scholarly journals 2½-minute 3D 7T 31P-MRSI of the human heart using concentric rings (CRT)

2021 ◽  
Author(s):  
William T Clarke ◽  
Lukas Hingerl ◽  
Bernhard Strasser ◽  
Wolfgang Bogner ◽  
Ladislav Valkovic ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Variable Number ◽  
Low Rank ◽  
Acquisition Time ◽  
Temporal Data ◽  
Scan Time ◽  
Spread Function ◽  
3D Acquisition ◽  
Spatio Temporal ◽  
31P Mrsi

A 3D density-weighted concentric ring trajectory (CRT) MRSI sequence is implemented for cardiac 31P-MRS at 7T. The point-by-point k-space sampling of traditional phase-encoded CSI sequences severely restricts the minimum scan time at higher spatial resolutions. Our proposed CRT sequence implements a stack of concentric rings trajectory, with a variable number of rings and planes spaced to optimise the density of k-space weighting. This creates flexibility in acquisition time, allowing acquisitions substantially faster than traditional phase-encoded CSI sequences, while retaining high SNR. We first characterise the signal-to-noise ratio and point spread function of the CRT sequence in phantoms. We then evaluate it at five different acquisition times and spatial resolutions in the hearts of five healthy participants at 7T. These different sequence durations are compared with existing published 3D acquisition-weighted CSI sequences with matched acquisition times and spatial resolutions. To minimise the effect of noise on the short acquisitions, low-rank denoising of the spatio-temporal data was also performed after acquisition. The proposed sequence measures 3D localised PCr/ATP ratios of the human myocardium in 2.5 minutes, 2.6 times faster than the minimum scan time for the acquisition-weighted phase-encoded CSI. Alternatively, in the same scan time a 1.7-times smaller nominal voxel volume can be achieved. Low-rank denoising reduced the variance of measured PCr/ATP ratios by 11% across all protocols. The faster acquisitions permitted by 7T CRT 31P-MRSI could make cardiac stress protocols or creatine kinase rate measurements (which involve repeated scans) more tolerable for patients without sacrificing spatial resolution.

scholarly journals Navigator-Free Submillimeter Diffusion Imaging Using Multishot-Encoded Simultaneous Multi-Slice (MUSIUM)

2020 ◽  
Author(s):  
Wei-Tang Chang ◽  
Khoi Huynh ◽  
Pew-Thian Yap ◽  
Weili Lin
Keyword(s):  
Signal To Noise Ratio ◽  
Diffusion Imaging ◽  
Brain Structures ◽  
Peak Amplitude ◽  
Acquisition Time ◽  
Rf Power ◽  
Isotropic Resolution ◽  
Low Snr ◽  
Scan Time

Abstract The ability to achieve submillimter isotropic resolution diffusion MR imaging (dMRI) is critically important to study fine-scale brain structures. One of the major challenges in submillimeter dMRI is the inherently low signal-to-noise ratio (SNR). While approaches capable of mitigating the low SNR have been proposed, namely simultaneous multi-slab (SMSlab) and generalized slice dithered enhanced resolution with simultaneous multislice (gSlider-SMS), limitations are associated with these approaches. The SMSlab sequences suffer from the slab boundary artifacts and require additional navigators for phase estimation. On the other hand, gSlider sequences require relatively high RF power and peak amplitude, which increase the SAR and complicate the RF excitation. In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts. The dMRI with ultrahigh resolution (0.86 mm isotropic), whole brain coverage and ~12.5 minute acquisition time were achieved, revealing detailed structures at cortical and white matter areas. The simulated and in vivo results also demonstrated that the MUSIUM imaging was minimally affected by the motion. Taken together, the MUSIUM imaging is a promising approach to achieve submillimeter diffusion imaging on 3T scanner within clinically feasible scan time.

scholarly journals Multi-version Tensor Completion for Time-delayed Spatio-temporal Data

2021 ◽  
Author(s):  
Cheng Qian ◽  
Nikos Kargas ◽  
Cao Xiao ◽  
Lucas Glass ◽  
Nicholas Sidiropoulos ◽  
...  
Keyword(s):  
Real World ◽  
Mean Squared Error ◽  
Ground Truth ◽  
Low Rank ◽  
Tensor Model ◽  
Temporal Data ◽  
Tensor Completion ◽  
Spatio Temporal ◽  
Tensor Data ◽  
Over Time

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i.d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to 27.2% lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings.

Some Quantification Problems in Parallel EELS Images

1990 ◽  
Vol 48 (2) ◽  
pp. 36-37
Author(s):  
G. Botton ◽  
G. L’Espérance ◽  
M.D. Ball ◽  
C.E. Gallerneault
Keyword(s):  
Electron Microscope ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Scanning Transmission Electron Microscope ◽  
Acquisition Time ◽  
Thickness Variation ◽  
Transmission Electron ◽  
Distribution Of Elements ◽  
Scanning Transmission ◽  
Present Distribution

The recently developed parallel electron energy loss spectrometers (PEELS) have led to a significant reduction in spectrum acquisition time making EELS more useful in many applications in material science. Dwell times as short as 50 msec per spectrum with a PEELS coupled to a scanning transmission electron microscope (STEM), can make quantitative EEL images accessible. These images would present distribution of elements with the high spatial resolution inherent to EELS. The aim of this paper is to briefly investigate the effect of acquisition time per pixel on the signal to noise ratio (SNR), the effect of thickness variation and crystallography and finally the energy stability of spectra when acquired in the scanning mode during long periods of time.The configuration of the imaging system is the following: a Gatan PEELS is coupled to a CM30 (TEM/STEM) electron microscope, the control of the spectrometer and microscope is performed through a LINK AN10-85S MCA which is interfaced to a IBM RT 125 (running under AIX) via a DR11W line.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

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