scholarly journals Fast 3D MR elastography of the whole brain using spiral staircase: Data acquisition, image reconstruction, and joint deblurring

2021 ◽  
Author(s):  
Xi Peng ◽  
Yi Sui ◽  
Joshua D. Trzasko ◽  
Kevin J. Glaser ◽  
John Huston ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Mr Elastography ◽  
Whole Brain ◽  
Spiral Staircase

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. 

scholarly journals 3D multislab, multishot acquisition for fast, whole-brain MR elastography with high signal-to-noise efficiency

2013 ◽  
Vol 71 (2) ◽  
pp. 477-485 ◽  
Author(s):  
Curtis L. Johnson ◽  
Joseph L. Holtrop ◽  
Matthew D.J. McGarry ◽  
John B. Weaver ◽  
Keith D. Paulsen ◽  
...  
Keyword(s):  
High Signal ◽  
Mr Elastography ◽  
Signal To Noise ◽  
Whole Brain

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 Fast PET Preview Image Reconstruction, Streaming, and Visualization During Data Acquisition: A Preliminary Study

2019 ◽  
Vol 47 (3) ◽  
pp. 243-248
Author(s):  
Xiaoying Ji ◽  
Yang-Ming Zhu ◽  
Bin Zhang ◽  
Andriy Andreyev
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Preliminary Study

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

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

scholarly journals Harmonization of PET image Reconstruction Parameters in Simultaneous PET/MRI

2020 ◽  
Author(s):  
Richard Laforest ◽  
Mehdi Khaligi ◽  
Yutaka Natsuaki ◽  
Dharshan Chandramohan ◽  
Darrin Byrd ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Data Acquisition ◽  
Mean Squared Error ◽  
Reconstruction Algorithms ◽  
Recovery Curves ◽  
Quantitative Pet ◽  
Quantitative Accuracy ◽  
Quantitative Performance ◽  
Pet Image Reconstruction ◽  
Image Reconstruction Algorithms

Abstract Background Simultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems. Method: The NEMA NU-2 image quality phantom was imaged on the GE Signa and on the Siemens mMR PET/MRI scanners. The phantom was imaged at 9.8:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 minute data acquisition and on a subset of 5 minutes of data acquisition. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3 to 7 mm in steps of 1 mm were applied. Attenuation correction was provided from a scaled Computed Tomography (CT) image of the phantom registered to the MR-based attenuation images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. The root mean squared error (RMSE) was computed and used to rank the similarity of image reconstruction combination pairs for the two scanners. The image reconstruction parameter set with the lowest RMSE was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction. Results The range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across devices. The best match of CRC curves were obtained with: for SUVmean, 2 iterations/ 16 subsets − 7 mm filter with PSF on the GE Signa and 4 iterations/21 subsets-5 mm filter on the Siemens mMR, for SUVmax, 2 iterations/16 subsets-7 mm filter on the GE Signa, 4 iterations/21 subsets- 6 mm filter on the Siemens mMR and for SUVpeak, 4 iterations/16subsets-7 mm filter with PSF on the GE Signa and 4 iterations/21 subsets-5 mm filter on the Siemens mMR. Over all reconstructions, the RMSE between CRCs for the scanners were 8.1%, 16.7% and 7.1% for mean, max and peak, respectively. These were reduced to less than 2% for harmonized reconstruction settings. Conclusions For two commercially-available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing, and PSF-modeling provide a range of contrast recovery curves that allow harmonization. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.

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