nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of the internal noise models for channelized Hotelling observer

The channelized Hotelling observer (CHO) has become a widely used approach for evaluating medical image quality, acting as a surrogate for human observers in early-stage research on assessment and optimization of imaging devices and algorithms. Its popularity stems from experiments showing that the CHOs detection performance can be tuned to correlate well with human observers detection performance. Typically, this tuning is achieved introducing an internal-noise model tuned on example data obtained from human observers. Thus, it can be argued that internal-noise model tuning step is essentially a model training exercise; therefore, just as in supervised learning, it is essential to test the CHO model on a set of data that is distinct from that used to tune the model. Furthermore, the test data should be significantly different from the training data if the CHO is to provide useful insight about new imaging algorithms or devices. Motivated by these considerations, in this work a train-test approach was proposed, with new models selection criterions, and used to evaluate ten established internal noise models utilizing four (here presenting only one) different channel models. I also propose a new internal noise model (although not the main intention of this work) which I, incidentally, find outperforms the ten established models. The results show that the proposed train-test approach is necessary, with the newly proposed model selection criterions, for avoiding spurious conclusions. It was also demonstrate that, in some CHO models, the optimal internal-noise parameter is very sensitive to the choice of training data; therefore, these models are prone to overfitting, and will not likely generalize well to new data.
GPU based calculation of a SPECT projection operator for content adaptive mesh model

In this paper we explore the use of a graphical processing unit (GPU) in a fast calculation of a projection operator using a ray casting algorithm for a content-adaptive mesh model (CAMM). Previously we had introduced 2D and 3D tomographic image reconstruction using a CAMM for emission tomography (EM). The proposed GPU based projection operator calculation is fast and allows incorporation of a non-uniform attenuation and distance-dependent spatial resolution of the imaging system. The GPU allows for fast computation, therefore establishing a necessary step for the future practical development of a CAMM tomographic reconstruction.
Image restoration using content-adaptive mesh modeling

In this work we explore the use of a content-adaptive mesh model (CAMM) in the classical problem of image restoration. In the proposed framework, we first model the image to be restored by an efficient mesh representation. A CAMM can be viewed as a form of image representation using nonuniform samples, of which the mesh nodes (i.e., image samples) are adaptively placed according to the local content of the image. The image is then restored through estimating the model parameters (i.e., mesh nodal values) from the data. A CAMM provides a natural spatially-adaptive regularization mechanism for image restoration in that the interpolation basis functions in a CAMM have their spatial support varying with the local image content. We present some exploratory results to demonstrate the proposed approach.
Learning based prior for analyzer-based phase contrast image reconstruction

Maximum a posteriori (MAP) method for image reconstruction is subjected to an appropriate selection of the prior distribution. In this paper, we introduce a new approach to estimate the prior distribution using a machine learning scheme based on Relevance Vector Machine (RVM). The RVM prior is applied to the Analyzer-based Imaging (ABI) reconstruction problem. ABI is a technique capable of measuring very subtle X-ray deflection and scatter phenomena when passing through an imaged object producing three parametric images (Absorption, Refraction and ultra-small angle scatter USAXS). The need of a quasi-monochromatic and highly collimated beam causes an extremely low photon count in the ABI systems detector, which leads to noisy reconstructions. Here we demonstrate the use of RVM priors to improve the resulting ABI images.
Task based assessment of cardiac function in Monte Carlo simulated gated Tl-201 perfusion SPECT: A human observer study

Different implementations of the receiver operator characteristic (ROC) method in cardiac perfusion SPECT have been described. However, none has attempted to independently assess cardiac function. The aim of this study was, therefore, to design and execute a human observer ROC study that includes the evaluation of cardiac function and perfusion. Due to the lack of a gold standard, our initial design used an improved Mathematical Cardiac Torso (MCAT) phantom to generate normal and abnormal regional function. Abnormal heart function included hypokinesis, akinesis, and dyskinesis. Two heart sizes (124 ml and 100 ml left ventricular cavities) were used for male and female patients, respectively. Eight different locations around the LV myocardium were selected and perfusion defects of various sizes generated. Sixteen gated hearts across the cardiac cycle with abnormal cardiac function according to the different motion models were generated. The SIMIND Monte Carlo package was used to simulate a clinical Tl-201 perfusion SPECT acquisition protocol on the 3-headed IRIX gamma camera (Philips Medical Systems, Cleveland, Ohio). Data were reconstructed using the rescaled block iterative (RBI) technique with 17 subsets (4 projections/subset) and 5 iterations. Three sets of sixty-five cases were shown to an observer, the first 10 cases were used as training while the remainder (55 cases) were read and scored. The readings of the total 165 observed cases served as input data for ROC curve generation. The observer read the data in two ways. In the first reading the observer gave a confidence rating for regional myocardial function followed by a separate confidence rating for a perfusion defect. In the second reading, the order was reversed. ROC curves and areas under curve (AUCs) were determined separately for each reading approach. ROCs and AUCs were calculated for the three coronary artery territories as well as an overall calculation for the heart. While several ideas to further improve the methodology were generated during this study, we believe that we have clearly demonstrated that one can perform an independent quantitative task based assessment of cardiac function.
4D smoothing of gated SPECT images using a left-ventricle shape model and a deformable mesh

