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.

Peripheral bronchial luminal conspicuity on dynamic-ventilation computed tomography: association with radiation doses and temporal resolution by using an ex vivo porcine lung phantom

2020 ◽  
Vol 61 (12) ◽  
pp. 1608-1617
Author(s):  
Yukihiro Nagatani ◽  
Makoto Yoshigoe ◽  
Shinsuke Tsukagoshi ◽  
Noritoshi Ushio ◽  
Kohei Ohashi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Temporal Resolution ◽  
Ex Vivo ◽  
Reconstruction Algorithm ◽  
Radiation Doses ◽  
Reconstruction Algorithms ◽  
Movement Velocity ◽  
Full Reconstruction ◽  
Luminal Area ◽  
Lung Phantom

Background It is still unclear which image reconstruction algorithm is appropriate for peripheral bronchial luminal conspicuity (PBLC) on dynamic-ventilation computed tomography (DVCT). Purpose To assess the influence of radiation doses and temporal resolution (TR) on the association between movement velocity (MV) and PBLC on DVCT. Material and Methods An ex vivo porcine lung phantom with simulated respiratory movement was scanned by 320-row CT at 240 mA and 10 mA. Peak and dip CT density and luminal area adjusted by values at end-inspiration (CTDpeak and CTDdip, luminal area ratio [LAR]) for PBLC and MVs were measured and visual scores (VS) were obtained at 12 measurement points on 13 frame images obtained at half and full reconstructions (TR 340 and 190 ms) during expiration. Size-specific dose estimate (SSDE) was applied to presume radiation dose. VS, CTDpeak, CTDdip, LAR, and their cross-correlation coefficients with MV (CCC) were compared among four methods with combinations of two reconstruction algorithms and two doses. Results The dose at 10 mA was presumed as 26 mA by SSDE for standard proportion adults. VS, CTDdip, CTDpeak, and LAR with half reconstruction at 10 mA (2.52 ± 0.59, 1.016 ± 0.221, 0.948 ± 0.103, and 0.990 ± 0.527) were similar to those at 240 mA except for VS, and different from those with full reconstruction at both doses (2.24 ± 0.85, 0.830 ± 0.209, 0.986 ± 0.065, and 1.012 ± 0.438 at 240 mA) ( P < 0.05). CCC for CTDdip with half reconstruction (–0.024 ± 0.552) at 10 mA was higher compared with full reconstruction (–0.503 ± 0.291) ( P < 0.05). Conclusion PBLC with half reconstruction at 10 mA was comparable to that at 240 mA and better than those with full reconstruction on DVCT.

scholarly journals Evaluation of 68Ga-PSMA PET/CT Images Acquired with a Reduced Scan Time Duration in Prostate Cancer Patients using the Digital Biograph Vision

2020 ◽  
Author(s):  
Manuel Weber ◽  
Regina Hofferber ◽  
Ken Herrmann ◽  
Wolfgang Peter Fendler ◽  
Maurizio Conti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Reconstruction ◽  
Diagnostic Performance ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Time Duration ◽  
Acquisition Protocol ◽  
Scan Time ◽  
Psma Pet ◽  
Pet Ct

Abstract Aim 68Ga-PSMA PET/CT allows for a superior detection of prostate cancer (PC) tissue, especially in context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration / administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand 68Ga-PSMA PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance. Methods Twenty PC patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent 68Ga-PSMA PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a scan time duration of approximately 17 min (reference acquisition protocol) and 5 min (reduced acquisition protocol). 4 iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak) and (c) image noise using the liver’s activity distribution. Results Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤ 4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13–21% (4 iterations) and from 10–15% (2 iterations) for PSF + TOF and TOF images. Conclusions High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98% of regions) and image quantification (mean deviation ≤ 10%) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5% of patients. Our results suggest that a reduction of scan time duration or administered 68Ga-PSMA activities can be considered in metastatic patients, where missing small lesions would not impact patient management.

scholarly journals Evaluation of [68Ga]Ga-PSMA PET/CT Images Acquired with a Reduced Scan Time Duration in Prostate Cancer Patients using the Digital Biograph Vision

2020 ◽  
Author(s):  
Manuel Weber ◽  
Walter Jentzen ◽  
Regina Hofferber ◽  
Ken Herrmann ◽  
Wolfgang Peter Fendler ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Reconstruction ◽  
Cancer Patients ◽  
Diagnostic Performance ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Time Duration ◽  
Acquisition Protocol ◽  
Scan Time ◽  
Pet Ct

