iterative reconstruction algorithm
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2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chunfang Zhou ◽  
Shufang Tian ◽  
Fei Lv ◽  
Rui Shang ◽  
Xuejiao Zheng

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liang Guo ◽  
Lu Ren ◽  
Yajun Shao ◽  
Wei Li ◽  
Shangxian Yu

The objective of this study was to compare the diagnostic value of computed tomography (CT) based on iterative reconstruction algorithm in old myocardial infarction (OMI), thereby providing theoretical guidance and practical basis for clinical treatment. In this study, in order to provide theoretical guidance and practical basis for the diagnosis and treatment of clinical OMI, 10 patients with OMI were selected and divided into two groups, with 5 patients in each group. In addition, an algebraic iterative reconstruction algorithm is constructed, which starts from the initial estimation value, compares, and corrects the estimation results and the measured results continuously until the error between the two results is less than the predetermined value. The experimental group was optimized by algebraic iterative reconstruction algorithm, and the control group was reconstructed by the hospital original method. The image quality parameters under different iteration times were analyzed and compared to obtain the optimal iteration times. The value of iterative reconstruction algorithm in clinical diagnosis was investigated by analyzing the time of drawing and the accuracy of diagnosis after drawing. Through the analysis and comparison of the image quality parameters of the patients from the experimental group, it was found that the image quality firstly increased with the increase in the number of iterations but decreased with the increase of the number of iterations after a certain number of iterations. The results showed that the optimal number of iterations was 13 times. The drawing time of the experimental group and the control group was 54.27 minutes and 117.87 minutes in turn, so the difference between the two groups was significant ( P < 0.05 ). Besides, there was a statistically marked difference in the accuracy rate of the experimental group (93.33%) and the control group (73.33%) ( P < 0.05 ). In conclusion, the time required for coronary artery CT imaging using algebraic iterative reconstruction algorithm was greatly reduced and the diagnostic accuracy was hugely improved. Therefore, the coronary artery CT imaging based on iterative reconstruction algorithm could make more effective use of medical resources and improve the diagnostic accuracy in the diagnosis of OMI.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Kunyang Bao ◽  
Chao Liu ◽  
Jin Li ◽  
Xiang Liu ◽  
Wenzhang Luo ◽  
...  

In order to analyze the change characteristics of blood flow field in cerebral aneurysms before and after stent implantation, this study first constructed an optimized iterative reconstruction algorithm to reconstruct CT images of patients with cerebral aneurysms and used it to solve the problem of image sharpness. In addition, backprojection image reconstruction algorithm and Fourier transform analytic method were introduced. According to the CT images of cerebral arteries of patients, the lesions were presented in a three-dimensional and visual way through the reconstructed three-dimensional images, thus achieving the effects of simulation and simulation. The results showed that the sensitivity, specificity, and accuracy of the optimized iterative reconstruction algorithm were 90.78%, 83.27%, and 94.82%, which were significantly higher than those of the backprojection image reconstruction algorithm and Fourier transform analysis method, and the difference was statistically significant ( P < 0.05 ). Before operation, the blood flow velocity in the neck of aneurysm was 7.35 × 10−2 m/s, the exit velocity was 1.51 × 10−1 m/s, and the maximum velocity appeared in the upstream part of the exit. After passing through the aneurysm, the blood flow velocity began to decrease gradually, forming a vortex at the top of the tumor. After stent implantation, the neck and outlet velocities of cerebral aneurysm were 9.352 × 10−2 m/s and 1.897 × 10−2 m/s, respectively. The velocity of blood flow decreased after entering the aneurysm, and there was no vortex at the top of the aneurysm. Among the outlet velocities of arterial blood vessels, the velocity before stent implantation was significantly lower than that after stent implantation, and the difference was statistically significant ( P < 0.05 ). Compared with prestent, the shear force distribution on the wall of cerebral aneurysm showed a significant decrease, and the difference was statistically significant ( P < 0.05 ). To sum up, pelvic floor ultrasound based on hybrid iterative reconstruction algorithm has high accuracy in diagnosing the changes of blood flow field in cerebral aneurysms. The application of CT images in the diagnosis of cerebral aneurysms can objectively provide imaging data for clinical practice and has high application value.


