nils-herrmann/clean_scirepeval huggingface dataset:
Curvature assessment of the heart walls in 3D dynamic imaging

Here the authors are concerned with 3D discrete curvature computing in the context of motion estimation of the heart. Curvature information can help to characterize the heart wall surfaces. It can also provide reliable reference points from real 3D data that could guide a matching proces or the inference of 3D local model of the heart's movement. The authors chose to implement an iterative method which refines an initial estimate of the curvatures. They present some results of its application on synthetic and actual 3D cardiac data of a dog.<<ETX>>
Multi-line transmission for 3D ultrasound imaging: An experimental study

Achieving a high frame rate in echocardiography is highly important for quantifying the short phases of the cardiac cycle that contain valuable information for medical diagnosis. Additionally, the 3D quantitative assessment of the heart would significantly improve the current measurements used in daily clinical routine. Nevertheless obtaining ultrafast images remains a challenge due to the trade-off between the image quality and a high frame rate, especially when volumetric data is acquired. Among the current ultrafast imaging methods, multi-linetransmit imaging (MLT) provides an increased frame rate but in the same time mostly preserves the image quality. In this paper we present the first real-time experimental implementation of the MLT in 3D ultrasound. The results indicate the potential of 3D MLT for achieving high contrast and resolution while increasing the frame rate. This study thus demonstrates the feasibility of 3D MLT in real-time and extends its possible applications to dynamic cardiac imaging.
OntoVIP: An Ontology for the Annotation of Object Models Used for Medical Image Simulation

This paper describes works carried out in the Virtual Imaging Platform (VIP) project to create a comprehensive conceptualization of object models used in medical image simulation and suitable for the major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in the VIP platform's model repository, to facilitate their sharing and reuse. Such annotations allow making the anatomical, physiological and pathophysiological content of the object models explicit.
Multi-modality medical image simulation of biological models with the Virtual Imaging Platform (VIP)

This paper describes a framework for the integration of medical image simulators in the Virtual Imaging Platform (VIP). Simulation is widely involved in medical imaging but its availability is hampered by the heterogeneity of software interfaces and the required amount of computing power. To address this, VIP defines a simulation workflow template which transforms object models from the IntermediAte Model Format (IAMF) into native simulator formats and parallelizes the simulation computation. Format conversions, geometrical scene definition and physical parameter generation are covered. The core simulator executables are directly embedded in the simulation workflow, enabling data parallelism exploitation without modifying the simulator. The template is instantiated on simulators of the four main medical imaging modalities, namely Positron Emission Tomography, Ultrasound imaging, Magnetic Resonance Imaging and Computed Tomography. Simulation examples and performance results on the European Grid Infrastructure are shown.
Segmentation and tracking of the cardiac muscle in echocardiographic sequences using a level set approach

In the context of echocardiographic imaging, we propose a level set formulation for registration and segmentation with a shape prior. Registration of a prior contour on the image is expressed as the problem of estimating a rigid transform minimizing a global region-based criterion. The segmentation step is then formulated through front propagation constrained with the registered shape prior. Based on the result of segmentation, the motion field of the segmented muscle is derived and is then used to track a region through the cardiac cycle. This segmentation and tracking framework was applied to in vivo data and was shown to provide very consistent results.
Compressive sensing for raw RF signals reconstruction in ultrasound

We address in this paper the feasibility of the recent compressive sensing (CS) technique for raw RF signals reconstruction in medical ultrasound. Successful CS reconstruction implies selecting a basis where the signal to be recovered has a sparse expansion. RF signals represent a specific challenge, because their oscillatory nature does not easily lend itself to a sparse representation. In that perspective, we propose to use the recently introduced directional wave atoms , which shows good properties for sparsely representing warped oscillatory patterns. Experiments were performed on simulated RF data from a cyst numerical phantom. CS reconstruction was applied to subsampled RF data, by removing 50% to 90% of the original samples. Reconstruction using wave atoms were compared to reconstruction obtained from Fourier and Daubechies wavelets. The obtained accuracies were in the range [0.4–4.0].10−3, [0.2–2.8].10−3 and [0.1–1.7].10−3, using wavelets, Fourier and wave atoms respectively, showing the superiority of the wave atoms representation, and the feasibility of CS for the reconstruction of US RF data.
B-spline explicit active tracking of surfaces (BEATS): Application to real-time 3D segmentation and tracking of the left ventricle in 3D echocardiography

