Objectives: : To analyze the influence of multiplanar reformations of thin-collimated multidetector computed tomography datasets on low-contrast performance.
Materials and methods: : A low-contrast phantom simulating focal hypodense lesions (-20 HU object contrast) was scanned on a 64-slice spiral CT scanner at 4 different dose levels (25 mAs, 50 mAs, 100 mAs, 200 mAs, and no dose modulation). Other scanner parameters were as follows: tube voltage = 120 kVp, rotation time = 0.8 s, reconstructed slice thickness = 0.625 mm, reconstruction interval = 0.5 mm, reconstruction kernel = standard. Coronal reformations were created using the open-source software OsiriX. A sliding-thin-slab (STS) averaging algorithm was applied to each axial and each reformatted dataset with an increasing slab thickness from 1 to 20 slices. The low-contrast performance of all datasets was calculated semiautomatically using a reader-independent, statistical approach and is expressed as the visibility index. The results were analyzed for differences between the coronal reformations and the original axial datasets. In addition, the statistical approach used herein was validated against a reader study.
Results: : The visibility index of the coronal reformatted datasets over all lesion sizes was inferior when compared with the original axial datasets and reached 75.4% (±11.7%), 79.9% (±16.3%), 79.4% (±5.5%), and 93.7% (±14.6%) for dose levels of 25, 50, 100, and 200 mAs, respectively. The overall mean low-contrast performance was 82.1% of the axial dataset (P < 0.05, except for 200 mAs). The deterioration of low-contrast performance was inversely correlated with lesion size (R = 0.91). The use of the STS averaging algorithm significantly improved image quality for all datasets (112.6%-180.2%) with the beneficial effect being stronger for the coronal reformations. There was no statistically significant difference in the evaluation of low-contrast performance between the statistical approach and the ready study.
Conclusion: : Coronal reformations of thin-collimated multidetector computed tomography datasets show a significant reduction of low-contrast performance when compared with the original axial dataset, especially in high noise data. The use of an STS averaging algorithm had a significant benefit for both, coronal and axial orientations. The effect was more pronounced with coronal reformations and should be routinely applied to improve image quality.