onto convex sets (POCS) techniques to recover speech spectral parameters from their quantized versions. Unlike prior works, the POCS approach enables us to obtain solutions that satisfy precise constraints. Two constraint sets are used in our POCS recovery algorithm: one set constrains the "roughness" of the parameter trajectories, and the other set confines the parameters to the proper quantizer partition cells. Simulation of our algorithm has consistently produced improvements in both the subjective quality and objective distortion measurements.
Fully 5d reconstruction of gated dynamic cardiac SPECT images using temporal B-splines

In previous work we explored a reconstruction approach based on B-spline modeling for dynamic cardiac images from a gated acquisition in single photon emission computed tomography (SPECT), of which the goal is to obtain a single sequence showing both cardiac motion and kinetic tracer distribution change over time simultaneously. In this work we further develop this approach by extending it to reconstruction of fully five-dimensional (5D) images. Besides quantifying its reconstruction accuracy, we also demonstrate the feasibility of using kinetic information from reconstructed dynamic images with this approach for differentiating defects from normal cardiac perfusion. The proposed approach is evaluated using a dynamic version of the 4D NURBS-based cardiac-torso (NCAT) phantom to simulate a gated SPECT perfusion acquisition with Tc99m Teboroxime. Our results demonstrate that, despite the greatly underdetermined nature of the problem, the 5D procedure can lead to faithful reconstruction of gated dynamic images for both cardiac motion and perfusion defect detection.
Regularized adaptive classification based on image retrieval for clustered microcalcifications

We propose a regularization based approach for efficient, case-adaptive classification in computer-aided diagnosis (CAD) of breast cancer. The goal is to boost the classification accuracy on a query case by making use of a set of similar cases retrieved from an existing library of known cases. In the proposed approach, a regularization scheme in the form a prior derived from an existing baseline classifier is used for the adaptive classifier, which can reduce the extra computational burden associated with adaption of the classifier for a query case. We consider two different forms for the regularization prior. In the experiments the proposed approach is demonstrated on a data set of 1,006 clinical cases. The results show that it could achieve improvements in both numerical efficiency and classification performance.
Textural feature comparison between FFDM and film mammograms

In this work, we conducted an imaging study to make a direct, quantitative comparison of image features measured by film and full-field digital mammography (FFDM). We acquired images of cadaver breast specimens containing simulated microcalcifications using both a GE digital mammography system and a screen-film system. To quantify the image features, we calculated and compared a set of 12 texture features derived from spatial gray-level dependence matrices. Our results demonstrate that there is a great degree of agreement between film and FFDM, with the correlation coefficient of the feature vector (formed by the 12 textural features) being 0.9569 between the two; in addition, a paired sign test reveals no significant difference between film and FFDM features. These results indicate that textural features may be interchangeable between film and FFDM for CAD algorithms.
Design of tapped-delay line antenna array using vector space projections

We design a tapped-delay line (TDL) structured wide-band smart antenna array using vector-space projection (VSP) method. The structure consists of a one-dimensional antenna array with a TDL attached to each antenna. By controlling the weight coefficients at the TDL taps using VSP method, we achieve beamforming the array over wide frequency bands. Constraints and associated projections are furnished to meet the design specifications. We demonstrate the capability of VSP method adapted to solve this array design task.
Detection of Clustered Microcalcifications Using Spatial Point Process Modeling

We propose a spatial point process approach to improve the detection accuracy of clustered microcalcifications (MCs) in mammogram images. The conventional approach to MC detection has been to first detect the individual MCs in an image independently, which are subsequently grouped into clusters. Our proposed approach aims to exploit the spatial distribution among the different MCs directly during the detection process. We model the MCs by a marked point process (MPP) in which spatially neighboring MCs interact with each other. The MCs are then simultaneously detected through maximum a posteriori (MAP) estimation of the model parameters of the MPP process. The proposed approach was evaluated with a dataset of 141 clinical mammograms from 66 cases, and the results show that it could yield improved detection performance compared to a recently proposed SVM detector.
Reconstruction of cardiac-gated dynamic SPECT images

In this work, we propose an image reconstruction procedure which will unify gated SPECT and dynamic SPECT into a single imaging method. Traditionally, gated SPECT and dynamic SPECT are treated as two distinct directions in SPECT imaging: gated SPECT aims to reconstruct a periodic stationary sequence of the cardiac cycle, while dynamic SPECT aims to obtain a time-varying distribution of the radiolabeled tracer during the study. In the proposed reconstruction procedure, 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 list-mode data, this procedure produces an image sequence that shows both cardiac motion and time-varying tracer distribution. To demonstrate the proposed method, we simulated gated cardiac perfusion imaging with Tc-99m labeled Teboroxime using the 4D gated mathematical cardiac-torso (gMCAT) phantom. Preliminary results are presented to demonstrate the feasibility of the proposed approach.
Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications

Computerized detection of clustered microcalcifications (MCs) in mammograms often suffers from the occurrence of false positives (FPs), which can vary greatly from case to case. We investigate how to apply statistical estimation to determine the number of FPs that are present in a detected MC lesion. First, we describe the number of true positives (TPs) by a Poisson–binomial probability distribution, wherein a logistic regression model is trained to determine the probability for an individual detected MC to be a TP based on its detector output. Afterward, we model the spatial occurrence of FPs in a lesion area by a spatial point process (SPP), of which the distribution parameters are estimated from the detections in the lesion and its surrounding region. Furthermore, to improve the estimation accuracy, we incorporate the Poisson–binomial distribution of the number of TPs into the SPP model using maximum a posteriori estimation. In the experiments, we demonstrated the proposed approach on the detection results from a set of 188 full-field digital mammography (FFDM) images (95 cases) by three existing MC detectors. The results showed that there was a strong consistency between the estimated and the actual number of TPs (or FPs) for these detectors. When the fraction of FPs in detection was varied from 20% to 50%, both the mean and median values of the estimation error were within 11% of the total number of detected MCs in a lesion. In particular, when the number of FPs increased to as high as 11.38 in a cluster on average, the error was 2.51 in the estimated number of FPs. In addition, lesions estimated to be more accurate in detection were shown to have better classification accuracy (for being malignant or benign) than those estimated to be less accurate.
Fully 4D reconstruction of gated cardiac images

In this paper we investigate the benefits of a spatiotemporal approach for the reconstruction of cardiac image sequences. To account for the temporal correlations among the gated frames, we introduce a temporal prior based on motion compensation in an image sequence. The image sequences are reconstructed simultaneously through a maximum a posteriori (MAP) framework. To evaluate the performance of our algorithm we used signal to noise ratio, time activity curves, and a channelized Hotelling observer (CHO). We simulated gated cardiac perfusion imaging with Tc-99m-sestamibi using the 4D gated mathematical cardiactorso (gMCAT) phantom. Our experiments show that the proposed algorithm can improve both the reconstruction accuracy of the myocardium and the detectability of perfusion defects without causing significant motion-blurring.
Regularized multichannel recovery of compressed video

In this study we propose a multichannel recovery approach to ameliorating artifacts in compressed video. According to this approach, regularization operators are defined to capture both spatial and temporal correlation properties in an image sequence. In particular a regularization operator is defined explicitly to enforce smoothness along the motion trajectories. In addition, a new approach is proposed for the determination of regularization parameters in the recovery algorithm utilizing the original image in the coder, unlike in traditional image restoration where they have to be estimated from the already degraded image data.
New results on multichannel regularized recovery of compressed video

We present some new results for multichannel recovery of compressed video. This work extends our previously proposed multichannel recovery algorithm in that: (1) it allows bi-directional motion information to be utilised in the recovery algorithm and the motion compensation can be done at a sub-pixel level; (2) a simplified version of the recovery algorithm is introduced to reduce the computational cost. Numerical simulations using H.261 and H.263 compressed video sequences are provided to demonstrate the effectiveness of the proposed algorithms.
Reconstruction of dynamic gated cardiac SPECT

In this paper we propose an image reconstruction procedure which aims to unify gated SPECT and dynamic SPECT into a single method. We will 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 will produce an image sequence that shows both cardiac motion and time-varying tracer distribution simultaneously. To demonstrate the proposed reconstruction methods, we simulated gated cardiac perfusion imaging using the gated mathematical cardiac-torso (gMCAT) phantom with Tc99m-Teboroxime dynamics. Our preliminary 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.
Detectability of perfusion defect in gated dynamic reconstruction images using temporal B-splines

In our recent work we developed a reconstruction approach for cardiac gated dynamic images based on B-spline modeling. The goal is to obtain a single sequence showing simultaneously both cardiac motion and kinetic tracer distribution change over time from one acquisition. In this work, we further develop and demonstrate the feasibility of this procedure for perfusion defect detection in gated dynamic images. We conduct Fisher's linear discriminant analysis and principal component analysis on the reconstructed dynamic image activities, and derive quantitative measures to differentiate defects from normal perfusion. The proposed development is demonstrated using a dynamic version of the 4D NURBS-based cardiac-torso (NCAT) phantom to simulate a gated SPECT perfusion acquisition with Tc99m Teboroxime.