Scatter reduction in mammography using statistical estimation techniques

Previously, we have shown the effectiveness of using Bayesian image estimation (BIE) to reduce scatter and increase the contrast to noise ratio (CNR) in digital chest radiography. Here, we investigate the use of BIE to reduce scatter and increase CNR on digital mammographic data. Calibrated photostimulable phosphor digital images were obtained of the American College of Radiologists (ACR) mammographic phantom at several different exposures. An iterative Bayesian estimation algorithm was used to process this data. Residual scatter fractions (RSF) and CNR were computed. Resolution was visually inspected. These results were compared to those of a mammogram acquired at standard clinical imaging parameters using an anti-scatter grid for scatter reduction. On average, at all exposure levels. BIE reduced scatter fractions from 57% to 6%, while a grid only reduced RSF to 31%. At similar exposure levels, BIE processing improved CNR to 21.6, while a grid produced images with a CNR of 15.8. At an exposure level of 37% less than the standard exposure, BIE improved CNR to 18.9. A visual assessment of resolution using the objects in the phantom showed no reduction of resolution. In some images, phantom masses appeared more readily apparent. BIE processing of mammographic data can reduce scatter and increase image CNR. This type of image processing may potentially allow for decreased radiation dose to the patient with no loss of image quality. BIE as a method for scatter compensation in mammography is very promising. This preliminary work shows improvement in CNR to values greater than that of a standard grid.