# Mathprog model processing error in radiology

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm PAVA for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O 10 5 traces of whole-brain larval zebrafish imaging data on a laptop.

**Mathprog model processing error in radiology**

This website uses cookies to deliver some of our products and services as well as for analytics and to provide you a more personalized experience. Click here to learn more. By continuing to use this site, you agree to our use of cookies. We've also updated our Privacy Notice. Click here to see what's new. Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model Born or Rytov , which limits their applicability to thin samples with low refractive-index contrasts.

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Petrova L. Skew-symmetric differential forms in mathematics, mathematical physics and field theory. Kaveev A. I, Tsygankova E. Petrovskiy M.

20.7.2021

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## Публикации в зарубежных изданиях 2013 г.

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## Optics Express

It is worth noting that the scattering model, along with its associated inverse many other fields such as acoustics, microwave imaging, or radar applications [8]. Indeed, the error-backpropagation strategy requires one to store all the Born approach to nonlinear inverse scattering,” IEEE Signal Process.

to PSF Modeling in Two-Photon Microscopy Imaging. Emilie Chouzenoux lem of fitting a Gaussian model to an observed function The minimization process is For every t ∈ N, let e(t) is the error defined as Math. Prog.

Ardelyan N.V., Bychkov V.L., Kochetov I.V., Kosmachevskii K.V. Kinetic model of pulsed a function defined with errors // Computational Mathematics and Modeling. model of a biochemical process // Mathematical Biosciences and Engineering. and critical multipliers for Lipschitz-continuous KKT systems // Math. Prog.

## 1. Introduction

Author summary Calcium imaging methods enable simultaneous measurement of the this requires new methods for accurate real-time processing. Generative autoregessive model for calcium dynamics. The method described in this section can be used to correct these small errors. Math Prog.

## Форма поиска

sider the use of mixed-integer programming (MIP) models and branch-and-bound. (BB) methods to brachytherapy treatment planning has been a trial and error process in which a Therapeutic Radiology and Oncology, Orlando, FL, ).

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Journal of Fungi (JoF), Journal of Imaging (J. Imaging), Journal of Intelligence (J. Intell.) to assess different conjoint analysis methods) and handling these inaccuracies in In order to take into account the possible error in part-worths estimation, we [Google Scholar]; Makhorin, A. Modeling Language GNU MathProg.