2021 ◽  
pp. 102594
Author(s):  
Leonardo Di Schiavi Trotta ◽  
Dmitri Matenine ◽  
Margherita Martini ◽  
Karl Stierstorfer ◽  
Yannick Lemaréchal ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Cui ◽  
Yang Sun ◽  
Meng Xia ◽  
Dan Yao ◽  
Jun Lei

This research was aimed to study CT image features based on the backprojection filtering reconstruction algorithm and evaluate the effect of ropivacaine combined with dexamethasone and dexmedetomidine on assisted thoracoscopic lobectomy to provide reference for clinical diagnosis. A total of 110 patients undergoing laparoscopic resection were selected as the study subjects. Anesthesia induction and nerve block were performed with ropivacaine combined with dexamethasone and dexmedetomidine before surgery, and chest CT scan was performed. The backprojection image reconstruction algorithm was constructed and applied to patient CT images for reconstruction processing. The results showed that when the overlapping step size was 16 and the block size was 32 × 32, the running time of the algorithm was the shortest. The resolution and sharpness of reconstructed images were better than the Fourier transform analytical method and iterative reconstruction algorithm. The detection rates of lung nodules smaller than 6 mm and 6–30 mm (92.35% and 95.44%) were significantly higher than those of the Fourier transform analytical method and iterative reconstruction algorithm (90.98% and 87.53%; 88.32% and 90.87%) ( P < 0.05 ). After anesthesia induction and lobectomy with ropivacaine combined with dexamethasone and dexmedetomidine, the visual analogue scale (VAS) decreased with postoperative time. The VAS score decreased to a lower level (1.76 ± 0.54) after five days. In summary, ropivacaine combined with dexamethasone and dexmedetomidine had better sedation and analgesia effects in patients with thoracoscopic lobectomy. CT images based on backprojection reconstruction algorithm had a high recognition accuracy for lung lesions.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lan Zang

Objective. This study was aimed to explore the accuracy of multi-slice spiral computed tomography (CT) scan in preoperative staging diagnosis of bladder cancer based on hybrid iterative reconstruction algorithm, so as to provide a more reasonable supporting basis for guiding clinical work in the future. Methods. Retrospectively, 120 patients admitted to hospital from July 2019 to April 2021, who were confirmed to be with urothelial carcinoma of the bladder by pathological examination after surgical treatment, were selected. CT images before processing were set as the control group and those after processing were set as the observation group according to whether they were processed by the hybrid iterative algorithm. Postoperative pathological examination was utilized as the standard for analysis. The accuracy and consistency of the two methods were compared. Results. The accuracy of the results of each stage of the observation group (T1 stage: 91.09%, T2 stage: 89.66%, T3 stage: 88.89%, and T4 stage: 88.89%) and consistency (T1 stage: 0.66, T2 stage: 0.69, T3 stage: 0.71, and T4 stage: 0.82) were higher than those of the control group (accuracy: T1—57.01%, T2—48.28%, T3—44.44%, and T4—44.44%). The consistency was as follows: T1—0.32, T2—0.24, T3—0.37, and T4—0.43, and the comparison was statistically significant ( P  < 0.05). Conclusion. The adoption value of the image features based on the hybrid iterative reconstruction algorithm in the diagnosis of bladder cancer staging was higher than that of the conventional multi-slice spiral CT, indicating that the hybrid iterative reconstruction algorithm had a good adoption prospect in clinical examination.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Warr ◽  
Evelina Ametova ◽  
Robert J. Cernik ◽  
Gemma Fardell ◽  
Stephan Handschuh ◽  
...  

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.


2021 ◽  
Vol 7 (10) ◽  
pp. 199
Author(s):  
Juan Manuel Álvarez-Gómez ◽  
Joaquín Santos-Blasco ◽  
Laura Moliner Martínez ◽  
María José Rodríguez-Álvarez

Improvements in energy resolution of modern positron emission tomography (PET) detectors have created opportunities to implement energy-based scatter correction algorithms. Here, we use the energy information of auxiliary windows to estimate the scatter component. Our method is directly implemented in an iterative reconstruction algorithm, generating a scatter-corrected image without the need for sinograms. The purpose was to implement a fast energy-based scatter correction method on list-mode PET data, when it was not possible to use an attenuation map as a practical approach for the scatter degradation. The proposed method was evaluated using Monte Carlo simulations of various digital phantoms. It accurately estimated the scatter fraction distribution, and improved the image contrast in the simulated studied cases. We conclude that the proposed scatter correction method could effectively correct the scattered events, including multiple scatters and those originated in sources outside the field of view.


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