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

Background: Imaging modalities in digital radiology produce large amounts of data for which image quality should be determined in order to validate the diagnostic operation.

Purpose: To develop an automatic method for image quality assessment.

Material and Methods: 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.

Results: The image quality score (IQs) of the phantom images increased from 0.51 to 0.82 as the radiation dose (CTDI_vol) increased from 9.2 to 74.3 mGy. Correlation of the IQs with the pixel noise was R² = 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 R² = 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.

Conclusion: 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.