Cardiac imaging in diagnostic VCT using multi-sector data acquisition and image reconstruction: step-and-shoot scan vs. helical scan

2008 ◽  
Author(s):  
Xiangyang Tang ◽  
Jiang Hsieh ◽  
John L. Seamans ◽  
Fang Dong ◽  
Darin Okerlund
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Cardiac Imaging ◽  
Step And Shoot ◽  
Helical Scan ◽  
Sector 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.

Reconstruction of 3D Regions-of-Interest from Data in Reduced Helical Cone-beam Scans

2005 ◽  
Vol 4 (2) ◽  
pp. 143-150 ◽  
Author(s):  
Xiaochuan Pan ◽  
Yu Zou ◽  
Dan Xia ◽  
Emil Y. Sidky
Keyword(s):  
Image Reconstruction ◽  
Motion Artifacts ◽  
Angular Range ◽  
Filtered Backprojection ◽  
Use Of Data ◽  
Numerical Studies ◽  
The Subject ◽  
Object Problem ◽  
Helical Scan ◽  
Practical Implications

The suffciency conditions are derived for exact image reconstruction of a 3D ROI from projections acquired with a reduced helical scan over an angular range considerably smaller than that required by image reconstruction in, e.g., the conventional long object problem, for which the scanned angular range is often more than 2π. ROI reconstruction is investigated by a recently developed filtered-backprojection algorithm that can make use of data acquired with a reduced helical scan. Preliminary numerical studies demonstrate and validate the ROI reconstruction. This work may have significant practical implications because a reduced scan in CT often translates to reduced motion artifacts and reduced radiation dose delivered to the subject.

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

Quality control system for data acquisition and image reconstruction with smart hybrid ECT device

2018 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Konrad Kania ◽  
Jakub Szumowski ◽  
Pawel Tchorzewski ◽  
Przemyslaw Adamkiewicz ◽  
...  
Keyword(s):  
Quality Control ◽  
Control System ◽  
Image Reconstruction ◽  
Data Acquisition ◽  
Quality Control System
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