1.Analysis of image artifacts in computed radiographic system
Huayong ZHU ; Shufeng FAN ; Sheng XU
Chinese Medical Equipment Journal 1993;0(05):-
Objective To investigate the appearance,causes and countermeasures of the image artifacts in a computed radiographic system.Methods Random 1968 CR images were analyzed by GE Radworks.Results Among the 1,968 images which were choosed at random,66 images with artifacts were found and processed.The artifacts in computed radiographic system are relevant to system software and hardware,dust as well as operators.Conclusion Familiarization with the appearance and causes of CR image artifacts helps to prevent or reduce artifacts.
2.Effect of adaptive statistical iterative reconstruction algorithm on the imaging quality in low-dose spectral CT scanning of the liver
Huayong ZHU ; Jingli PAN ; Weiping ZHU ; Yangfei LI ; Jianrong DING ; Shufeng FAN ; Wenbin JI
Chinese Journal of Radiological Medicine and Protection 2015;35(12):948-952
Objective To investigate the value of the adaptive statistical iterative reconstruction (ASIR) algorithm for reducing the radiation dose and optimizing the image quality in the low-dose spectral CT scanning in GSl (Gemstone spectral imaging) of the liver.Methods A total of 60 patients who underwent hepatic spectral CT scanning in GSI were enrolled in this study.The patients were randomly divided into two groups according to priority with 30 cases per group.Low-dose spectral CT scanning was used for group A, and images were reconstructed by ASIR 0 and 50% , marked as A1 and A2.Group B was scanned with conventional dose of spectral CT, and images were reconstructed by Filtered back projection (FBP).Effective doses (E) for each group were calculated.Image quality was assessed by two radiologists, and the radiation doses were compared between groups A and B.Results All image quality of each group were good enough for clinical diagnosis.E for group A and B were (3.2 ±0.2) and (5.8 ± 0.2) mSv, respectively.There was statistical difference with image noise between group A and B(Z =-6.784,P < 0.05).The image noise, SNR and CNR had statistical differences between group A and B (F =24.013, 15.646, 8.285, P <0.05).Compared with group A1, the image noise was lower, and the SNR and CNR were higher in groups A2 and B(P < 0.05).There were no statistical differences of image noise, SNR and CNR between groups A2 and B (P > 0.05).There were no statistical differences of the image quality score between groups A1, A2 and B (F =102.38,105.768, P < 0.05).Conclusions ASIR combined with low-dose spectral CT scanning was helpful to reduce radiation dose and could obtain better image quality in hepatic CT examination.
3.Application value of the deep learning-based image reconstruction algorithm in combined head and neck CT angiography with low radiation dose
Yangfei LI ; Weiping ZHU ; Yidi HOU ; Jianxin PANG ; Yicheng FANG ; Huayong ZHU
Chinese Journal of Radiological Medicine and Protection 2024;44(1):53-59
Objective:To explore the differences between the deep learning-based image reconstruction (DLIR) and the adaptive statistical iterative reconstruction V (ASiR-V) algorithms in the radiation dose and image quality of head and neck CT angiography (CTA).Methods:The data of 80 patients undergoing head and neck CTA due to vascular diseases in the head and neck were prospectively collected. These patients were randomly divided into groups A and B based on their examination sequence. The CTA images of group A were reconstructed based on ASiR-V 50%, with a tube voltage of 120 kV and a noise index of 11.0. In contrast, those of group B were reconstructed based on ASiR-V 50% (for group B1) and DLIR-H (for group B2), with a tube voltage of 80 kV and a noise index of 9.0. Then, the radiation doses and image quality of both groups were compared using the independent-sample t-test. The radiation doses, and both subjective and objective image quality of the two imaging method were compared through the Kruskal-Wallis test and the Wilcoxon rank-sum test. The independent- or paired-sample t-test was employed to measure inter-group vascular enhanced CT values, as well as signals and noise from regions of interest (ROIs), with signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) calculated. Results:The effective doses of groups A and B were (0.77±0.08) and (0.45±0.05) mSv, respectively, with a statistically significant difference ( t = 21.96, P < 0.001). The vascular enhanced CT values, SDs, SNRs, and CNRs in the arch of the aorta, the initial and bifurcation parts of the common carotid artery, and the M1 segment of the middle cerebral artery showed statistically significant differences among groups A, B1, and B2 ( F = 67.69, 68.50, 50.52, 74.10, 63.10, 91.22, 69.16, P < 0.001). Additionally, statistically significant differences were observed in the subjective scores of image quality among groups A, B1, and B2 ( Z = 71.06, P < 0.05). Conclusions:The DLIR algorithm can further reduce the radiation dose in head and neck CTA examination while significantly reducing image noise and ensuring image quality, thus demonstrating high clinical application value.
4.EST pipeline system: detailed and automated EST data processing and mining.
Hao XU ; Ling HE ; Yuanzhong ZHU ; Wei HUANG ; Lijun FANG ; Lin TAO ; Yuedong ZHU ; Lin CAI ; Huayong XU ; Liang ZHANG ; Hong XU ; Yan ZHOU
Genomics, Proteomics & Bioinformatics 2003;1(3):236-242
Expressed sequence tags (ESTs) are widely used in gene survey research these years. The EST Pipeline System, software developed by Hangzhou Genomics Institute (HGI), can automatically analyze different scalar EST sequences by suitable methods. All the analysis reports, including those of vector masking, sequence assembly, gene annotation, Gene Ontology classification, and some other analyses, can be browsed and searched as well as downloaded in the Excel format from the web interface, saving research efforts from routine data processing for biological rules embedded in the data.
Automation
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Base Composition
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Computational Biology
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methods
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Databases, Genetic
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Expressed Sequence Tags
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Software
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User-Computer Interface