We present a new 4D smoothing method for cardiac-gated SPECT. The method uses motion-compensated spatio-temporal filtering, based on motion estimated using a global left-ventricular shape model, and a deformable mesh, which accounts for myocardial brightening. The high noise levels in gated SPECT can cause difficulties for motion estimation methods that are based on local information, such as optical-flow techniques. In the proposed approach, we mitigate these problems by using a global shape model of the left ventricle (LV). This model is used to generate a volumetric mesh which is deformed from frame to frame to track myocardium motion through time. As opposed to surface based motion estimation, which follow the motion trajectories of points on the LV surface, our approach is a volumetric method, which aims to follow points within the myocardium. Therefore the method is explicitly designed to track and maintain the relative position of myocardial tissue elements. In addition, the proposed motion estimation allows incorporation of myocardial brightening, which is an artifact of the partial volume artifact, but plays a useful role in clinical assessment of wall thickening. Clinical SPECT data are used in this preliminary study to demonstrate the proposed method.
A Naive-Bayes model observer for a human observer in detection, localization and assessment of perfusion defects in SPECT

In medical imaging, it is widely accepted that image quality should be assessed through the performance of human observers at some diagnostic task. For these evaluations, mathematical algorithms known as model observers (MOs) are often used as a substitute for human observers in early stages of image reconstruction algorithm development. For SPECT-MPI (myocardial perfusion imaging), diagnostic tasks involve detection, localization and assessment of regions with abnormal myocardial perfusion. In this work we propose a new MO for these tasks. The proposed MO is based on a machine-learning algorithm known as Naive-Bayes classifier (NB-MO). In the proposed approach, NB-MO is applied over a set of image features extracted from SPECT polar maps, aiming to predict perfusion scores given by humans for each myocardium region (segment). Next we compute average MO performance by first directly predicting individual human observer scores, and then using multi reader alternative free-response analysis (AFROC) in the same fashion as used on human observers. Our human observer study was comprised of five experienced (physicians) readers who scored location and severity of perfusion defects in 179 simulated SPECT-MPI cases and two different reconstruction methods (FBP and OSEM). The presented results show good performance agreement between humans and the proposed NB-MO, as well as excellent generalization properties of NB-MO between different reconstruction methods.
Deformable mesh model of cardiac motion from tagged MRI data

In this work we present a motion analysis for cardiac-gated tagged MRI data of the left ventricle (LV) of the heart. The dense field motion analysis is based on a deformable mesh model (DMM) that incorporates a heart surface model. The motion field is derived using intensity based image matching. DMM is well suited for motion tracking and analysis of organs undergoing non-rigid deformation. Previously, we successfully utilized DMM for heart motion tracking in cardiac SPECT image reconstruction. Preliminary results presented here for tagged MRI demonstrate further potential of this approach.
TOMOSYNTHESIS IMPLEMENTATION OF MULTIPLE IMAGE RADIOGRAPHY

Multiple-image radiography (MIR) is an analyzer-based phase sensitive X-ray imaging method, which simultaneously generates three planar images containing information about absorption, refraction and scattering properties of the object. These three planar images are linear projections of the corresponding properties inside the object. The linearity of the projections allows reconstruction of volumetric images by computed tomography (CT) methods. In this work, we explore the use of tomosynthesis, a limited-angle tomography method, to reconstruct volumetric images of the object. We investigated the accuracy of reconstructions as a function of the number of angular views when the total object radiation exposure is fixed
Learning a nonlinear channelized observer for image quality assessment

We propose two algorithms for task-based image quality assessment based on machine learning. The channelized Hotelling observer (CHO) is a well-known numerical observer, which is used as a surrogate for human observers in assessments of lesion detectability. We explore the possibility of replacing the linear CHO with nonlinear algorithms that learn the relationship between measured image features and lesion detectability obtained from human observer studies. Our results suggest that both support vector machines and neural networks can offer improved performance over the CHO in predicting the human-observer performance.
Estimation of Left Ventricular Motion from Cardiac Gated Tagged MRI Using an Image-Matching Deformable Mesh Model

