Devising a deep neural network based mammography phantom image filtering algorithm using images obtained under mAs and kVp control

We study whether deep neural network based algorithm can filter out mammography phantom images that will pass or fail. With 543 phantom images generated from a mammography unit, we created VGG16 based phantom shape scoring models (multi-and binary-class classifiers). Using these models we designed filtering algorithms that can filter failed or passed phantom images. 61 phantom images obtained from two different medical institutions were used for external validation. The performances of the scoring models show an F1-score of 0.69 (95% confidence interval (CI) 0.65, 0.72) for multi-class classifiers and an F1-score of 0.93 (95% CI 0.92, 0.95) and area under the receiver operating characteristic curve of 0.97 (95% CI 0.96, 0.98) for binary-class classifiers. A total of 42 of the 61 phantom images (69%) were filtered by the filtering algorithms without further need for assessment from a human observer. This study demonstrated the potential to reduce the human workload from mammographic phantom interpretation using the deep neural network based algorithm.

Automated image quality assessment of mammography phantoms: a systematic review

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.

Convolutional neural network -based phantom image scoring for mammography quality control

BACKGROUND

Visual evaluation of phantom images is an important, but time-consuming part of mammography quality control (QC). Consistent scoring of phantom images over the device's lifetime is highly desirable. Recently, convolutional neural networks (CNNs) have been applied to a wide range of image classification problems, performing with a high accuracy. The purpose of this study was to automate mammography QC phantom scoring task by training CNN models to mimic a human reviewer.

METHODS

Eight CNN variations consisting of three to ten convolutional layers were trained for detecting targets (fibres, microcalcifications and masses) in American College of Radiology (ACR) accreditation phantom images and the results were compared with human scoring. Regular and artificially degraded/improved QC phantom images from eight mammography devices were visually evaluated by one reviewer. These images were used in training the CNN models. A separate test set consisted of daily QC images from the eight devices and separately acquired images with varying dose levels. These were scored by four reviewers and considered the ground truth for CNN performance testing.

RESULTS

Although hyper-parameter search space was limited, an optimal network depth after which additional layers resulted in decreased accuracy was identified. The highest scoring accuracy (95%) was achieved with the CNN consisting of six convolutional layers. The highest deviation between the CNN and the reviewers was found at lowest dose levels. No significant difference emerged between the visual reviews and CNN results except in case of smallest masses.

CONCLUSION

A CNN-based automatic mammography QC phantom scoring system can score phantom images in a good agreement with human reviewers, and can therefore be of benefit in mammography QC.

Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data

Digital breast tomosynthesis (DBT) has become a well-established breast imaging technique, whose performance has been investigated in many clinical studies, including a number of prospective clinical trials. Results from these studies generally point to non-inferiority in terms of microcalcification detection and superior mass-lesion detection for DBT imaging compared to digital mammography (DM). This modality has become an essential tool in the clinic for assessment and ad-hoc screening but is not yet implemented in most breast screening programmes at a state or national level. While evidence on the clinical utility of DBT has been accumulating, there has also been progress in the development of methods for technical performance assessment and quality control of these imaging systems. DBT is a relatively complicated ‘pseudo-3D’ modality whose technical assessment poses a number of difficulties. This paper reviews methods for the technical performance assessment of DBT devices, starting at the component level in part one and leading up to discussion of system evaluation with physical test objects in part two. We provide some historical and basic theoretical perspective, often starting from methods developed for DM imaging. Data from a multi-vendor comparison are also included, acquired under the medical physics quality control protocol developed by EUREF and currently being consolidated by a European Federation of Organisations for Medical Physics working group. These data and associated methods can serve as a reference for the development of reference data and provide some context for clinical studies.

Automated daily quality control analysis for mammography in a multi-unit imaging center

BACKGROUND

The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users.

PURPOSE

To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center.

MATERIAL AND METHODS

An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated.

RESULTS

The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center.

CONCLUSION

Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

Dose sensitivity of three phantoms used for quality assurance in digital mammography

Technical quality assurance (QA) is one of the key issues in breast cancer screening protocols. For this QA task, three different methods are commonly used to assess image quality. The European protocol suggests a contrast-detail phantom (e.g. the CDMAM phantom), while in North America the American College of Radiology (ACR) accreditation phantom is proposed. Alternatively, phantoms based on image quality parameters from applied system theory such as the noise-equivalent number of quanta (NEQ) are applied (e.g. the PAS 1054 phantom). The aim of this paper was to correlate the changes in the output of the three evaluation methods (CDMAM, ACR and NEQ) with changes in dose. We varied the time-current product within a range of clinically used values (40-140 mAs, corresponding to 3.5-12.4 mGy entrance dose and detector dose of 32-110 μGy). For the ACR phantom, the examined parameter was the number of detected objects. With the CDMAM phantom we chose the diameters 0.10, 0.13, 0.20, 0.31 and 0.5 mm and recorded the threshold thicknesses. With respect to the third method, we evaluated the NEQ at typical spatial frequencies to calculate the relative changes in NEQ. Plotting NEQ versus dose increment shows a linear relationship and can be described by a linear function (with R > 0.99). Every manually selectable current- time product increment can be detected. With the ACR phantom, the number of detected objects increases only in the lower dose range and reaches saturation at about 9 mGy entrance dose (80 μGy detector dose). The CDMAM can detect a 50% increase in dose over the examined dose range with all five diameters, although the increases of threshold thickness are not monotonous. We conclude that an NEQ-based method has the potential to replace the established detail phantom methods to detect dose changes in the course of QA.