Objectives: To assess the feasibility of an objective approach for the evaluation of low-contrast detectability in multidetector computed-tomography (MDCT) by combining a virtual phantom containing simulated lesions with an image quality metric.
Materials and methods: A low-contrast phantom containing hypodense spheric lesions (-20 HU) was scanned on a 64-slice MDCT scanner at 4 different dose levels (25, 50, 100, 200 mAs). In addition, virtual round hypodense low-contrast lesions (20 HU object contrast) based on real CT data were inserted into the lesion-free section of the datasets. The sliding-thin-slab algorithm was applied to the image data with an increasing slice-thickness from 1 to 15 slices. For each dataset containing simulated lesions a lesion-free counterpart was reconstructed and post-processed in the same manner. The low-contrast performance of all datasets containing virtual lesions was determined using a full-reference image quality metric (modified multiscale structural similarity index, MS-SSIM*). The results were validated against a reader-study of the real lesions.
Results: For all dose levels and lesion sizes there was no statistically significant difference between the low-contrast performance as determined by the image quality metric when compared to the reader study (p<0.05). The intraclass correlation coefficient was 0.72, 0.82, 0.90 and 0.84 for lesion diameters of 4 mm, 5 mm, 8 mm and 10 mm, respectively. The use of the sliding-thin-slab algorithm improves lesion detectability by a factor ranging from 1.15 to 2.69 when compared with the original axial slice (0.625 mm).
Conclusion: The combination of a virtual phantom and a full-reference image quality metric enables a systematic, automated and objective evaluation of low-contrast detectability in MDCT datasets and correlates well with the judgment of human readers.
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