An alternative approach to the image reconstruction for parallel data acquisition in MRI

2007 ◽  
Vol 30 (12) ◽  
pp. 1437-1451 ◽  
Author(s):  
Frank Bauer ◽  
Stephan Kannengiesser
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Parallel Data ◽  
Alternative Approach

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. 

Proving the Preclusion of Data Manipulation Using Parallel Data Acquisition in Chromatography

2018 ◽  
Vol 941 ◽  
pp. 2390-2394
Author(s):  
David Thomas Marehn ◽  
Detlef Wilhelm ◽  
Heike Pospisil ◽  
Roberto Pizzoferrato
Keyword(s):  
Data Acquisition ◽  
Best Practice ◽  
Sampling Rate ◽  
Good Manufacturing Practice ◽  
Special Role ◽  
Analytical Instruments ◽  
Data Collector ◽  
Parallel Data ◽  
Time Variations ◽  
Hplc Detector

Traceability has an enormous value for companies, but especially for those working in the regulated environment. It plays a special role in the field of pharmacy with respect to manufacturing, controlling and distributing batches of drugs. Through the guidance of Good Manufacturing Practice (GMP) traceability should be ensured. An increasing number of pharmaceutical companies are member of one of the global pharmacopoeias (United States Pharmacopeia, European Pharmacopeia and Japanese Pharmacopeia). The specifications of these pharmacopoeias describe the best practice in documentation, control, qualification and risk management. But however, the pharmacopoeias are written very generally and do not distinguish between the vendors of the analytical instruments. Here, we analyze how chromatographic analyses and data acquisition rely on a specific vendor of the device and the chromatography data system (CDS), the controlling software. We present a way to compare the data acquisition of different CDSs communicating with HPLC instruments. A newly developed software called Data Collector allows the acquisition of data from a HPLC detector parallel to the controlling CDS in the same run. Two HPLC systems and two different CDSs using a well defined sample standard have been tested. The direct comparison of the acquired data precludes unexpected data manipulations of both tested CDSs and shows that there are primarily deviations between the CDSs due to time variations only which depend on the sampling rate. All in all the Data Collector can be used for the traceability of data acquisition.

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.

scholarly journals Parallel data acquisition of in-source fragmented glycopeptides to sequence the glycosylation sites of proteins

2016 ◽  
Vol 37 (11) ◽  
pp. 1420-1430 ◽  
Author(s):  
Jingfu Zhao ◽  
Ehwang Song ◽  
Rui Zhu ◽  
Yehia Mechref
Keyword(s):  
Data Acquisition ◽  
Glycosylation Sites ◽  
Parallel Data

Real-time data acquisition and parallel data processing solution for TJ-II Bolometer arrays diagnostic

2006 ◽  
Vol 81 (15-17) ◽  
pp. 1863-1867
Author(s):  
E. Barrera ◽  
M. Ruiz ◽  
S. López ◽  
D. Machón ◽  
J. Vega ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Time Data ◽  
Parallel Data ◽  
Real Time Data ◽  
Bolometer Arrays ◽  
Processing Solution

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
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