In this paper, we propose a deformable mesh model (DMM) method for left-ventricular (LV) motion-estimation from cardiac-gated tagged magnetic resonance imaging (MRI). DMM starts by partitioning short-axis (SA) and long-axis (LA) images at end-diastole (ED) in a collection of non-overlapping mesh elements (triangles). Next, mesh elements are deformed attempting to track temporal changes in pixel intensities by minimizing an image-matching cost function from frame to frame. Finally, dense motion fields can be obtained at any frame from the displacement of mesh nodes, without any additional user interaction. For validation, 23 human tagged MRI studies were used, allocated as 7 normal, 9 hypertensive (HTN), and 7 with mitral regurgitation (MR). DMM end-systolic (ES) motion and strains were quantitatively compared to a “gold-standard” feature-based (FB) motion analysis method that includes manual tagline editing. DMM motion achieves subpixel average displacement error of SA landmarks with respect to FB. 3-D ES strains obtained from DMM correlate well with FB method ( $\rho $  ranging from 0.79 to 0.97). Finally, we show temporal curves for several DMM strain components, grouped according to the condition of patients (normal and diseased). These curves are qualitatively compared to a strain from unwrapped harmonic phase motion estimation algorithm. DMM produces robust temporal curves, which capture strain fluctuations reported in medical literature for HTN and MR conditions. This makes it a viable alternative to analyze motion from images at phases suffering from low contrast-to-noise ratio that may convey important information about the presence of certain cardiac pathologies.
Motion compensated spatio-temporal filtering of cardiac gated SPECT images

Here we describe a post-reconstruction motion-compensated spatio-temporal processing algorithm for cardiac gated SPECT using estimated myocardium motion. In SPECT systems, radioactive dose limitations result in low photon count and therefore, reconstructed images are corrupted by noise. This is even more so in gated SPECT where the data is divided in a number of time frames. Existing noise reduction methods include spatio-temporal filtering, however, temporal filtering usually does not account for the heart motion thus introducing an artifact known as motion blur. This paper presents a motion-compensated temporal filtering method along with a motion estimation algorithm. Motion estimation is performed using a deformable mesh model, fitted to the left ventricle, which can track the myocardium displacement between the time frames. Temporal filtering is then applied utilizing the estimated myocardium displacement. Additionally, both motion estimation and temporal filtering account for the partial volume effect which is manifested by an image brightening effect when the heart wall thickens. Thickening is an important clinical indicator and as such has to be preserved by temporal filtering. The presented quantitative assessments demonstrate that the proposed method provide better images while preserving the brightening effect in comparison to methods used in clinical practice.
Imaging in sitting position may reduce liver artifact in myocardium perfusion imaging

In this work we evaluated liver artifacts in SPECT myocardium perfusion imaging. Liver artifact occurs due to the liver-heart proximity and is manifested as an apparent increase or decrease in radioactive uptake in myocardium. The main motivation for the work is that there are now SPECT imaging devices on the market that image patients in an upright seated position, as opposed to the conventional supine position. The upright position may aid in removal of the artifact during reconstruction by causing the liver to sag downward, thus increasing liver-heart distance. In this work we investigate whether this change of the liver-heart distance can result in more accurate image reconstructions, and thus lower myocardium defect misdetection rates. To investigate this we use the realistic NCAT phantom to simulate the human torso, and SIMIND simulation code.
Machine learning based parametric image estimation for Analyzer-based phase contrast imaging

An X-ray beam passing through biological tissue is deflected (i.e., refracted) by a small angle typically <;10 μrad. Analyzer-based phase contrast imaging (ABI) systems are capable of measuring this tinny refraction by sampling the intensity of the beam at different propagation directions. An Analyzer crystal is the key element for this task as it acts as a narrow angular filter. Since refraction effects are highly dependent of the radiation wavelength, X-ray beam must be quasi-monochromatic. Therefore the amount of photons that reach the object and detector is much lower then that in traditional radiography. Using a reasonable exposure time, noisy reconstructions of refraction images are obtained. In this manuscript, we present a machine learning parametric image estimation approach to obtain accurate refraction images from noisy raw data.
A fast approach for accurate content-adaptive mesh generation

Mesh modeling is an important problem with many applications in image processing. A key issue in mesh modeling is how to generate a mesh structure that well represents an image by adapting to its content. We propose a new approach to mesh generation, which is based on a theoretical result derived on the error bound of a mesh representation. In the proposed method, the classical Floyd-Steinberg error-diffusion algorithm is employed to place mesh nodes in the image domain so that their spatial density varies according to the local image content. Delaunay triangulation is next applied to connect the mesh nodes. The result of this approach is that fine mesh elements are placed automatically in regions of the image containing high-frequency features while coarse mesh elements are used to represent smooth areas. The proposed algorithm is noniterative, fast, and easy to implement. Numerical results demonstrate that, at very low computational cost, the proposed approach can produce mesh representations that are more accurate than those produced by several existing methods. Moreover, it is demonstrated that the proposed algorithm performs well with images of various kinds, even in the presence of noise.
Content-adaptive mesh modeling for fully-3D tomographic image reconstruction

