scholarly journals Joint Motion Estimation and Source Identification using Convective Regularisation with an Application to the Analysis of Laser Nanoablations

2019 ◽  
Author(s):  
Lukas F. Lang ◽  
Nilankur Dutta ◽  
Elena Scarpa ◽  
Bénédicte Sanson ◽  
Carola-Bibiane Schönlieb ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Source Identification ◽  
Synthetic Data ◽  
Joint Motion ◽  
Tissue Formation ◽  
Lagrange Equations ◽  
Significant Information ◽  
Extensive Evaluation ◽  
Minimisation Problem ◽  
Microscopy Data

AbstractWe propose a variational method for joint motion estimation and source identification in one-dimensional image sequences. The problem is motivated by fluorescence microscopy data of laser nanoablations of cell membranes in live Drosophila embryos, which can be conveniently—and without loss of significant information—represented in space-time plots, so called kymographs. Based on mechanical models of tissue formation, we propose a variational formulation that is based on the nonhomogenous continuity equation and investigate the solution of this ill-posed inverse problem using convective regularisation. We show existence of a minimiser of the minimisation problem, derive the associated Euler–Lagrange equations, and numerically solve them using a finite element discretisation together with Newton’s method. Based on synthetic data, we demonstrate that source estimation can be crucial whenever signal variations can not be explained by advection alone. Furthermore, we perform an extensive evaluation and comparison of various models, including standard optical flow, based on manually annotated kymographs that measure velocities of visible features. Finally, we present results for data generated by a mechanical model of tissue formation and demonstrate that our approach reliably estimates both a velocity and a source.

A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution

2007 ◽  
Vol 16 (2) ◽  
pp. 479-490 ◽  
Author(s):  
Huanfeng Shen ◽  
Liangpei Zhang ◽  
Bo Huang ◽  
Pingxiang Li
Keyword(s):  
Motion Estimation ◽  
Super Resolution ◽  
Joint Motion ◽  
Map Approach

scholarly journals Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data

2017 ◽  
Vol 36 (1) ◽  
pp. 203-213 ◽  
Author(s):  
Jieqing Jiao ◽  
Alexandre Bousse ◽  
Kris Thielemans ◽  
Ninon Burgos ◽  
Philip S. J. Weston ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Joint Motion ◽  
Brain Pet

Investigating the resolution of IP arrays using inverse theory

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1326-1341 ◽  
Author(s):  
Les P. Beard ◽  
Alan C. Tripp
Keyword(s):  
Gaussian Noise ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Inverse Solution ◽  
The Other ◽  
Inverse Theory ◽  
Significant Information ◽  
Array Data ◽  
Data Subject ◽  
Structure Inversion

Using a fast 2-D inverse solution, we examined the resolution of different resistivity/IP arrays using noisy synthetic data subject to minimum structure inversion. We compared estimated models from inversions of data from the dipole‐dipole, pole‐dipole, and pole‐pole arrays over (1) a dipping, polarizable conductor, (2) two proximate conductive, polarizable bodies, (3) a polarizable conductor beneath conductive overburden, and (4) a thin, resistive, polarizable dike. The estimated resistivity and polarizability models obtained from inversion of the dipole‐dipole data were usually similar to the pole‐dipole estimated models. In the cases examined, the estimated models from the pole‐pole data were more poorly resolved than the models from the other arrays. If pole‐pole resistivity data contain even a fraction of a percent of Gaussian noise, the transformation of such data through superposition to equivalent data of other array types may be considerably distorted, and significant information can be lost using the pole‐pole array. Though the gradient array is reputed to be more sensitive to dip than other arrays, it evidently contains little information on dip that does not also appear in dipole‐dipole data, for joint inversion of dipole‐dipole and gradient array data yields models virtually identical to those obtained from inversion of dipole‐dipole data alone.

scholarly journals Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data

2021 ◽  
Vol 22 (21) ◽  
pp. 11792
Author(s):  
Lena-Marie Woelk ◽  
Sukanya A. Kannabiran  ◽  
Valerie J. Brock  ◽  
Christine E. Gee  ◽  
Christian Lohr  ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Image Data ◽  
Live Cell ◽  
Time Dependent ◽  
Temporal Consistency ◽  
Spatio Temporal ◽  
Microscopy Data

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

scholarly journals Bayesian weighing of electron cryo-microscopy data for integrative structural modeling

2017 ◽  
Author(s):  
Massimiliano Bonomi ◽  
Samuel Hanot ◽  
Charles H. Greenberg ◽  
Andrej Sali ◽  
Michael Nilges ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Synthetic Data ◽  
Scoring Function ◽  
Structural Modeling ◽  
List Type ◽  
Sources Of Information ◽  
Macromolecular System ◽  
Microscopy Data

SummaryCryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map and other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.HighlightsWe present a modeling approach to integrate cryo-EM data with other sources of informationWe benchmark our approach using synthetic data on 21 complexes of known structureWe apply our approach to the GroEL/GroES, RNA polymerase II, and exosome complexes

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