Background: Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers. Purpose: To systematically review the image analysis software used in the assessment of 2D image quality using mammography phantoms. Material and Methods: A systematic search of multiple databases was performed from inception to July 2020 for articles that incorporated computerized analysis of 2D images of physical mammography phantoms to determine image quality. Results: A total of 26 studies were included, 12 were carried out using direct digital imaging and 14 using screen film mammography. The ACR phantom (model-156) was the most frequently evaluated phantom, possibly due to the lack of accepted standard software. In comparison to the inter-observer variations, the computerized image analysis was more consistent in scoring test objects. The template matching method was found to be one of the most reliable algorithms, especially for high-contrast test objects, while several algorithms found low-contrast test objects to be harder to distinguish due to the smaller contrast variations between test objects and their backgrounds. This was particularly true for small object sizes. Conclusion: Image analysis software was in agreement with human observers but demonstrated higher consistency and reproducibility of quality evaluation. Additionally, using computerized analysis, several quantitative metrics such as contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) could be used to complement the conventional scoring method. Implementing a computerized approach for monitoring image quality over time would be crucial to detect any deteriorating mammography system before clinical images are impacted.

Purpose: To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images.Methods: Twenty‐seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses).Results: The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61 mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25 mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15 mm.Conclusions: Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.

Background: Quality control (QC) in mammography is crucial for breast imaging and it relies predominantly on subjective assessment of image quality (IQ) in phantom. Purpose: To validate the efficacy of automated software assessment in comparison with human observers. Material and Methods: A total of 80 processed images of the ACR DM phantom were collected from mammography systems supplied by five different vendors. IQ was assessed using in-house developed software and 11 human observers (five experts and six non-experts). Three different target objects of various sizes and shapes were scored (six fibers, six speck groups, six masses) for each image. Results: The software assessment demonstrated good to moderate agreement with the human observers’ scoring of target objects, especially with expert observers. The intraclass correlation coefficient (ICC) values between the software and all observers were 0.77, 0.61, and 0.78 for fibers, speck groups, and masses, respectively. There was variability in the scoring of low contrast objects, especially with non-expert observers. Meanwhile, high contrast objects (e.g. specks) showed the highest visibility rate and received more consistent scores. The comparison between the software and all observers indicated mean differences of 0.53 for fibers, 0.27 for specks, and 0.36 for masses. Conclusion: The software assessment effectively scored the phantom IQ, demonstrating comparability to assessments made by human observers, regardless of the image acquisition factors and manufacturers’ design specifications. This software should therefore ensure consistent IQ assessment that mitigates the limitations of subjective human assessment in mammography quality testing.