Abstract Aim: [68Ga]Ga-PSMA-11 PET/CT allows for a superior detection of prostate cancer tissue, especially in the context of a low tumor burden. Digital PET/CT bears the potential of reducing scan time duration / administered tracer activity due to, for instance, its higher sensitivity and improved time coincidence resolution. It might thereby expand [68Ga]Ga-PSMA-11 PET/CT that is currently limited by 68Ge/68Ga-generator yield. Our aim was to clinically evaluate the influence of a reduced scan time duration in combination with different image reconstruction algorithms on the diagnostic performance.Methods: Twenty prostate cancer patients (11 for biochemical recurrence, 5 for initial staging, 4 for metastatic disease) sequentially underwent [68Ga]Ga-PSMA-11 PET/CT on a digital Siemens Biograph Vision. PET data were collected in continuous-bed-motion mode with a mean scan time duration of 16.7 min (reference acquisition protocol) and 4.6 min (reduced acquisition protocol). 4 iterative reconstruction algorithms were applied using a time-of-flight (TOF) approach alone or combined with point-spread-function (PSF) correction, each with 2 or 4 iterations. To evaluate the diagnostic performance, the following metrics were chosen: (a) per-region detectability, (b) the tumor maximum and peak standardized uptake values (SUVmax and SUVpeak) and (c) image noise using the liver’s activity distribution.Results: Overall, 98% of regions (91% of affected regions) were correctly classified in the reduced acquisition protocol independent of the image reconstruction algorithm. Two nodal lesions (each ≤4 mm) were not identified (leading to downstaging in 1/20 cases). Mean absolute percentage deviation of SUVmax (SUVpeak) was approximately 9% (6%) for each reconstruction algorithm. The mean image noise increased from 13% to 21% (4 iterations) and from 10% to 15% (2 iterations) for PSF+TOF and TOF images.Conclusions: High agreement at 3.5-fold reduction of scan time in terms of per-region detection (98 % of regions) and image quantification (mean deviation ≤ 10 %) was demonstrated; however, small lesions can be missed in about 10% of patients leading to downstaging (T1N0M0 instead of T1N1M0) in 5 % of patients. Our results suggest that a reduction of scan time duration or administered [68Ga]Ga-PSMA-11 activities can be considered in metastatic patients, where missing small lesions would not impact patient management. Limitations include the small and heterogeneous sample size and the lack of follow-up.

scholarly journals An Image Reconstruction Algorithm based on Sparse Representation for Image Compressed Sensing

2021 ◽  
Vol 15 ◽  
pp. 511-518
Author(s):  
Shuyao Tian ◽  
Liancheng Zhang ◽  
Yajun Liu
Keyword(s):  
Image Reconstruction ◽  
Sparse Representation ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Step Size ◽  
Sampling Process ◽  
Artifact Suppression ◽  
Reconstruction Time ◽  
Improved Algorithm

It is difficult to control the balance between artifact suppression and detail preservation. In addition, the information contained in the reconstructed image is limited. For achieving the purpose of less lost information and lower computational complexity in the sampling process, this paper proposed a novel algorithm to realize the image reconstruction using sparse representation. Firstly, the principle of algorithm for sparse representation is introduced, and then the current commonly used reconstruction algorithms are described in detail. Finally, the algorithm can still process the image when the sparsity is unknown by introducing the sparsity theory and dynamically changing the step size to approximate the sparsity. The results explain that the improved algorithm can not only reconstruct the image with unknown sparsity, but also has advantages over other algorithms in reconstruction time. In addition, compared with other algorithms, the reconstruction time of the improved algorithm is the shortest under the same sampling rate.

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 Computerized Tomography Imaging Omics under Iterative Reconstruction Algorithm in Diagnosis of Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chunfang Zhou ◽  
Shufang Tian ◽  
Fei Lv ◽  
Rui Shang ◽  
Xuejiao Zheng
Keyword(s):  
Gastric Cancer ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Subjective Evaluation ◽  
Area Under The Curve ◽  
Reconstruction Algorithm ◽  
Training Group ◽  
Iterative Reconstruction Algorithm ◽  
Significant Difference ◽  
Noise Ratio

This study aimed to explore the application value of computed tomography (CT) imaging radiomics based on a sinogram-affirmed iterative reconstruction algorithm (SAFIRE) in the diagnosis of gastric cancer. 59 patients who were clinically diagnosed with gastric cancer were selected as research objects and arranged CT examinations. The images obtained were optimized by the SAFIRE for the staging of gastric cancer. The pathological biopsy results were used as the gold standard to evaluate its diagnostic effect and compared with the filtered back-projection (FBP) method. The results showed that the carrier-to-noise ratio (CNR) (0.979) and signal-to-noise ratio (SNR) (0.967) of the CT image after the algorithm processing were significantly higher than those (0.781, 0.744) before ( P < 0.05 ). There was no significant difference in CT values between the FBP algorithm and S1, S2, and S3 ( P > 0.05 ); the area under the curve (AUC) (0.999) and sensitivity (0.98) of the CT training group under the SAFIRE algorithm for gastric cancer classification were higher than those of the verification group (0.958, 0.92). The preoperative CT staging kappa value was consistent with the postoperative pathological diagnosis of 0.882. CT images guided by SAFIRE can objectively and noninvasively assess the tumor asymmetry, discover additional information from subjective evaluation beyond the naked eye, and perform reasonable staging diagnosis of gastric cancer, which was useful for clinicians to develop high-quality individualized treatment plans.

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