Experimental study on the effect of adaptive statistical iterative reconstruction algorithms on image quality and radiation dose in paranasal sinus CT
10.3760/cma.j.issn.1005-1201.2020.01.014
- VernacularTitle: 多模型自适应统计迭代重建算法对鼻窦CT图像质量和辐射剂量影响的实验研究
- Author:
Lili ZHANG
1
;
Yantao NIU
;
Junfang XIAN
;
Yongxian ZHANG
;
Jianxing WU
Author Information
1. Department of Radiology, Beijing Tongren Hospital & Clinical Center for Eye Tumors, Capital Medical University, Beijing 100730, China
- Publication Type:Journal Article
- Keywords:
Paranasal sinuses;
Tomography, X-ray computed;
Radiation dosage;
Image processing, computer-assisted;
Image quality
- From:
Chinese Journal of Radiology
2020;54(1):66-70
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the effect of pre- and post-adaptive statistical iterative reconstruction-Veo (ASiR-V) on image quality and radiation dose in paranasal sinus CT, and to find the best combinations.
Methods:One head specimen was scanned with the routine spiral CT scanning parameters [noise index (NI)=8] and different levels of pre-ASiR-V (0—100%, with an interval of 10%). The images were reconstructed with different post-ASiR-V (0—100%, with an interval of 10%) for the bone algorithm and standard algorithm. All of 242 thin-layer images of paranasal sinuses were obtained. The region of interest (ROI) was selected to measure the CT value to calculate the contrast to noise ratio (CNR) and figure of merit (FOM). The volume CT dose index (CTDIvol) and Smart mA were recorded. The linear regression was conducted to analyze the relationship between CTDIvol, SmartmA, CNR and FOM. And with the same pose-ASiR-V level, the CNR of images which reconstructed by bone and soft algorithms were compared with pair-wise t test. The image quality was subjectively evaluated by three independent experienced radiologists using a 4-point scale (4 was the best).
Results:As the pre-ASiR-V levels (0—100%) increased, Smart mA and CTDIvol were reduced with a linear negative correlation (r=-0.981, -0.976, both P<0.001). The Smart mA decreased by 72.05% and CTDIvol by 71.22%. Keeping the same pre-ASiR-V level,the CNR increased with the increase of post-ASiR-V level (for the bone algorithm images:R2=0.976, 0.992, 0.982, 0.982, 0.975, 0.975, 0.979, 0.996, 0.952, 0.978, 0.965;for the standard algorithm images: R2=0.944, 0.990, 0.988, 0.993, 0.996, 0.987, 0.984, 0.996, 0.996, 0.990, 0.965).Under the same level of post-ASiR-V, the CNR and FOM fluctuated with the pre-ASiR-V level (for the bone algorithm images:R2=0.335, 0.341, 0.344, 0.364, 0.385, 0.405, 0.418, 0.429, 0.455, 0.474, 0.516; for the standard algorithm images: R2=0.223, 0.278, 0.327, 0.285, 0.309, 0.329, 0.325, 0.346, 0.360, 0.390, 0.380). All subjective image quality could meet the diagnostic requirements (the score≥3).
Conclusion:At NI=8, for the bone algorithm, the best combination is 80% pre-ASiR-V and 100% post-ASiR-V; for the standard algorithm, the best iteration combination is 100% and 100%. The appropriate choice of pre- and post-ASiR-V levels in paranasal sinus CT scan can effectively reduce the radiation dose under the premise of maintaining the image quality that meets the diagnostic needs.
