nils-herrmann/clean_scirepeval huggingface dataset:
Greedy framework for optical flow tracking of myocardium contours

Optical flow (OF) tracking of the myocardium contours has a potential in segmenting the myocardium in time sequences of cardiac medical images. Nevertheless, to estimate the displacement field of the contour points, a number of assumptions are required to solve an under-determined set of optical flow equations. In this work, a new framework is proposed to solve the OF tracking problem using greedy optimisation algorithm. The new framework allows different types of constraints such as motion invariance, shape and topology to be applied in a unified way. The developed methods are applied to a publicly-available cardiac magnetic resonance imaging dataset containing image sequences for 33 patients. Quantitative evaluation of the results shows high potential of the methods to accurately track and segment the myocardium contours.
The effects of the analysis technique on hepatic iron evaluation using T2∗ mapping with magnetic resonance imaging

Iron toxicity is the major factor for tissue damage and organ failure in patients with iron overload. T2* measurement with magnetic resonance imaging (MRI) has been established as an effective and non-invasive technique for evaluating iron content in the liver. However, different factors related to the adopted image processing criterion affect the resulting measurements. These factors include the exponential fitting model (single exponential, bi-exponential, or exponential + constant) and the size and location of the selected region of interest and whether it includes vasculature, susceptibility artifacts, or is close to the liver boundary. In this study, we investigate the effects of these various factors on T2* measurement using calibrated phantoms with different amounts of iron as well as on patients with different degrees of iron overload. The results show various degrees of similarities and differences between different processing techniques, which should be known by the operator for better selection of the image processing parameters and proper interpretation of the resulting measurements.
Hybrid intensity- and phase- based optical flow tracking of tagged MRI

Accurate tracking of the myocardium tissues in tagged Magnetic Resonance Images (MRI) is essential for evaluating the cardiac function. Current tracking methods utilize either the image intensity or the image phase as landmarks that can be tracked. In either case, the performance is vulnerable to the image quality and the fading of the tag lines. In this work, we propose a hybrid optical flow tracking method that combines both the intensity and the phase features of the image. The method is validated using numerical cardiac phantom as well as real MRI data experiments. Both experiments showed that the proposed method outperforms current intensity-based optical flow tracking and the phase-based HARP method with maximum error of 1 pixel at extreme conditions of tag fading.
The ANN as a nonlinear regularization technique to solve the inverse problem of electrocardiography

The inverse problem of electrocardiography is ill-conditioned, which amplifies the errors generated by the geometric uncertainties. Linear regularization treatment is not enough for this problem. Therefore, the authors introduced the ANN as a nonlinear regularization approach to the inverse problem of electrocardiography. To differentiate between the need for nonlinear treatment and the need for regularization, two simple models were chosen. In the first model, the inverse problem was highly ill-conditioned (matrix condition no.>lOE6). The epicardial potentials (17 points) are to be restored from the surface potentials (17 points). The cardiac source was represented by a concentric cap of distribution of dipoles-near the epicardium-in a homogenous volume conductor. Several values for the source angle were taken for both linear calculations and training the ANN. For this model, regularization was needed even without geometric uncertainties. In the second model, the inverse problem was relatively well-conditioned (matrix condition no.<20), where the cardiac source was represented by six current dipoles in a homogenous spherical volume conductor of radius R=1. Body surface potentials were calculated at 41 measuring points on the body sphere. The potentials calculated from the 64 possible combinations of dipoles status were used to get the transfer matrix.
Tag removal in cardiac tagged MRI images using coupled dictionary learning

Tagged Magnetic Resonance Imaging (tMRI) is considered to be the gold standard for quantitative assessment of the cardiac local functions. However, the tagging patterns and low myocardium-to-blood-pool contrast of tagged images bring great challenges to cardiac image processing and analysis tasks such as myocardium segmentation and tracking. Hence, there has been growing interest in techniques for removing tagging lines. In this work, a method for removing tagging patterns in tagged MR images using a coupled dictionary learning (CDL) model is proposed. In this model, identical sparse representations are assumed for image patches in the tagged MRI and corresponding cine MRI image spaces. First, we learn a dictionary for the tagged MRI image space. Then, we compute a dictionary for the cine MRI image space so that corresponding tagged and cine patches have the same sparse codes in terms of their respective dictionaries. Finally, in order to produce the de-tagged (cine version) of a test tagged image, the sparse codes of the tagged patches and the trained cine dictionary are used together to construct the de-tagged patches. We have tested this tag removal method on a dataset of tagged cardiac MR images. Our experimental results compared favorably with a recently proposed tag removal method that removes tags in the frequency domain using an optimal band-stop filter of harmonic peaks.
Improved Harmonic Phase (HARP) Method for Motion Tracking a Tagged Cardiac MR images

