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Preliminary clinical application of an adaptive iterative statistical reconstruction algorithm inhead and neck computed tomography angiography with low tube voltage and a low concentration of contrast medium 被引量:1
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作者 Shan Hu Wenzhen Zhu +4 位作者 Daoyu Hu XiaoYan Meng Jinhua Zhang Weijia Wan Li Zhou 《Oncology and Translational Medicine》 CAS 2015年第4期171-176,共6页
Objective To evaluate the feasibility of using a low concentration of contrast medium (Visipaque 270 mgl/mL), low tube voltage, and an advanced image reconstruction algorithm in head and neck computed tomography ang... Objective To evaluate the feasibility of using a low concentration of contrast medium (Visipaque 270 mgl/mL), low tube voltage, and an advanced image reconstruction algorithm in head and neck computed tomography angiography (CTA). Methods Forty patients (22 men and 18 women; average age 48.7 ± 14.25 years; average body mass index 23.9 ± 3.7 kg/m^2) undergoing CTA for suspected vascular diseases were randomly assigned into two groups. Group A (n = 20) was administered 370 mgl/mL contrast medium, and group B (n = 20) was administered 270 mgl/mL contrast medium. Both groups were administered at a rate of 4.8 mL/s and an injection volume of 0.8 mL/kg. Images of group A were obtained with 120 kVp and filtered back projection (FBP) reconstruction, whereas images of group B were obtained with 80 kVp and 80% adaptive iterative statistical reconstruction algorithm (ASiR). The CT values and standard deviations of intracranial arteries and image noise on the corona radiata were measured to calculate the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). The beam-hardening artifacts (BHAs) around the skull base were calculated. Two readers evaluated the image quality with volume rendered images using scores from 1 to 5. The values between the two groups were statistically compared. Results The mean CT value of the intracranial arteries in group B was significantly higher than that in group A (P 〈 0.001). The CNR and SNR values in group B were also statistically higher than those in group A (P 〈 0.001). Image noise and BHAs were not significantly different between the two groups. The image quality score of VR images of in group B was significantly higher than that in group A (P = 0.001). However, the quality scores of axial enhancement images in group B became significantly smaller than those in group A (P〈 0.001). The CT dose index volume and dose-length product were decreased by 63.8% and 64%, respectively, in group B (P 〈 0.001 for both). Conclusion Visipaque combined with 80 kVp and 80% ASiR provided similar image quality in intracranial CTA with 64% radiation dose reduction compared with the use of lopamidol, 120 kVp, and FBP reconstruc-tion. 展开更多
关键词 low concentration contrast medium head and neck computed tomography angiography adaptive iterative statistical reconstruction algorithm
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An adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization
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作者 XIE Jianping WANG Rui +1 位作者 HE Xiongxiong LI Sheng 《Chinese Journal of Acoustics》 CSCD 2017年第1期87-101,共15页
An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive (RJIO-DA) is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response (MVD... An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive (RJIO-DA) is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response (MVDR), the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms. 展开更多
关键词 SINR DA MVDR RLS An adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization
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