Matrix rearrangement for ISAR echo completion with undersampled data

2021 ◽  
Author(s):  
wang shuo ◽  
Han Yusheng ◽  
Zhu Hong
Keyword(s):  
Undersampled Data ◽  
Matrix Rearrangement

Quantification of T1, T2 relaxation times from Magnetic Resonance Fingerprinting radially undersampled data using analytical transformations

2021 ◽  
Vol 80 ◽  
pp. 81-89
Author(s):  
Nikolaos Dikaios ◽  
Nicholas E. Protonotarios ◽  
Athanasios S. Fokas ◽  
George A. Kastis
Keyword(s):  
Magnetic Resonance ◽  
Relaxation Times ◽  
T2 Relaxation ◽  
Undersampled Data

scholarly journals Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinhua Sheng ◽  
Yuchen Shi ◽  
Qiao Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Variable Density ◽  
Reduction Factor ◽  
Resonance Imaging ◽  
Parallel Magnetic Resonance Imaging ◽  
Multiple Variable ◽  
Space Data ◽  
Sampling Pattern ◽  
Undersampled Data ◽  
Reduction Factors

AbstractGeneralized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.

Dispersive noise removal in t-x space: Application to Arctic data

Geophysics ◽  
1988 ◽  
Vol 53 (3) ◽  
pp. 346-358 ◽  
Author(s):  
Greg Beresford‐Smith ◽  
Rolf N. Rango
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Analysis ◽  
Noise Removal ◽  
North Slope ◽  
Frequency Bandwidth ◽  
The North ◽  
North Slope Of Alaska ◽  
Seismic Records ◽  
Time Offset ◽  
Undersampled Data

Strongly dispersive noise from surface waves can be attenuated on seismic records by Flexfil, a new prestack process which uses wavelet spreading rather than velocity as the criterion for noise discrimination. The process comprises three steps: trace‐by‐trace compression to collapse the noise to a narrow fan in time‐offset (t-x) space; muting of the noise in this narrow fan; and inverse compression to recompress the reflection signals. The process will work on spatially undersampled data. The compression is accomplished by a frequency‐domain, linear operator which is independent of trace offset. This operator is the basis of a robust method of dispersion estimation. A flexural ice wave occurs on data recorded on floating ice in the near offshore of the North Slope of Alaska. It is both highly dispersed and of broad frequency bandwidth. Application of Flexfil to these data can increase the signal‐to‐noise ratio up to 20 dB. A noise analysis obtained from a microspread record is ideal to use for dispersion estimation. Production seismic records can also be used for dispersion estimation, with less accurate results. The method applied to field data examples from Alaska demonstrates significant improvement in data quality, especially in the shallow section.

scholarly journals Dynamic MRI reconstruction from undersampled data with an anatomical prescan

2018 ◽  
Vol 34 (7) ◽  
pp. 074001 ◽  
Author(s):  
Julian Rasch ◽  
Ville Kolehmainen ◽  
Riikka Nivajärvi ◽  
Mikko Kettunen ◽  
Olli Gröhn ◽  
...  
Keyword(s):  
Dynamic Mri ◽  
Mri Reconstruction ◽  
Undersampled Data

scholarly journals Motion-compensated reconstruction of magnetic resonance images from undersampled data

2019 ◽  
Vol 55 ◽  
pp. 36-45
Author(s):  
Daniel S. Weller ◽  
Luonan Wang ◽  
John P. Mugler ◽  
Craig H. Meyer
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Undersampled Data

Estimation of Most Probable Maximum From Short-Duration or Undersampled Time-Series Data

2015 ◽  
Author(s):  
Puneet Agarwal ◽  
William Walker ◽  
Kenneth Bhalla
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Short Duration ◽  
Time Series Data ◽  
Extreme Value ◽  
Series Data ◽  
Sampled Data ◽  
Zero Crossing ◽  
Short Time ◽  
Undersampled Data

The most probable maximum (MPM) is the extreme value statistic commonly used in the offshore industry. The extreme value of vessel motions, structural response, and environment are often expressed using the MPM. For a Gaussian process, the MPM is a function of the root-mean square and the zero-crossing rate of the process. Accurate estimates of the MPM may be obtained in frequency domain from spectral moments of the known power spectral density. If the MPM is to be estimated from the time-series of a random process, either from measurements or from simulations, the time series data should be of long enough duration, sampled at an adequate rate, and have an ensemble of multiple realizations. This is not the case when measured data is recorded for an insufficient duration, or one wants to make decisions (requiring an estimate of the MPM) in real-time based on observing the data only for a short duration. Sometimes, the instrumentation system may not be properly designed to measure the dynamic vessel motions with a fine sampling rate, or it may be a legacy instrumentation system. The question then becomes whether the short-duration and/or the undersampled data is useful at all, or if some useful information (i.e., an estimate of MPM) can be extracted, and if yes, what is the accuracy and uncertainty of such estimates. In this paper, a procedure for estimation of the MPM from the short-time maxima, i.e., the maximum value from a time series of short duration (say, 10 or 30 minutes), is presented. For this purpose pitch data is simulated from the vessel RAOs (response amplitude operators). Factors to convert the short-time maxima to the MPM are computed for various non-exceedance levels. It is shown that the factors estimated from simulation can also be obtained from the theory of extremes of a Gaussian process. Afterwards, estimation of the MPM from the short-time maxima is explored for an undersampled process; however, undersampled data must not be used and only the adequately sampled data should be utilized. It is found that the undersampled data can be somewhat useful and factors to convert the short-time maxima to the MPM can be derived for an associated non-exceedance level. However, compared to the adequately sampled data, the factors for the undersampled data are less useful since they depend on more variables and have more uncertainty. While the vessel pitch data was the focus of this paper, the results and conclusions are valid for any adequately sampled narrow-banded Gaussian process.

scholarly journals Patch based reconstruction of undersampled data (PROUD) for high signal-to-noise ratio and high frame rate contrast enhanced liver imaging

2014 ◽  
Vol 74 (6) ◽  
pp. 1587-1597 ◽  
Author(s):  
Mitchell A. Cooper ◽  
Thanh D. Nguyen ◽  
Bo Xu ◽  
Martin R. Prince ◽  
Michael Elad ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Frame Rate ◽  
High Signal ◽  
Liver Imaging ◽  
Signal To Noise ◽  
High Frame Rate ◽  
Contrast Enhanced ◽  
Noise Ratio ◽  
Undersampled Data
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