Pulse-echo ultrasound (US) aims at imaging tissue using an array of piezoelectric elements by transmitting short US pulses and receiving backscattered echoes. Conventional US imaging relies on delay-and-sum (DAS) beamforming which retrieves a radio-frequency (RF) image, a blurred estimate of the tissue reflectivity function (TRF). To address the problem of the blur induced by the DAS, deconvolution techniques have been extensively studied as a post-processing tool for improving the resolution. Most approaches assume the blur to be spatially invariant, i.e. stationary, across the imaging domain. However, due to physical effects related to the propagation, the blur is nonstationary across the imaging domain. In this work, we propose a continuous-domain formulation of a model which accounts for the diffraction effects related to the propagation. We define a PSF operator as a sequential application of the forward and adjoint operators associated with this model, under some specific assumptions that we precise. Taking into account this sequential structure, we exploit efficient formulations of the operators in the discrete domain and provide a PSF operator which exhibits linear complexity with respect to the grid size. We use the proposed model in a maximum-a-posteriori estimation algorithm, with a generalized Gaussian distribution prior for the TRF. Through simulations and in-vivo experimental data, we demonstrate its superiority against state-of-the-art deconvolution methods based on a stationary PSF.
Objective This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis. Methods This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. LOS was predicted using two random forest models. The first included unstructured text extracted from electronic health records (EHRs). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. Results The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. The two models produced a similar prediction in 86.6% of cases. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). Conclusions LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS.
Abstract-The point spread function (PSF), namely the response of an ultrasound system to a point source, is a powerful measure of the quality of an imaging system. The lack of an analytical formulation inhibits many applications ranging from apodization optimization, array-design, and deconvolution algorithms. We propose to fill this gap through a general PSF derivation that is flexible with respect to the type of transmission (synthetic aperture, plane-wave, diverging-wave etc.), while faithfully capturing the spatially-variant blurring of the Tissue Reflectivity Function as caused by Delay-And-Sum reconstruction. We validate the derived PSF against simulation using Field II, and show that accounting for PSF spatial-variability in sparsebased deconvolution improves reconstruction.Index Terms-Point-Spread-Function, deconvolution, image enhancement, ultrasonic image simulation, apodization design. I. BACKGROUND AND MOTIVATIONIn ultrasound (US) imaging, the finite bandwidth and aperture of transducer elements limits image resolution. This limitation on the resolving capability of the tissue reflectivity function (TRF) can be modelled explicitly by re-casting the radio-frequency (RF) image as the results of an operator between the point-spread function (PSF) and the TRF. In this sense, the PSF, defined as the response of the imaging method in presence of a single scatterer, contains the blurring due to the instrument, and is a powerful tool in assessing the equipment performance in terms of imaging quality.Deconvolution methods use RF images to retrieve the TRF by accounting for the effect of PSF and prior knowledge of the image type. Many authors thus discuss deconvolution in conjunction with PSF assessment [1]- [3]. Two approaches to account for PSF blurring can be distinguished: deterministic Most deconvolution methods, blind or not, assume a spatially-invariant PSF model, primarily for computational purposes. In the blind-deconvolution context, [3], [5] argue that tissue-dependent attenuation and dispersive effects require the PSF to be estimated during the TRF computation. To avoid too complex an optimization, the PSF is usually assumed spatially-invariant across the imaging domain. In non-blind deconvolution [4], [6], a deterministic model of the PSF is obtained by means of simulation, such as Field II [7] or numerical approximation [1]. Again, the convenience of a spatiallyinvariant PSF avoids time-consuming repeated simulation.In both cases, a spatial invariance assumption on the PSF hence leads to important computational gain, since the operator linking the TRF and RF image becomes a convolution, which can efficiently be implemented in the Fourier domain. This assumption can however have profound consequences on the quality of the recovered TRF images: in practice, the PSF can indeed vary quite dramatically across the imaging domain, leading to non-uniform recovery performances.A few attempts have been made to account for spatiallyvarying PSFs [3]. However, they usually come at the cost of some simplifying...
This paper shows how a system of ordinary differential equations describing the evolution of the anaerobic energy, the oxygen uptake, the propulsive force and the velocity of a runner accurately describes pacing strategy. We find a protocol to identify the physiological parameters needed in the model using numerical simulations and time splits measurements for an 80 m and a 1600 m race. The velocity curve of the simulations is very close to the experimental one. This model could allow to study the influence of training and improving some specific parameters for the pacing strategy.
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