Automatic image quality quantification and mapping with an edge-preserving mask-filtering algorithm

Abstract

Imaging modalities in digital radiology produce large amounts of data for which image quality should be determined in order to validate the diagnostic operation. To develop an automatic method for image quality assessment. A filtering algorithm using a moving square mask was applied to create a map of filtered local intensity and noise values. Image quality scores were calculated from the filtered image data. The procedure was applied to technical and anthropomorphic (radiosurgery verification phantom [RSVP] head) phantom images obtained with varying radiation dose, field of view (FOV), and image content. The method was also applied to a clinical computed tomography (CT) brain image. The image quality score (IQs) of the phantom images increased from 0.51 to 0.82 as the radiation dose (CTDIvol) increased from 9.2 to 74.3 mGy. Correlation of the IQs with the pixel noise was R(2)=0.99. The deviation (1 SD) of IQs was 2.8% when the reconstruction FOV was set between 21 and 25 cm. The correlation of IQs with the pixel noise was R(2)=0.98 with variable image contents and dose. Automatic tube current modulation applied to the RSVP phantom scan reduced the variation in the calculated image quality score by about 60% compared to the use of a fixed tube current. The image quality score provides an efficient tool for automatic quantification of image quality. The presented method also produces a 2D image quality map, which can be used for further image analysis.