We recently proposed a generic framework for realtime 3D segmentation (BEAS) and we demonstrated that it allows real-time segmentation of volumetric cardiac US sequences. Nevertheless, typical artifacts in RT3DE, such as lateral wall dropout and near-field artifacts still pose some challenges. The aim of the present study was to develop a real-time 3D LV automatic segmentation/tracking framework able to robustly deal with the aforementioned artifacts and to assess its performance in the extraction of volumetric functional parameters used in clinical routine. To this end, we propose a geometrical shape regularizer, penalizing the local curvature variation, and a contrast-dependent motion regularizer term based on the global motion of the LV which is applied only to areas of low local contrast thereby enhancing the tracking of drop-out regions. The proposed regularization terms contribute to a significant increase in tracking performance. Furthermore, the proposed algorithm (BEATS) is able to accurately extract relevant cardiac (functional) indexes in a clinical setting, fully automatically and in real-time.
Three-dimensional velocity field estimation of moving cardiac walls

The quantification of the heart local motion from medical image sequences can be a very useful diagnostic tool in assessing noninvasively the heart function. Several authors have proposed to tackle this problem in two-dimensions (2D) by applying optical flow technique to cardiac image sequences. The application of this method to 2D images has nevertheless severe limitations, since it can in essence only lead to the estimation of the projected fraction of the heart motion. The authors propose in this study to assess the generalization of such a method, based on the continuum theory framework and allowing the estimation of a three-dimensional (3D) motion. In order to illustrate the potential interest of this method toward the quantification of the heart motion, the authors then describe its application to 3D data obtained in vivo on a high speed and high resolution CT scanner. The experimental results are presented and discussed.<<ETX>>
Inverse problem approaches for coded high frame rate ultrasound imaging

In this paper the concept of Simultaneous Coded Emission in the context of Coherent ultrasound Plane Wave Compounding (SCE-CPWC) is studied. The constraints under which SCE-CPWC was proven to be effective are released and we show that a good estimation of the insonified medium can be obtained using l1-regularization. Simulation results on medium with sparse and dense distribution of scatterers are presented and discussed.
Autoregressive modeling application for vascular zones detection in the contrast echographic images

Contrast agents are used in ultrasound imaging to enhance blood region and thereby separate the perfused area and the surrounding tissues. But unfortunately the signals backscattered from agent and tissues are still close. So it is necessary to implement signal processing to enhance the contrast echo. In this article, we apply the autoregressive model to exploit the nonlinear behavior agent properties. Then, we process the obtained pictures by a classification method followed by erosion dilatation algorithm to obtain a satisfying differentiation of the ultrasound image into two classes. The Agent to Tissue Ratio (ATR) factor is used to compare the performance of the methods, and the Fisher criterion is used to study the classification feasibility.
Sharing object models for multi-modality medical image simulation: A semantic approach

Medical image simulation produces virtual images from software representations of imaging devices and virtual object models representing the human body. Object models consist of the geometry of the objects (e.g. organs, tissues, pathological structures, etc.) and of their physical parameters used for the simulation. The diversity of this information makes it difficult to share and reuse across simulation modalities and users. We address this issue by explicitly describing object models using a semantic approach. In particular, we developed an ontology that contains the relevant concepts and relations of the domain of object models for image simulation. This ontology is used to annotate object models and to describe their content and structure. In this paper, we present the construction steps of this ontology, the representation choices and we illustrate how it is used to annotate object models.
Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning

In this paper we propose a framework for using duplex Doppler ultrasound systems. These type of systems need to interleave the acquisition and display of a B-mode image and of the pulsed Doppler spectrogram. In a recent study (Richy , 2013), we have shown that compressed sensing-based reconstruction of Doppler signal allowed reducing the number of Doppler emissions and yielded better results than traditional interpolation and at least equivalent or even better depending on the configuration than the study estimating the signal from sparse data sets given in Jensen, 2006. We propose here to improve over this study by using a novel framework for randomly interleaving Doppler and US emissions. The proposed method reconstructs the Doppler signal segment by segment using a block sparse Bayesian learning (BSBL) algorithm based CS reconstruction. The interest of using such framework in the context of duplex Doppler is linked to the unique ability of BSBL to exploit block-correlated signals and to recover non-sparse signals. The performance of the technique is evaluated from simulated data as well as experimental in vivo data and compared to the recent results in Richy , 2013.
Doppler velocity estimation in 3D cardiac ultrafast ultrasound imaging: An in vitro study

The emergence of ultrafast imaging allowed new insights into cardiac deformation/motion analysis that enabled new advancements in clinical diagnosis. Several studies showed that a good compromise between the temporal and the spatial resolution can be obtained by transmitting multiple focused beams (MLT) simultaneously. However, the current implementation of MLT in 3D cannot be used in dynamic conditions as it was obtained synthetically, by summing the Single Line Transmit (SLT) events before beamforming [Ortega et al., TUFFC, 2016]. The objective of this study is to evaluate for the first time the performance of MLT in 3D ultrasound under dynamic conditions.
Accelerating plane wave imaging through deep learning-based reconstruction: An experimental study

