Comparing image quality of five breast tomosynthesis systems based on radiologists' reviews of phantom data.

BACKGROUND: Previous studies have shown differences in technical image quality between digital breast tomosynthesis (DBT) systems. However, quantitative image quality measurements may not necessarily fully reflect the clinical performance of DBT. PURPOSE: To study the subjective image quality of five DBT systems manufactured by Fujifilm, GE, Hologic, Planmed, and Siemens using phantom images. MATERIAL AND METHODS: A TOR MAM test object with polymethyl methacrylate plates was imaged on five DBT systems from different vendors. Three DBT acquisitions were performed at mean glandular doses of 1.0 mGy, 2.0 mGy, and 3.5 mGy while maintaining a constant phantom set-up. Eight DBT acquisitions with different test plate positions and phantom set-up thicknesses were performed at clinically applied dose levels. Additionally, three conventional two-dimensional mammogram images were acquired with different phantom thicknesses. Six radiologists ranked the systems based on the visibilities of the targets seen in the phantom images. RESULTS: In the DBT acquisitions performed at comparable dose levels, one system differed significantly from all other systems in microcalcification scores. When using site-specific DBT protocols, significant differences were found between the devices for microcalcification, filament, and low-contrast targets. A strong correlation was observed between the reviewer scores and radiation doses in DBT acquisitions, whereas no such correlation was observed in the 2D acquisitions. CONCLUSION: In DBT acquisitions, dose level was found to be a major factor explaining image quality differences between the systems, regardless of other acquisition parameters. Most DBT systems performed equally well at similar dose levels.

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.

A phantom study comparing technical image quality of five breast tomosynthesis systems.

BACKGROUND: Digital breast tomosynthesis (DBT) is a three-dimensional breast imaging method. DBT vendors employ various approaches in both image acquisition and data processing, which may affect image quality and radiation exposure to patients. OBJECTIVE: This study aimed to evaluate the performance of five DBT systems: Fujifilm Amulet Innovality (using both a standard mode and high-resolution mode), GE Senographe Essential, Hologic Selenia Dimensions, Planmed Clarity 3D, and Siemens Mammomat Inspiration. MATERIALS AND METHODS: The performance of each device and imaging technique was evaluated and compared by phantom measurements performed with four quality assurance phantoms. Technical image quality assessments consisted of measuring artefact extent, in-plane resolution, relative noise power spectrum, and geometric accuracy. RESULTS: Artefact spreading varied remarkably between the devices, and the full width at half maximum values of artefact spread functions varied from 3.5 mm to 10.7 mm. Noticeable in-plane resolution anisotropy, determined using modulation transfer function (MTF) analysis, was typically observed between tube travel direction and chest wall-nipple direction. The MTF50 varied from 1.1 mm-1 to 1.6 mm-1 and from 1.5 mm-1 to 4.1 mm-1 in the tube travel and chest wall-nipple directions, respectively. Moreover, distinctly different noise power spectra were observed between the systems. The geometric accuracy in every system was within 0.5%. CONCLUSION: Technical image quality assessments with image quality phantoms revealed remarkable differences in artefact spread, in-plane resolution, and noise properties between the DBT systems and imaging methods.

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.