研究目的
To evaluate and compare the image quality and acceptance of a full model-based iterative reconstruction (MBIR) algorithm with an earlier full hybrid iterative reconstruction (HIR) algorithm and filtered back projection (FBP) in abdominal CT imaging.
研究成果
Full MBIR improves objective image quality metrics such as CNR, spatial resolution, and low-contrast detectability compared to HIR and FBP, especially at low doses, but leads to changes in image texture that reduce subjective acceptance and diagnostic confidence among radiologists. Awareness of these benefits and texture changes could improve clinical adoption.
研究不足
The study used a single MBIR algorithm and one CT scanner model, which may not generalize to other algorithms or scanners. It was a phantom-based study, so results may not directly translate to clinical diagnostic accuracy in patients. Factors like patient body habitus and metal implants were not evaluated.
1:Experimental Design and Method Selection:
A phantom study was conducted using a CATPHAN 600 phantom to compare three reconstruction algorithms (FBP, HIR, full MBIR) at seven dose levels. Objective and subjective analyses were performed to assess image quality parameters.
2:Sample Selection and Data Sources:
The CATPHAN 600 phantom was used, with specific modules (CTP404, CTP515, CTP528, CTP486) for sensitometry, low-contrast detectability, spatial resolution, and noise power spectrum analysis.
3:List of Experimental Equipment and Materials:
A 320 detector-row CT scanner (Aquilion ONE, Canon Medical Systems), CATPHAN 600 phantom, ImageJ
4:48v software, IQworks V2 software. Experimental Procedures and Operational Workflow:
Acquisitions were performed with varying mA levels (35 to 250 mA), images reconstructed with FBP, HIR (AIDR 3D), and full MBIR (FIRST) algorithms. Objective measurements (CNR, MTF, NPS) and subjective evaluations by radiologists and non-radiologists were conducted.
5:Data Analysis Methods:
Statistical analysis using R software (version 3.0.1), Wilcoxon rank test for comparisons, linear regression, and Cronbach alpha for reliability.
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