Using virtual clinical trials to assess objective image quality metrics in the task of microcalcification localization in digital mammography

Many works have investigated methods to assess the quality of mammography images using objective image quality metrics. However, few studies have evaluated the ability of these metrics to predict the performance of human observers on specific tasks related to mammographic examination that are highly dependent on image quality. The propose of this work is to evaluate the quality of digital mammography acquired at a range of radiation doses through a set of objective metrics and to compare the results with the performance of human observers in the task of locating microcalcification clusters in these images. A dataset of 100 synthetic mammograms was simulated using a virtual clinical trials software. Microcalcification clusters of different sizes and contrasts were computationally inserted into the images. Acquisitions with five different radiation doses were simulated using a noise injection method proposed in a previous work. Four medical physicists with experience in analysis of mammographic images participated in the microcalcification cluster localization tests. The quality of digital mammography images was assessed considering nine well-known objective metrics. The metrics were calculated on both the raw data (DICOM ‘for processing’ tag) and the processed images (DICOM ‘for presentation’ tag). Finally, the association between readers performance and image quality index was conducted by calculating the percentage variation of all metrics as a function of radiation dose, taking the standard dose as a reference. Although the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are the most used in the literature, our results showed that Quality Index based on Local Variance (QILV) is the objective metric that best describes the behavior of human visual perception with the variation of radiation dose in digital mammography.