Optimization of low-contrast detectability in thin-collimated modern multidetector CT using an interactive sliding-thin-slab averaging algorithm.

OBJECTIVES: To analyze the effects of the sliding-thin-slab averaging algorithm on low-contrast performance in MDCT imaging and to find reasonable parameters for clinical routine work.
MATERIALS AND METHODS: A low-contrast phantom simulating hypodense lesions (20 HU object contrast) was scanned with a 16-slice spiral CT scanner using different mAs-settings of 25, 50, 100, and 195 mAs. Other scan parameters were as follows: tube voltage = 120 kVp, slice collimation = 0.625 mm, pitch = 1.375 (high speed), reconstruction interval = 0.5 mm. Images were reconstructed with soft, standard, and bone algorithms, resulting in a total of 12 datasets. A sliding-thin-slab averaging algorithm was applied to these primary datasets, systematically varying the slab thickness between 0.5 and 5.0 mm. The low-contrast performance of the resulting datasets was semi-automatically analyzed using a statistical reader-independent approach: A size-dependent analysis of the image noise within the phantom was used to empirically generate a contrast discrimination function (CDF). The ratio between the actual contrast and the minimum contrast necessary for the detection (as given by the CDF) was calculated for all lesions in each dataset and used to evaluate the low-contrast detectability of the different lesions at increasing slab thickness. The results were compared with the original datasets to calculate the improvement in low-contrast detectability.
RESULTS: Using the sliding-thin-slab algorithm, low-contrast performance was increased by a factor between 1.1 and 1.7 when compared with the primary dataset. The improvement of the visibility index at optimal slab thickness when compared with the original slice thickness (0.625 mm) was statistically significant (P < 0.05, Student t test) for the following datasets: 8 mm: all datasets; 6 mm: 25 mAs/soft, 195 mAs/bone, 25 mAs/bone; 5 mm: 25 mAs/soft, 25 mAs/bone. The ideal slab thickness over all datasets was 43% (+/-3%) of the diameter of the lesion to be detected.
CONCLUSIONS: The use of an interactive sliding-thin-slab averaging algorithm can be readily applied to optimize low-contrast detectability in thin-collimated CT datasets. As a general rule for daily routine, a slice thickness of approximately 2.5 to 3.0 mm can be regarded as a reasonable preset, resulting in an optimized detectability of lesions with a diameter of 5 mm and above.