| Liu Xiaodong,Liu Ailian,Liu Jinghong,Liu Yijun,Sun Meiyu,Fang Xin,Yuan Gang,Zhao Ying,Xu Mingzhe.Influence on image quality in low-dose colonography with adaptive statistical iterative reconstruction algorithm[J].Chinese Journal of Radiological Medicine and Protection,2018,38(4):311-316 |
| Influence on image quality in low-dose colonography with adaptive statistical iterative reconstruction algorithm |
| Received:June 04, 2017 |
| DOI：10.3760/cma.j.issn.0254-5098.2018.04.013 |
| KeyWords:X-ray computer CT colonography Colon polyps Iterative reconstruction algorithm |
| FundProject: |
| Author Name | Affiliation | E-mail | | Liu Xiaodong | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Liu Ailian | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | cjr.liuailian@vip.163.com | | Liu Jinghong | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Liu Yijun | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Sun Meiyu | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Fang Xin | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Yuan Gang | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Zhao Ying | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | | | Xu Mingzhe | Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China | |
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| Abstract:: |
| Objective To evaluate the feasibility of adaptive statistical iterative reconstruction-V (ASIR-V)to improve image quality in low-dose CT colonography. Methods A series of thirty artificial polyps were established by ligation in an isolated segment of porcine colon. Volume data was acquired on Revolution CT scanner (GE, USA) with High Definition scan mode and different scan parameter combinations:120 kVp with different mAs (10, 30, 50, 70, 90, 100, 120, 140, 160, 180, 200, 220, 240, 260, respectively). Images were reconstructed with six different ASIR-V levels of 0(filtered back projection, FBP),10%, 30%, 50%, 70% and 90%. Two radiologists were blinded to measure and analyze the objective data independently, including image noise (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The kappa test was used to assess interobserver agreement in subjective image quality score. ICC test was used to examine the consistency of the measurements between two observers. SD, SNR, CNR were performed for statistical analysis in different tube current and different levels of ASIR-V using variance analysis(ANOVA). Results Interobserver agreement for subjective image quality score was good with a kappa value of 0.683. The variation of the tube current(r=0.734, P=0.000) and ASIR-V level(r=0.220, P=0.044) is related to the subjective score of image quality. Under the condition of the same tube current, image quality score of 50% ASIR-V reconstruction was the highest. Two objective data consistency is good.The differences of image noise (F=423.58, P<0.05), SNRs(F=124.26, P<0.05) and CNRs (F=1 030.17, P<0.05)of different tube current and different levels of ASIR-V reconstruction were statistically significant. In the same tube current, with increased levels of ASIR-V, image noise reduced, CNRs increased. Only in 10, 120,140, 160, 220, 240, 260 mA, the differences of SNRs were statistically significant(F=8.75-31.36, P<0.05). For the same level of ASIR-V reconstruction, with the increase of tube current, the image noise decreased, SNR and CNR increased gradually. Conclusions In the CT colonography, the application of ASIR-V algorithm can significantly reduce the noise and enhance the image contrast noise ratio and improve image quality. ASIR-V algorithm with 50% has better performance in reducing CT image noise. |
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