Detector Low dose Medium dose High dose
CRc +7% +2% - 1%
CSI +2% +7% -9%
7.10. Discussion on methods
7.10.1. Accuracy o f model
It is not necessary for the conversion methodology to produce a perfect match to the target system. The simulated images just need to be representative of a particular class of imaging system rather than to exactly match a specific system. These results of the validation seem to be adequate for that purpose. The image quality of the simulated images can be quantified by converting images of the CDMAM test object.
It is highlighted that the purpose of the conversion methodology presented is to correct the image for differences in noise source and changes in average signal to appear as if acquired on a different detector. The beam qualities of the original and target images must be the same, although the reference beam quality for the measurement o f the noise coefficients may be different. If necessary, the noise coefficients can be corrected to correspond to beam quality of the images to be converted. Conversion of an image acquired at one beam quality and to appear as if acquired at a different beam quality would require changes to the contrast within the image and this is beyond the scope o f the present work.
The conversion method presented does not require that both the reference beam quality and beam quality of the image being modified are the same. Therefore, this method is suitable for adapting mammograms to appear with a different image quality, providing an estimate of the compressed breast thickness and average glandularity can be made. The results from the conversion of the images of the anatomical breast phantom indicate that this approach works with clinical images.
7.10.2. Assumptions in the conversion methodology
There are image properties that the conversion model cannot correct. The ASEh system in this study has a presampled MTF value of about 0.5 at the Nyquist frequency; this means that the signal and noise will be aliased in the image. The noise aliasing is included in the conversion as part of the noise power spectra, but cannot be corrected to match the expected aliasing in the target system.
In order to undertake the image conversion, a number of simplifying assumptions have been made. There are small variations in the MTF due to the change in the angle of incidence for the x-ray beam across the detector (Hajdok and Cunningham 2004). The model does not take this into consideration and it has been assumed that equation 7.1 is true over the whole detector. The MTF reduction is negligible up to 5 m m '\ Above this frequency the severest effects are at the furthest edge from the chest wall, so there will be minimal effect on mammograms.
It was assumed that the magnitude of the additional scatter was a constant across the detector and that the detector has the same response to scattered and primary x-ray photons.
The methodology has been designed for the addition of noise sources which are random such as quantum and electronic. However, the phase of the strueture noise will not be random and in fact the added structure using this model is in effect an average structure noise. The NFS of the simulated structure noise will be the same magnitude but the visual appearance could be different.
It has also been assumed that the beam qualities used for the original CDMAM images and for the noise coefficients measurements match. In the real CDMAM images there is a larger proportion of scattered radiation which lowers the average beam energy and the anti scatter grid will harden the beam. Overall, these are small changes in the beam quality and the changes in the quantum noise correction factor o f the detectors will be small.
Despite the simplifying assumptions, a close match was found between the CDMAM threshold gold thickness for the simulated and target images for the conversions to CSI and CRc images. A particular difficulty with the method of validation was that the digital detectors were used with different imaging systems. Thus, the methodology also had to account for differences due to the x-ray system, and it is difficult to know whether the differences found between the simulated and the target images were due to corrections for the detector or the x-ray system.
7.10.3. Advantages o f the adaption method
As part o f the image degradation, noise needs to be added. The conversion method is quite flexible and can allow processes such as a dose increase to the detector providing the added noise from other changes is greater. In some cases, it was not possible to compensate at all spatial frequencies and an NCF was used to correct the overall magnitude of the noise. This introduced slight errors at some spatial frequencies. For this to be a valid approach the NCF should be close to 1 and generally the NCF was a small correction (>0.84).
The quantum noise tends to be the dominant noise source in most situations. However at the dose extremes it is important to include the electronic and structure noise, especially for electronic noise in ASEs detector and structure noise for the CRc detector. This also applies to simulations of tomosynthesis images. To accurately simulate low dose tomosynthesis images the methodology needs to account for electronic noise.
It has been shown here that images can be suceessfully converted for doses covering a range of quarter to quadruple normal dose levels, providing that the image quality of the target image is lower than the original image. Validation of doses outside this range was not possible because of the limited range o f detail thickness of the CDMAM test object.
7.11. CONCLUSIONS
This work has demonstrated how digital mammography images obtained with a particular system can be degraded in terms of noise magnitude and colour, sharpness and contrast to realistically simulate the appearance of images obtained by a different detector and anti-scatter grid. Images can be changed provided the NFS and MTF are known or can be estimated for the original and target systems.
This method is used in the next chapter as part of an observer study. The observer study converts mammograms which are normal or contain a cancer to appear as if acquired on four different types of detectors. This study is statistically strong as the number o f variables has been reduced such as patient and positioning variability associated with acquiring patient images on different systems