Obtaining ultrafast images using steered plane wave (PW) imaging remains a challenge due to the trade-off between image quality and frame rate. PW imaging indeed relies on compounding in order to preserve a good image quality, usually using multiple successive emissions, which in turn yields a decrease of the frame rate. As opposed to this classical approach, we propose a new strategy to reduce the number of emitted PWs. This is done using a deep learning technique, i.e. by training a convolutional neural network (CNN) to reconstruct high quality images using a small number of PW emissions (typically two).
Multi-line transmission for 3D ultrasound imaging: AN experimental study

Achieving a high frame rate in echocardiography is highly important for quantifying the short phases of the cardiac cycle that contain valuable information for medical diagnosis. Additionally, the 3D quantitative assessment of the heart would significantly improve the current measurements used in daily clinical routine. Nevertheless, obtaining ultrafast images remains a challenge due to the trade-off between the image quality and a high frame rate, especially when volumetric data is acquired. Among the current ultrafast imaging methods, multi-line-transmit imaging (MLT) provides an increased frame rate but in the same time mostly preserves the image quality. However, the current implementation of this method in 3D proposed by Ortega et al.  is based on generating the MLT data synthetically by summing up the raw data before beamforming. In this paper we present the first real-time implementation of the MLT in 3D ultrasound.
High-Quality Plane Wave Compounding Using Convolutional Neural Networks

Single plane wave (PW) imaging produces ultrasound images of poor quality at high frame rates (ultrafast). High-quality PW imaging usually relies on the coherent compounding of several successive steered emissions (typically more than ten), which in turn results in a decreased frame rate. We propose a new strategy to reduce the number of emitted PWs by learning a compounding operation from data, i.e., by training a convolutional neural network to reconstruct high-quality images using a small number of transmissions. We present experimental evidence that this approach is promising, as we were able to produce high-quality images from only three PWs, competing in terms of contrast ratio and lateral resolution with the standard compounding of 31 PWs ( $10\times $  speedup factor).
Nonlinear propagation effects on broadband attenuation measurements

It has been anticipated that the frequency dependency of the attenuation coefficient represents a useful parameter for tissue characterization. Two well established frequency methods to measure this frequency dependency are the so called "log spectral difference" and the "central frequency shift" methods which are both techniques using broadband pulses. It is known that large amplitudes of the emitted wave can bias the estimated frequency dependency of the attenuation coefficient due to nonlinear propagation of finite amplitude waves. The authors investigated the influence of the central frequency and bandwidth of the emitted pulse on this phenomenon and tried to apply these results to in vivo attenuation measurements of liver. Firstly, tissue mimicking phantoms were used in a transmission substitution experiment. The frequency dependency of their attenuation coefficient was defined at different energy levels of the emitted pulse (ranging from 50-400 kPa), changing center frequency and bandwidth. Secondly, a commercial echocardiographic scanner was used-to image the liver of healthy volunteers at different acoustic power outputs. Radiofrequency data was acquired and the frequency dependency of the attenuation coefficient was estimated. The in vitro and in vivo experiments showed that nonlinear propagation effects can bias the estimate of the attenuation coefficient. In substitution measurements (as in the authors' in vitro experiments), the extracted frequency dependency of the attenuation coefficient decreased with increasing amplitude of the emitted pulse, while, as expected, the opposite occurred in reflection measurements (as in the authors' in vivo experiments).
Tangential sound field oscillations for 2D motion estimation in echocardiography

In this paper we present a quantitative study of a method for estimating 2D motion in echocardiography. This method is based on tangential oscillations (TO) of the sound field combined with a phase based motion estimator developed by our group. The feasibility study was presented last year at the IEEE ultrasonics symposium. Here the influence of deformation and depth of investigation are first quantitatively studied in simple simulations of a small 5×5 mm2 square phantom displaced and deformed. RMSE study shows that the proposed method based on TO is more accurate than a conventional block matching (BM) with interpolation. A second set of more realistic simulations based on a left ventricle model during a whole cardiac cycle shows the same tendency. RMSE for TO remains beyond RMSE for BM in axial and lateral directions. The improvement goes up to 50%.
Simultaneous coded plane wave imaging: Implementation on a research echograph