The diagnosis of cardiovascular disease requires precise assessment of both morphology and function of the heart. Currently, magnetic resonance imaging (MRI) provides a useful tool for accurate and reproducible assessment of regional function of the left ventricle noninvasively. MR tagging produces images of the heart that can be analyzed using harmonic phase (HARP) method to describe the regional function of the heart. In order to calculate regional function, a circular mesh is manually built at a specific timeframe, after which the points of such mesh are tracked using the HARP technique. The tracking is not perfect and some individual points on the mesh could fail in tracking. In this work, a new method is presented to improve the tracking by combining HARP with active contour methods. This modified HARP technique is more robust than the previous HARP technique
The influence of the analysis technique on myocardial T2 estimation using cardiac magnetic resonance imaging (CMR)

Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) has been considered as a noninvasive technique for characterizing myocardial tissues. Specifically, T2 mapping technique has been demonstrated to be an excellent tool to detect myocardial edema by estimation of the transverse relaxation time constant (T2) of myocardial tissue. However, there are several factors that could influence the analysis technique and affect the estimating T2 value. These factors include the type of exponential fitting model, which either be single exponential or exponential plus constant fitting model, the signal intensity estimation method, which either be Average, Median or Mapping method and signal-to-noise ratio (SNR) level of the T2-weighted images. In this paper, we discuss the effect of these factors on T2 estimation using a numerical phantom, T2 calibrated phantoms with different T2 values, and human subjects. The results of numerical phantom showed that the average percentage error for T2 measurement when using the single exponential fitting model reached 0.6 %, 0.4 %, and 5.9 % for the Average, Median, and Mapping methods, respectively. However, the exponential plus constant fitting model resulted in high average percentage error among the three signal intensity estimation methods (above 26 %). The experiments of calibrated phantom resulted in high correlation (R2 > 0.99) between the estimated and reference T2 values when using the single exponential fitting model compared to low correlation (R2 < 0.73) when using the exponential plus constant fitting model at high SNR levels. Finally, the results of human subjects were in agreement with both numerical and calibrated phantoms. Based on the results of this paper, the single exponential fitting model with the median estimation method is the preferable analysis technique for T2 measurement as it results in a lower error for T2 measurements at low SNR levels and higher correlation values at high SNR levels compared to the other analysis techniques.
Performance of Optical Flow tracking approaches for cardiac motion analysis

Tagging Magnetic Resonance Imaging (MRI) sequence is used for evaluating Left Ventricular contractility. In this technique, a pattern of spatially varying magnetism is applied at the end diastole. Analyzing the deformation of tag pattern during the cardiac cycle has wide applications for cardiac deformation analysis. Noninvasive myocardial tagging in MRI has shown great potential in measuring and studying the motion of the heart. This paper presents a mathematical model that simulates the real cardiac motion during myocardial tagging. We synthesized both the Spatial Modulation of Magnetization (SPAMM) and complementary Spatial Modulation of Magnetization (CSPAMM) tag patterns with arbitrary spatial frequency. Using this model, we tested the performance and limitations of different Optical Flow (OF) motion tracking techniques and compared them with the performance of Harmonic Phase (HARP) analysis technique. The results exhibit that the OF tracking accuracy differs from point to another with a noticeable over estimation at the end of systole. Also OF is performing better than HARP at the heart borders.
The Influence of the Analysis Technique on Myocardial T2 Estimation Using Cardiac Magnetic Resonance Imaging (CMR)

Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) has been considered as a noninvasive technique for characterizing myocardial tissues. Specifically, T2 mapping technique has been demonstrated to be an excellent tool to detect myocardial edema by estimation of the transverse relaxation time constant (T2) of myocardial tissue. However, there are several factors that could influence the analysis technique and affect the estimating T2 value. These factors include the type of exponential fitting model, which either be single exponential or exponential plus constant fitting model, the signal intensity estimation method, which either be Average, Median or Mapping method and signal-to-noise ratio (SNR) level of the T2-weighted images. In this paper, we discuss the effect of these factors on T2 estimation using a numerical phantom, T2 calibrated phantoms with different T2 values, and human subjects. The results of numerical phantom showed that the average percentage error for T2 measurement when using the single exponential fitting model reached 0.6 %, 0.4 %, and 5.9 % for the Average, Median, and Mapping methods, respectively. However, the exponential plus constant fitting model resulted in high average percentage error among the three signal intensity estimation methods (above 26 %). The experiments of calibrated phantom resulted in high correlation (R2 > 0.99) between the estimated and reference T2 values when using the single exponential fitting model compared to low correlation (R2 < 0.73) when using the exponential plus constant fitting model at high SNR levels. Finally, the results of human subjects were in agreement with both numerical and calibrated phantoms. Based on the results of this paper, the single exponential fitting model with the median estimation method is the preferable analysis technique for T2 measurement as it results in a lower error for T2 measurements at low SNR levels and higher correlation values at high SNR levels compared to the other analysis techniques.
Combining long and short axis myocardial contours for accurate reconstruction of the left ventricular surface

