scholarly journals Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

2021 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Singanallur Venkatakrishnan ◽  
Yuxuan Zhang ◽  
Luc Dessieux ◽  
Christina Hoffmann ◽  
Philip Bingham ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Wait Time ◽  
Reconstruction Algorithm ◽  
Acquisition Time ◽  
Neutron Tomography ◽  
Reconstruction Algorithms ◽  
High Quality ◽  
Model Based ◽  
Data Fidelity ◽  
Tomographic Measurement

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

PET/CT BiographTM Sensation 16

2006 ◽  
Vol 45 (03) ◽  
pp. 126-133 ◽  
Author(s):  
Y. Bercier ◽  
M. Schwaiger ◽  
S. I. Ziegler ◽  
M.-J. Martínez
Keyword(s):  
Image Quality ◽  
Count Rate ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Considerable Change ◽  
Acquisition Time ◽  
Average Increase ◽  
Scatter Fraction ◽  
Pet Ct ◽  
Before And After

SummaryAim: The new PET/CT Biograph Sensation 16 (BS16) tomographs have faster detector electronics which allow a reduced timing coincidence window and an increased lower energy threshold (from 350 to 400 keV). This paper evaluates the performance of the BS16 PET scanner before and after the Pico-3D electronics upgrade. Methods: Four NEMA NU 2–2001 protocols, (i) spatial resolution, (ii) scatter fraction, count losses and random measurement, (iii) sensitivity, and (iv) image quality, have been performed. Results: A considerable change in both PET count-rate performance and image quality is observed after electronics upgrade. The new scatter fraction obtained using Pico-3D electronics showed a 14% decrease compared to that obtained with the previous electronics. At the typical patient background activity (5.3 kBq/ml), the new scatter fraction was approximately 0.42. The noise equivalent count-rate (RNEC) performance was also improved. The value at which the RNEC curve peaked, increased from 3.7·104s-1 at 14 kBq/ml to 6.4·104s-1 at 21 kBq/ml (2R-NEC rate). Likewise, the peak true count-rate value increased from 1.9·105s-1 at 22 kBq/ml to 3.4·105s-1 at 33 kBq/ml. An average increase of 45% in contrast was observed for hot spheres when using AW-OSEM (4ix8s) as the reconstruction algorithm. For cold spheres, the average increase was 12%. Conclusion: The performance of the PET scanners in the BS16 tomographs is improved by the optimization of the signal processing. The narrower energy and timing coincidence windows lead to a considerable increase of signal- to-noise ratio. The existing combination of fast detectors and adapted electronics in the BS16 tomographs allow imaging protocols with reduced acquisition time, providing higher patient throughput.

scholarly journals Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Daniela Ribeiro ◽  
William Hallett ◽  
Adriana A. S. Tavares
Keyword(s):  
Kinetic Modelling ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Brain Pet ◽  
Good Spatial Resolution ◽  
Positron Emission ◽  
Pet Ct ◽  
Modelling Studies ◽  
Pet Scans

Abstract Background Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Brain studies in research require a reconstruction that provides a good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) reconstruction is not available on GE SIGNA PET-Magnetic Resonance (PET-MR) and studies have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose a strategy to approximate brain PET quantitative outcomes obtained from images reconstructed with Q.Clear versus traditional FBP and OSEM. Methods Contrast recovery and background variability were investigated with the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity and SNR were investigated using the Hoffman phantom. Both phantoms were scanned on a Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) and a General Electric SIGNA PET-MR, for FBP, OSEM and Q.Clear. Differences between the metrics obtained with Q.Clear with different β values and FBP obtained on the PET-CT were determined. Results For in plane and axial resolution, Q.Clear with low β values presented the best results, whereas for SNR Q.Clear with higher β gave the best results. The uniformity results are greatly impacted by the β value, where β < 600 can yield worse uniformity results compared with the FBP reconstruction. Conclusion This study shows that Q.Clear improves contrast recovery and provides better resolution and SNR, in comparison to OSEM, on the PET-MR. When using low β values, Q.Clear can provide similar results to the ones obtained with traditional FBP reconstruction, suggesting it can be used for quantitative brain PET kinetic modelling studies.

scholarly journals Adaptive Reconstruction Algorithm Based on Compressed Sensing Broadband Receiver

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei-Jian Si ◽  
Qiang Liu ◽  
Zhi-An Deng
Keyword(s):  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Adaptive Algorithms ◽  
Reconstruction Algorithm ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Blind Estimation ◽  
Reconstruction Algorithms ◽  
Receiver System ◽  
Adaptive Reconstruction

Existing greedy reconstruction algorithms require signal sparsity, and the remaining sparsity adaptive algorithms can be reconstructed but cannot achieve accurate sparsity estimation. To address this problem, a blind sparsity reconstruction algorithm is proposed in this paper, which is applied to compressed sensing radar receiver system. The proposed algorithm can realize the estimation of signal sparsity and channel position estimation, which mainly consists of two parts. The first part is to use fast search based on dichotomy search, which is based on the high probability reconstruction of greedy algorithm, and uses dichotomy search to cover the number of sparsity. The second part is the signal matching and tracking algorithm, which is mainly used to judge the signal position and reconstruct the signal. Combine the two parts together to realize the blind estimation of the sparsity and the accurate estimation of the number of signals when the number of signals is unknown. The experimental analyses are carried out to evaluate the performance of the reconstruction probability, the accuracy of sparsity estimation, the running time of the algorithm, and the signal-to-noise ratio.

scholarly journals Deflection Tomography Reconstruction Based on Diagonal Total Variation

