softMip: a novel projection algorithm for ultra-low-dose computed tomography.

Abstract

Two projection algorithms are currently available for viewing computed tomography (CT) data sets: average projection (AVG) and maximum intensity projection (MIP). Although AVG images feature good suppression of image noise but reduced edge sharpness, MIP images are characterized by good edge sharpness but also amplify image noise. Ultra-low-dose (ULD) CT has very low radiation exposure but has high image noise. Maximum intensity projection images of ULDCT data sets amplify image noise and are therefore unsuitable for image interpretation in the routine clinical setting. We developed a synthesis of both algorithms that tries to unite the respective advantages. The resulting softMip algorithm was implemented in C++ and installed on a workstation. Depending on the settings used, softMip images can represent any graduation between MIP and AVG. The new softMip algorithm was evaluated and compared with MIP and AVG in terms of image noise and edge sharpness in a series of phantom experiments performed on 7 different CT scanners. Furthermore, image quality of the transition from AVG to MIP by means of softMip was compared with the image quality of simply blending AVG and MIP. Images generated with softMip showed less image noise than MIP images (P < 0.0005) and higher edge sharpness than AVG images (P< 0.0005). The softMip transition from AVG to MIP had a better ratio of edge sharpness and image noise than blending (P < 0.0005). Our results suggest that softMip is a very promising projection procedure for postprocessing cross-sectional image data, especially ULDCT data sets.