Reconstruction of the left ventricular (LV) surface is required in a number of applications including modeling the LV shape and computing the LV volume. In this work, a new method is presented for accurately reconstructing the LV surface from magnetic resonance images of the heart. The method depends on novel technique for combining the long-axis (LAX) and short-axis (SAX) myocardium contours. A crucial step to achieve this goal is to properly reconstruct the LV surface from the long-axis (LAX) and short-axis (SAX) myocardium contours. This is done using three steps: (1) register the LAX and SAX contours to compensate the patient respiratory motion; (2) non-parametric interpolation of the LV surface at the gaps between SAX slices. To evaluate the proposed methods, out-of-place LAX and SAX myocardial contours was simulated to represent the respiratory motion effect. Then, the LV volume is calculated using the proposed method. The results are compared to the ground truth obtained by the modified Simpson's method. The significance of the methods was proved by calculating its error in calculating the LV volume when only few slices are available, i.e. reduced acquisition. In addition, the method was compared to other available methods using a dataset of 5 patients.
Estimation of Eulerian strain from tagged MRI using Optical flow

Cardiac magnetic resonance imaging (MRI) is considered as noninvasive imaging modalities provide information on myocardial function, viability, and morphology. One of the most common MRI techniques is tagging MRI, based on this technique the deformation of the myocardium can be evaluated and some of cardiac functions can be examined. The strain of cardiac muscle one of the parameters estimated based on the estimated deformation, this estimated strain help in the examination of myocardium viability. This paper presents a mathematical model that simulates the real cardiac motion during myocardial tagging. Using this model and real data, we estimate the circumferential strain using strain tensor with Band-pass Optical flow (BPOF) technique and compare it with the estimated strain of Harmonic Phase (HARP as a golden standard technique. The results exhibit that the stain can be estimated using BPOF method with the same level of accuracy compared to HARP method, however, BPPOF provides more reliable results than HARP, with no tracking errors, at the myocardial boundaries.
Evaluation of cardiac left ventricular mass from Tagged Magnetic Resonance Imaging

Tagged Magnetic Resonance Imaging (tMRI) is considered the gold standard for quantitative assessment of the cardiac regional function. However, quantification of the global function from tMRI is challenging due to the low contrast between myocardium and blood in these images, and hence, prevents the accurate segmentation of the myocardium. In this work, a method for enhancing the myocardium-to-blood contrast in tagged MR images is presented. First, the tag pattern in tMRI is removed by accurately suppressing the frequency components of the tag pattern to produce tagless images. Then, the image contrast is enhanced by estimating the local standard deviation and using it to suppress the blood signal intensity. The proposed method is applied on a dataset for 12 patients to calculate the global cardiac functional parameters. The results are then compared to those calculated from standard cine MRI image sequences of the same patients. The results show that the proposed method can be used to accurately estimate the left ventricular volume and mass.
Automatic synthesis of cine viability MRI images for evaluation of coronary heart disease

Coronary heart disease (CHD) is the leading cause of death worldwide. Cardiac magnetic resonance imaging (MRI) is a valuable imaging modality, as it can noninvasively provide information about myocardial function, viability, and morphology. Viability delayed-enhancement (DE) images are acquired at a single timeframe while myocardial functional (tagged) images are acquired as a cine loop of timeframes throughout the cardiac cycle. In this work, we propose a method for estimating DE images at all timeframes in the cardiac cycle without additional scan time to show both viability and functional information in the same image. The method is based on generating a dense motion field of the heart from the acquired tagged images, and then applying the extracted field to the acquired DE image. The developed technique is accurate in generating cine DE images and providing simultaneous information about myocardial viability and wall motion for comprehensive patient evaluation and optimal treatment selection.
Optimal design of RF pulses with arbitrary profiles in magnetic resonance imaging