We propose the use of a content-adaptive volumetric mesh model for fully three-dimensional (3D) tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first modeled by an efficient mesh representation. The image is then obtained through estimation of the nodal values from the measured data. The use of a mesh representation can alleviate the ill-posed nature of the reconstruction problem, thereby leading to improved quality in the reconstructed images. In addition, it reduces the data storage requirement, resulting in efficient algorithms. The proposed methods are tested using gated cardiac-perfusion images. Initial results demonstrate that the proposed approach achieves good performance when compared to several commonly used methods for image reconstruction, and produces results very rapidly.
Four-dimensional gated cardiac SPECT reconstruction and evaluation study

Recently we proposed a motion-compensated approach for four-dimensional (4D) reconstruction for gated cardiac single photon emission tomography (SPECT), and demonstrated that motion-compensated gate smoothing can be effective for noise reduction in the reconstructed images. In this paper we further develop our 4D approach by including attenuation and scatter compensation and evaluate it based on a more realistic Monte Carlo simulation and a set of patient data. Our results from both the simulated and patient data demonstrate that the 4D method can lead to improved reconstruction.
Noise analysis for diffraction enhanced imaging

Herein we present a quantitative noise analysis of diffraction enhanced imaging (DEI), an X-ray imaging method that produces absorption and refraction images, with inherent immunity to wide-angle scatter. DEI produces remarkable images, but requires an X-ray source of very high power; therefore, it has principally been confined to synchrotron studies. Clinical systems currently under development using conventional X-ray sources will be photon-limited. Therefore, it is important that the noise properties of DEI be understood. Herein, we show that the original formulation of DEI, given by Chapman, et al, is the maximum-likelihood solution of the image-estimation problem for the case of Poisson noise. We derive the mean, covariance and signal-to-noise ratio of the images produced by this method, which sheds light on the effect of system parameters on the computed images. We will use these results in future work to derive reconstruction methods that are more optimal in the presence of noise than the original DEI formulation.
Estimation of myocardial strain from MRI image sequence using a deformable mesh model

Analysis of myocardial strain plays an important role in cardiac MRI, since fluctuations in the strain pattern of the heart may be a sign of cardiac abnormalities. In order to extract strain information, cardiac deformation must be estimated from a set of cardiac-gated MR images. In previous works, we developed a deformable mesh model (DMM) to describe 3D myocardial motion from MRI. In DMM, left ventricle motion was estimated by deforming a 3D mesh to match with inter-frame pixel-intensity variations of a combined gated tagged and untagged MRI image sequence of the heart. In this work, from an estimated DMM we compute myocardial 3D strain maps by applying a prolate spheroidal B-spline model (PSB), and we compare it to a reference model which uses an active-contour (i.e. snakes) tracking algorithm. Furthermore, we show a comparison of DMM-based strain measurements (torsion) obtained from healthy and abnormal MRI datasets, showing the diagnostic potential of the DMM technique.
Accurate mesh representation of vector-valued (color) images

In this work we present a fast procedure for content-adaptive mesh representation of vector-valued (e.g., color) images. The goal is to obtain a single mesh structure that accurately represents all the individual components of the image. The proposed method is justified by an error bound that is rigorously derived for such a representation. It employs an error-diffusion type algorithm to place the mesh nodes nonuniformly in the image domain according to the image content. Experimental results demonstrate that: (1) a compact and accurate representation for color images can be achieved at low computational cost by the proposed algorithm; and (2) joint treatment of the different image components by the proposed algorithm can result in a more accurate mesh representation than a mesh based on a single image component (such as intensity) alone.
Motion-compensated temporal summation of cardiac gated SPECT images using a deformable mesh model

We propose a motion-compensated non-rigid summation method for noise reduction in cardiac gated SPECT. This approach generates a static SPECT image containing counts from all frames of the gated sequence while accounting for heart motion to avoid motion-blur artifact. Static cardiac images typically suffer from heart motion occurring during acquisition which introduces the so-called motion blur artifact. Gated acquisitions, on the other hand, are characterized by lower counts in each individual frame, thus resulting in noisy images. Methods have been proposed to sum the gated sequence along the time dimension while accounting for heart motion, but they do not account for partial volume effect, manifested by an intensity increase as the myocardium contracts. The partial volume effect, a useful diagnostic feature has to be accounted for during both motion estimation and temporal summation. The proposed method relies on a deformable mesh model to estimate heart motion while accounting for the partial volume effect. The estimated motion is further used to perform non-rigid summation along the time dimension. We show that the proposed method yields visual improvement on clinical data. In addition, quantitative evaluation from phantom studies proves that the proposed method achieves better noise reduction performance than available clinical techniques.