Recent theoretical study  showed that the concept of simultaneous coded plane wave imaging has the potential to yield good image qualities at much higher frame rates than coherent plane wave compounding. In this paper we present the feasibility of this method on an ultrasound (US) scanner widely used in US research and present the first acquisition results on some handmade wire and cyst phantoms. By writing a simplified direct model of wave propagation and using a Least Square Estimator (LSE) based on the Singular Value Decomposition (SVD) to solve the inverse problem we show that the received data can be reconstructed in such way that simultaneous emitted plane waves yield disjointed images. The performance of the LSE is increased by using temporal encoding of the plane waves with pseudo-orthogonal codes. In the context of noisy acquisition the stability of the estimator is discussed and the evolution of the solution is presented.
Processing radio frequency ultrasound images: a robust method for local spectral features estimation by a spatially constrained parametric approach

Spectral estimation is a major component in studies aiming at characterizing biological tissues through the analysis of backscattered radio frequency (RF) ultrasonic signals and images. However, conventional spectral estimation techniques yield a well-known trade-off between spatial resolution and variance. The backscattered signals are stochastic by nature, so short-term local analysis results in a high variance of the estimates, which cannot efficiently be reduced through conventional spatial averaging. We address this issue by describing a spectral estimation technique that reduces the variance of the estimates (by smoothing the local estimates in spectrally homogeneous regions) while preserving spectral discontinuities (i.e., the smoothing is not performed across regions with different spectral contents). The proposed approach is set in a Bayesian framework and is based on local autoregressive (AR) estimation, constrained by smoothness priors. These smoothness priors are introduced through a Markov random field in which the associated potential functions are nonquadratic, allowing thereby to preserve discontinuity. The method is validated on simulated RF images and tested on echocardiographic images acquired in vivo. The results are compared to the estimates provided by the conventional Burg technique. These results clearly demonstrate the ability of the proposed approach to improve spectral estimation in terms of variance reduction and discontinuity detection.
A measure of confidence for Phase-Based Motion Estimator applied to 2D US-TO images

Recent studies showed the potential of the Phase Based Motion Estimator when applied to ultrasound images containing transverse oscillations. Even though the emergence of ultrafast or high frame rates imaging overcame some of the prior limitations of the algorithm regarding its capability to detect large displacement values, the method still presents an important lack of robustness. In this paper, we propose an improvement of the Phase Based Motion Estimator, by introducing a measure of confidence. The robustness of the method is indeed highly influenced by the orientations of the patterns present in radio frequency images containing transverse oscillations. The current phase based method relies on a specific pattern, containing pixels oriented in the two perpendicular directions of the oscillations. Any deviation from the assumed image model results in a underperforming estimate. The detection of the orthogonal patterns, which are in concordance with the theoretical image model, could guarantee the favorable outcome of the algorithm. A promising tool for identifying the desired patterns is given by Gabor filters oriented in 0 and 90 degrees direction. The magnitude response of the filter to the image orientations can be then grouped in two classes by applying the k-Means method. The class described by high intensity values is expected to guarantee the accuracy of the motion estimator. By keeping the regions corresponding with the first class and discarding the ones identified as belonging to the second class, we eliminate the underperforming estimates and increase the robustness of the method. The validation on simulated ultrafast images improved estimates with error reduced by 73% in the axial direction and 53% in the lateral direction, compared to the case when no measure of confidence was used.
Towards 3-D tissue doppler ultrafast echocardiography: An in vitro study

The emergence of ultrafast imaging allows new insights into cardiac deformation/motion analysis that could enable new advancements in clinical diagnosis. Several studies showed that a good compromise between the temporal and the spatial resolution can be obtained by transmitting multiple focused beams simultaneously, method referred in literature as MLT (multi line transmit). However, previous investigations on 3D MLT were limited to simulations or retrospective synthetic implementations, and thus cannot be used in dynamic conditions. The objective of this study was to evaluate the performance of MLT on a moving phantom represented by a spinning disk. Images of the in vitro model were acquired using four Verasonics systems synchronized to drive a 32×32 Vermon probe. The Doppler velocities were estimated on volumetric data using an ensemble auto-correlator. The results showed that the images acquired using MLT can provide accurate Doppler velocities (mean relative error in the entire volume = 4.1%). MLT allowed an 8-fold increase in volume rate with preserved Doppler image quality, which could open up new clinical opportunities in cardiac echo imaging.
Simultaneous coded plane wave imaging in ultrasound: Problem formulation and constraints

In this paper, we propose a new emission strategy in the context of plane wave imaging. Plane wave imaging indeed implies compounding in order to preserve a good image quality. Such compounding is usually obtained using multiple, successive emissions, which in turn yields a decrease of the frame rate. As opposed to this approach, our method is based on the simultaneous emission of several coded plane waves. This allows the reconstruction of all the images corresponding to the different plane waves, by using an inverse problem approach. The proposed method is closely related to channel estimation in telecommunications, and coded excitation in synthetic aperture ultrasound imaging. In this paper, we extend these lines of work to the case of ultrasound plane wave imaging and evaluate the obtained performance from various numerical simulations.