2018 ◽  
Vol 246 ◽  
pp. 03022
Author(s):  
Huaxin Li ◽  
Jinxiao Pan
Keyword(s):  
Sparse Representation ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
High Quality ◽  
Directional Information ◽  
Field Reconstruction ◽  
Tomography Reconstruction ◽  
Measured Field ◽  
Representation Method

In view of the shortages of the reconstruction algorithm based on Total Variation (TV) minimum under the framework of measured field compressed sensing, we study the measured field sparse representation method and solving method of optimization equation, and propose the measured field reconstruction algorithms based on Diagonal Total Variation (DTV). When there is no obvious change in the reconstruction iteration of TV, gradient transformation of diagonal direction is introduced, the multi-directional information is used to obtain a more sparse representation of the measured field in the reconstruction. Under the condition of sparse projections, experimental results of this algorithm are demonstrated and compared with the results from the TV method. Comparisons show that this method can reconstruct high-quality measured field.

scholarly journals A Comparative Study of Four Total Variational Regularization Reconstruction Algorithms for Sparse-View Photoacoustic Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xueyan Liu ◽  
Limei Zhang ◽  
Yining Zhang ◽  
Lishan Qiao
Keyword(s):  
Total Variation ◽  
Photoacoustic Imaging ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Absolute Error ◽  
Biological Tissues ◽  
Total Variation Regularization ◽  
Reconstruction Algorithms ◽  
Scanning Time ◽  
Comparative Results

Photoacoustic imaging (PAI) is a new nonionizing, noninvasive biomedical imaging technology that has been employed to reconstruct the light absorption characteristics of biological tissues. The latest developments in compressed sensing (CS) technology have shown that it is possible to accurately reconstruct PAI images from sparse data, which can greatly reduce scanning time. This study focuses on the comparative analysis of different CS-based total variation regularization reconstruction algorithms, aimed at finding a method suitable for PAI image reconstruction. The performance of four total variation regularization algorithms is evaluated through the reconstruction experiment of sparse numerical simulation signal and agar phantom signal data. The evaluation parameters include the signal-to-noise ratio and normalized mean absolute error of the PAI image and the CPU time. The comparative results demonstrate that the TVAL3 algorithm can well balance the quality and efficiency of the reconstruction. The results of this study can provide some useful guidance for the development of the PAI sparse reconstruction algorithm.

scholarly journals A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

2018 ◽  
Vol 8 (9) ◽  
pp. 1570 ◽  
Author(s):  
Parsa Omidi ◽  
Mohsin Zafar ◽  
Moein Mozaffarzadeh ◽  
Ali Hariri ◽  
Xiangzhi Haung ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Signal To Noise Ratio ◽  
Iterative Algorithms ◽  
Cost Effective ◽  
Photoacoustic Signal ◽  
Reconstruction Algorithms ◽  
Edge Preservation ◽  
High Quality ◽  
Minimum Number

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.

Compressed Sensing in Parallel MRI: A Review

2021 ◽  
pp. 2250038
Author(s):  
Rafiqul Islam ◽  
Md Shafiqul Islam ◽  
Muhammad Shahin Uddin
Keyword(s):  
Compressed Sensing ◽  
Imaging Technique ◽  
Reconstruction Algorithm ◽  
Acquisition Time ◽  
Reconstruction Algorithms ◽  
Parallel Mri ◽  
Image Domain ◽  
Conventional Mri ◽  
Magnetic Resonance Imaging Mri ◽  
Space Data

Magnetic resonance imaging (MRI) is a dynamic and safe imaging technique in medical imaging. Recently, parallel MRI (pMRI) is widely used for accelerating conventional MRI. Both frequency and image domain-based reconstructions are the most attractive methods for generating the image from multi-channel k-space data. Compressed sensing (CS) is a recently used procedure to reduce the acquisition time of conventional MRI. This reduction is achieved by taking fewer measurements from the fully sampled k-space data. Therefore, applying the CS technique in pMRI is the most emerging way for further improving the acquisition time that is a tremendous research interest. However, as the phase encoding plane may be perpendicular or parallel to the coil elements plane, finding the exact domain for CS in pMRI reconstruction is a major challenging issue. In this work, the application of the CS technique in pMRI in both domains is investigated. Later some widely used methodologies are presented as the nonlinear reconstruction algorithm of CS in pMRI. Finally, a discussion is performed based on CS in pMRI to perceive the reality of different reconstruction algorithms at a glance for finding preferred methodologies.

scholarly journals Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Warr ◽  
Evelina Ametova ◽  
Robert J. Cernik ◽  
Gemma Fardell ◽  
Stephan Handschuh ◽  
...  
Keyword(s):  
Biological Sample ◽  
Ex Vivo ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Tissue Segmentation ◽  
Step Size ◽  
Computed Tomographic ◽  
Iterative Reconstruction Algorithm ◽  
Scan Time

AbstractHere we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (~ 1 keV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic K-edge. Conventionally, because the photons detected at each pixel are distributed across as many as 200 energy channels, energy-selective images are characterised by low count-rates and poor signal-to-noise ratio. This means high X-ray exposures, long scan times and high doses are required to image unique spectral markers. Here, we achieve high quality energy-dispersive tomograms from low dose, noisy datasets using a dedicated iterative reconstruction algorithm. This exploits the spatial smoothness and inter-channel structural correlation in the spectral domain using two carefully chosen regularisation terms. For a multi-phase phantom, a reduction in scan time of 36 times is demonstrated. Spectral analysis methods including K-edge subtraction and absorption step-size fitting are evaluated for an ex vivo, single (iodine)-stained biological sample, where low chemical concentration and inhomogeneous distribution can affect soft tissue segmentation and visualisation. The reconstruction algorithms are available through the open-source Core Imaging Library. Taken together, these tools offer new capabilities for visualisation and elemental mapping, with promising applications for multiply-stained biological specimens.

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.

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