Novel ultrahigh resolution data acquisition and image reconstruction for multi-detector row CT

2007 ◽  
Vol 34 (5) ◽  
pp. 1712-1723 ◽  
Author(s):  
T. G. Flohr ◽  
K. Stierstorfer ◽  
C. Süß ◽  
B. Schmidt ◽  
A. N. Primak ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Ultrahigh Resolution ◽  
Resolution Data

Fundamental Sensor Development in Electrical Resistance Tomography

2015 ◽  
Vol 73 (6) ◽  
Author(s):  
Ling En Hong ◽  
Ruzairi Hj. Abdul Rahim ◽  
Anita Ahmad ◽  
Mohd Amri Md. Yunus ◽  
Khairul Hamimah Aba ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Electrical Resistance ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Region Of Interest ◽  
Display System ◽  
Electrical Resistance Tomography ◽  
Fundamental Understanding ◽  
Collection Strategies

This paper will provide a fundamental understanding of one of the most commonly used tomography, Electrical Resistance Tomography (ERT). Unlike the other tomography systems, ERT displayed conductivity distribution in the Region of Interest (ROI) and commonly associated to Sensitivity Theorem in their image reconstruction. The fundamental construction of ERT includes a sensor array spaced equally around the imaged object periphery, a Data Acquisition (DAQ), image reconstruction and display system. Four ERT data collection strategies that will be discussed are Adjacent Strategy, Opposite Strategy, Diagonal Strategy and Conducting Boundary Strategy. We will also explain briefly on some of the possible Data Acquisition System (DAQ), forward and inverse problems, different arrangements for conducting and non-conducting pipes and factors that influence sensor arrays selections. 

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics

2020 ◽  
Vol 26 (3) ◽  
pp. 403-412 ◽  
Author(s):  
Pavel Potocek ◽  
Patrick Trampert ◽  
Maurice Peemen ◽  
Remco Schoenmakers ◽  
Tim Dahmen
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Scanning Electron Microscopy ◽  
Image Reconstruction ◽  
Data Acquisition ◽  
Real Life ◽  
Large Field ◽  
Acquisition Time ◽  
Scanning Electron Microscopy Data ◽  
Scanning Electron

AbstractWith the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2–3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

Susceptibility Weighted Imaging: Data Acquisition, Image Reconstruction and Clinical Applications

2006 ◽  
Vol 16 (4) ◽  
pp. 240-250 ◽  
Author(s):  
Alexander Rauscher ◽  
Jan Sedlacik ◽  
Andreas Deistung ◽  
Hans-Joachim Mentzel ◽  
Jürgen R. Reichenbach
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Clinical Applications ◽  
Susceptibility Weighted Imaging ◽  
Imaging Data
Sign in / Sign up

Export Citation Format

Share Document