In this research computer tomography (CT) iterative reconstruction (IR) algorithms are investigated, specifically the impact of their statistical and model-based strength on image quality in low-dose lung screening CT protocols in comparison to filtered back projection (FBP). It has been probed whether statistical, model-based IR in conjunction with low-dose, and ultra-low-dose protocols are suitable for lungcancer screening. To this end, artificial lung nodules shaped as spheres and spicules made from material with calibrated Hounsfield units (HU) were attached on marked positions in the lung structure of an anthropomorphic phantom. Nodule positions were selected by distinguished radiologists. The phantom with nodules was scanned on a CT Scanner using standard high contrast (SHC), low-dose (LD) and ultra low-dose (ULD) protocol. For reconstruction FBP and the IR algorithm ADMIRE at three different strength levels were used. Volume CT dose index (CTDIvol) and dose-length product were recorded. Radiologists assessed subjective image quality using a six-point Likert scale by reading all image series in terms detectability of lung nodules. As a measurable objective image quality parameter signal-to-noise ratios (SNR) were investigated. The CTDIvol decreases by more than 70% for all protocols and nodules compared to diagnostic reference value for chest CT (p<0.00001). The evaluation of image quality parameters, i.e. SNR, indicates that LD and ULD protocols in conjunction with IR assert high quality lung-nodule detection. The results reveal that IR algorithm with moderate to high strength is an indispensable alternative to FBP in low-dose scanning, thus, potentially suitable for lung-tumour screening.
© 2019 by Walter de Gruyter Berlin/Boston
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