The purpose of this study was to quantitatively evaluate the effect of reducing radiation dose (i.e., mAs) on
lesion detection in head CT examinations. We used a simulation package (Syngo Explorer) to reconstruct
5-mm thick CT images of the brain of one patient pertaining to the centrum semiovale, the basal ganglia,
and the sella turcica. Lesion detection was measured using two Alternate Forced Choice (2-AFC)
experiments that measure the lesion contrast (I92%) corresponding to a detection accuracy of 92%. Two
observers performed experiments to investigate detection of low contrast lesions with four sizes ranging
from 3 mm to 10 mm and at four x-ray beam intensities ranging from 105 mAs to 300 mAs. Results were
plotted as log[I92%] versus log[mAs], and the slopes were measured for each lesion size. Lowering the mAs
always reduced lesion detection performance in all images, and for all lesion sizes. Average slopes of the
I92% versus mAs curves were -0.23 for 3 mm lesions, -0.16 for 4.5 mm lesions, and ~-0.11 for the 7 and 10
mm lesions. For the smallest lesions investigated (3 mm), doubling the x-ray intensity improved lesion
detection performance by ~ 15%, whereas for the largest sized lesions (7 and 10 mm), doubling the tube
current improved lesion detection performance by ~ 7%. The observed improvements in detection
performance are markedly lower than predicted by the Rose model where a doubling of the tube current
would be expected to improve detection performance by 29% at all lesion sizes.
The purpose of this study was to generate contrast detail (CD) curves for low contrast mass lesions
embedded in images obtained in head and neck CT examinations. Axial head and neck CT slice images
were randomly chosen from patients at five different levels. All images were acquired at 120 kV, and
reconstructed using a standard soft tissue reconstruction filter. For each head CT image, we measured
detection of low contrast mass lesions using a 2 Alternate Forced Choice (2-AFC) experimental paradigm.
In an AFC experiment, an observer identifies the lesion location in one of two regions of interest. After
performing 128 sequential observations, it is possible to compute the lesion contrast corresponding to a
92% accuracy of lesion detection (i.e., I<sub>92%</sub>). Five lesion sizes were investigated ranging from 4 mm to 12.5
mm, with the experimental order randomized to eliminate learning curve as well as observer fatigue.
Contrast detail curves were generated by plotting log[I<sub>92%</sub>] versus log[lesion size]. Experimental slopes
ranged from ~ -0.1 to ~ -0.4. The slope of the CD curve was directly related to the complexity of the
anatomical structure in the head CT image. As the apparent anatomical complexity increased, the slope of
the corresponding CD curve was reduced. Results from our pilot study suggest that anatomical structure is
of greater importance than quantum mottle, and that the type of anatomical background structure is an
important determinant of lesion detection in CT imaging.