Image deconvolution as an aid to mammographic artefact identification I : basic techniques

Digital mammography has the potential to provide radiologists with a tool which can detect tumours earlier and with greater accuracy then film based systems. Although a digital mammography system can provide much greater contrast when compared with a conventional film system, the ability to detect small artifacts associated with breast cancer is limited by a reduced spatial resolution due to screen unsharpness and scatter induced fog. In this paper we model the radiological image formation process as the convolution of a linear shift invariant point spread function (PSF) with the projected tissue density source function. We model the PSF as consisting of two components screen unsharpness and scatter. We present results from a method designed to compensate for screen unsharpness. The screen PSF was measured and subsequently used in an iterative deconvolution algorithm which incorporated wavelet based de-noising between steps in order to reduce noise amplification. When applied to a University of Leeds TORMAX breast phantom the results show as much as a two-fold improvement in resolution at the 50 percent MTF level. Our results show that the regularized deconvolution algorithm significantly improves the signal-to-noise ratio in the restored image.