Topics covered in this article include:

• Introduction
• Low Contrast Measurements
  •  
    • Rose Model
    • References
• Contrast Detail Plot
• Contrast Detail Values
• Alternative Low Contrast Test

Introduction

Low contrast information is given for the following Catphan® models: 500, 504, 600, 700.

CTP515 Low Contrast Module 

The low contrast targets have the following diameters and contrasts:

Supra-slice target diameters       Subslice target diameters

2.0 mm                                        3.0 mm

3.0 mm                                        5.0 mm

4.0 mm                                        7.0 mm

5.0 mm                                        9.0mm

6.0 mm

7.0 mm

8.0 mm

9.0 mm

15.0 mm

Nominal target contrast levels

0.3%

0.5%

1.0%

Low Contrast Measurements

Low contrast performance in CT imaging is a difficult subject. Clearly it is related to the ability of a "system" to distinguish between two objects or regions with similar CT number or X-ray attenuation. It is known to depend on statistical noise levels, contrast of the signal, and the size of the signal. In many cases, small signals (in the 3-7 mm diameter range) with contrast levels of some 3-5 HU are typically difficult to discern even in relatively uniform backgrounds. However, it is well reported that the contrast level (C) and the diameter of the target (D), when combined as a product approaches a constant (C*D≅constant). Thus, a plot can be made of contrasts (C) that might be found at a given diameter (D). 

Because of the difficulty of the subject, much thought went into the construction of the low contrast algorithm. The analysis estimates noise from the standard deviation of the mean HUs of two rows of circles of each target size in the background.

At each target size (2-9mm and 15mm), an inner and outer row of ROIs (circles) are generated in the background as shown above. The mean HU for each circle is calculated and the standard deviation (SD) calculated from the set of means. If the standard deviation from the outer and inner circles deviates more than 10%, the algorithm uses the set with the lower standard deviation. A detection level for each target size is calculated, with a perceptibility factor of 4, as 4*SD. 

Rose Model

The Rose Model represents probably the simplest model for detection of circular targets in a relatively uniform background (other than the statistical noise fluctuations seen in medical images). Strictly speaking, the model applies to "white" noise, meaning the noise excursions are uncorrelated from point to point (pixel to pixel). This is not necessarily true in CT, but nevertheless works reasonably well.

In the Rose Model, the eye (or the computer in this case) puts a circle (ROI of Region of Interest) over a target with the same diameter as the target and estimates the mean signal intensity of the target. This is a visual or numerical integration of the CT numbers in the target as averaged over the target. This value is then compared to mean intensity values in identical sized circles in the background region where only noise is present. A test of significance is made to determine how many times the mean signal intensity would exceed the expected fluctuation in the regions where only noise is present. The best case usually requires the mean signal intensity to be at least two times the expected noise deviation in white noise, and probably twice that for CT correlated noise.

Consider the case of Signal (S) and Background Level (B); with CT# of signal, CT_S; and CT# of background, CT_B.

For CT, contrast is CT_S - CT_B; and the CT noise is σ(H). Then signal contrast to noise ratio is CT_S - CT_B/σ(H).

To estimate noise excursions in signal sized targets, we note that for white noise, the relative noise standard deviation decreases by the square root of the number of pixels in the profile. If the single pixel noise standard deviation is "σ " then the profile (area) noise is approximately σ (H)/√N; where N is the number of single pixels needed to obtain the full area of the circular target. 

Consider a profile through a 5mm, 0.3% target in an ideal scenario. Here we estimate background as 50H; ∴0.3% target is 53H. Remember in CT, 1% change from water represents 10H. 

Now consider the same profile, but with single pixel noise at approximately 2H present in the profile.

References

1. D.J. Goodenough, Psychophysical perception of computed tomography images. Radiology of the Skull and Brain; Technical Aspects of Computed Tomography, T.H. Newton and D. G. Potts, Eds, Vol. 5, pp. 3393-4021. Mosby, St Louis (1981).
2. D.J. Goodenough and K.E. Weaver, Factors related to low contrast resolution in CT scanners, Computerized Radiol. 8, Vol. 5, 297-308 (1984).

Contrast Detail Plot

The contrast detail plot corresponds to the contrast detail values. The plot is calculated using the formula contrast = constant/diameter. The algorithm computes the y-axis (contrast) values for every diameter rod (x-axis).

Contrast Detail Values

Low contrast values are based on three factors: nominal contrast, estimation of noise, and a detection level.

The nominal contrast is the difference in CT number between the background (HU_bg) and the target (HU_target). (HU_target - HU_bg)/10HU = %contrast To estimate the noise, two rows (inner and outer) of circles are generated for each target size (mm) and a standard deviation is calculated from the mean HUs for each circle. The detection level is a perceptibility factor, the algorithms use 4, times the SD for each target size.

More information on low contrast calculation can be found in the Low Contrast page of the Physics Background section of these help pages. 

Alternative Low Contrast Test