The proper design of RF pulses in magnetic resonance imaging has a direct impact on the quality of acquired images. Several techniques have been proposed to obtain the RF pulse envelope given the desired slice profile. Unfortunately, these techniques do not take into account the limitations of practical implementation such as limited amplitude resolution. Moreover, implementing constraints for special RF pulses on most techniques is not possible. In this work, we propose an approach for designing optimal RF under theoretically any constraints. The new technique poses the RF pulse design problem as a combinatorial optimization problem and uses efficient techniques from this area to solve this problem. In particular, an objective function is proposed as the norm of the difference between the desired profile and the one obtained from solving the Bloch equations for the current RF pulse design values. Two global optimization techniques were implemented using genetic algorithms and simulated annealing. The results show a significant improvement over conventional design techniques and suggest the practicality of using the new technique for clinical use.
Dual-energy computed tomography versus magnetic resonance imaging for the assessment of iron overload

Iron toxicity is a key factor for tissue damage in iron-overloaded patients, with induced heart failure being the main cause of death. T2*-weighted magnetic resonance imaging (MRI) has been established as the method of choice for evaluating iron content with strong correlation with biopsy, where T2* <; 20 ms and T2* <; 10 ms at 1.5T indicate iron overload and severe iron overload, respectively. Recently introduced dual-energy computed tomography (DECT) has the potential for evaluating iron overload without energy-dependent CT attenuation or tissue fat effects. This study investigates the performance of DECT for iron mapping in scans of calibrated iron phantoms, and compare the results to MRI T2* imaging. The results show that DECT has high accuracy for evaluating iron overload, comparable to that of MRI T2* imaging, which might help in patient staging based on the severity of iron overload, independent of the implemented imaging energy.
The ANN as a technique to solve the inverse problem of electrocardiography: the effect of the training margin on the errors caused by geometric uncertainties in an eccentric homogenous spherical model

Artificial neural networks (ANN) were previously proposed by the author as a technique for solving the inverse problem of electrocardiography. The aim of the paper is to find the training limits of the input data for this problem and the related probability of output errors. Using a model of a homogeneous spherical body, the cardiac source was represented by six current dipoles located on the surface of an eccentric heart sphere. Body surface potentials were calculated at 26 measuring points for (19/spl times/64) cases representing the all possible combinations of dipole status (64) in the following (19) cases; the basic case, 8 cases of uncertainty in heart radius r/sub o/, 8 cases of angular uncertainty in /spl theta/, and 2 cases in /spl phi/ of the spherical coordinates. The data base for 64/spl times/19 cases was used to test the response of the ANN for each group of training. The statistical output error E was calculated in each case (E=Nf/spl times/100/64/spl times/6, where Nf is the number of false outputs). The obtained results showed that the ANN accepted the training data within a limited margin, beyond which the data would appear to be contradictory.
The influence of the analysis technique on myocardial T1 estimation using MRI

Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) provides unprecedented capabilities for myocardial tissue characterization. Specifically, T1 mapping showed to be a valuable technique for identifying myocardial fibrosis and scar tissues without the need for contrast agent administration. Nevertheless, various factors can influence the analysis technique and affect the resulting T1 measurements. These factors include the signal calculation method (Average, Median, or pixel-wise Mapping), size and location of the analyzed region-of-interest (ROI), and signal-to-noise ratio (SNR) level of the acquired images. In this study, we evaluate the influence of these factors on T1 measurement using numerical and calibrated phantoms. The numerical phantom results showed that the percentage error for T1 estimation reached 2.3%, 3.1%, and 12.28% for the Median, Average, and Mapping methods, respectively, at SNR level of 7 dB, compared to 0% error at high SNR (44 dB) .The calibrated phantom experiments revealed high correlation (R>0.9) between the estimated and reference T1 values at high SNR levels. Based on the results in this study, the Median calculation method outperforms the Average and Mapping methods due to its lower estimation error at low SNR and higher correlation values with reference measurements. Further, large ROI's that are placed at the center of the analyzed region result in more accurate T1 estimates than smaller ROI's or those placed closer to the object boundary.
Evaluation of myocardial strain using bandpass optical flow. Comparison to harmonic phase analysis

Magnetic resonance imaging (MRI) is a noninvasive imaging modality for providing information about the myocardial function, viability, and morphology. One of the most common MRI techniques is myocardial tagging, based on which deformation of the myocardium can be quantified. Different measures of regional cardiac function, e.g. strain, can thus be calculated to provide information about wall motion abnormality. In this paper, we investigate the performance of the band-pass optical flow (BPOF) method for measuring myocardial strain and compare the results to the commonly used harmonic phase (HARP) method. The results show that BPOF provides accurate strain estimates and surpasses HARP at the myocardial boundaries.