Purpose: We have evaluated the feasibility of using a software package to monitor the results of quality control (QC) testing on digital mammography units in the Ontario Breast Screening Program. The intent is to make the quality control process more efficient for the technologist and physicist, and improve the consistency of test performance and results interpretation. Methods: A DICOM service class provider, “GLADYS” (originally developed at University Hospitals Leuven) was installed at pilot screening sites. All images acquired on the sitesˈ mammography systems are automatically sent to GLADYS over the PACS networks. GLADYS recognizes QC images by predetermined patient names and performs an automated analysis, measuring various parameters and generating summary thumbnail images. Clinical images are de‐identified and the technique factors and dose are extracted for tracking. The QC and dose reports are sent by email to the central monitoring site. At the central site QC image measures are plotted and thumbnail images displayed for artefact evaluation. The patient header information is stored such that dose reports can be generated. Results: GLADYS has been installed at two remote screening sites, and locally for a total of six machines. QC and dose data have been collected for the past 5.5 months. 285 QC images have been analyzed. Artefacts and changes in automatic exposure control or detector behaviour are easily perceived. Dosimetry information from 18282 patient images has been collected, with an average mean glandular dose of 1.3 mGy. Conclusions: The automated analysis works well, and reduces the technologistˈs QC workload. The addition of features to allow for automated immediate feedback to the remote sites of test results to ensure rapid response to detected problems is under development. Incorporation of centralized automatic quality control has the potential to improve the consistency and reliability of the tests and results, while streamlining QC procedures.

Optimization of exposure parameters (target, filter, and kVp) in digital mammography necessitates maximization of the image signal-to-noise ratio (SNR), while simultaneously minimizing patient dose. The goal of this study is to compare, for each of the major commercially available full field digital mammography (FFDM) systems, the impact of the selection of technique factors on image SNR and radiation dose for a range of breast thickness and tissue types. This phantom study is an update of a previous investigation and includes measurements on recent versions of two of the FFDM systems discussed in that article, as well as on three FFDM systems not available at that time. The five commercial FFDM systems tested, the Senographe 2000D from GE Healthcare, the Mammomat Novation DR from Siemens, the Selenia from Hologic, the Fischer Senoscan, and Fuji's 5000MA used with a Lorad M-IV mammography unit, are located at five different university test sites. Performance was assessed using all available x-ray target and filter combinations and nine different phantom types (three compressed thicknesses and three tissue composition types). Each phantom type was also imaged using the automatic exposure control (AEC) of each system to identify the exposure parameters used under automated image acquisition. The figure of merit (FOM) used to compare technique factors is the ratio of the square of the image SNR to the mean glandular dose. The results show that, for a given target/filter combination, in general FOM is a slowly changing function of kVp, with stronger dependence on the choice of target/filter combination. In all cases the FOM was a decreasing function of kVp at the top of the available range of kVp settings, indicating that higher tube voltages would produce no further performance improvement. For a given phantom type, the exposure parameter set resulting in the highest FOM value was system specific, depending on both the set of available target/filter combinations, and on the receptor type. In most cases, the AECs of the FFDM systems successfully identified exposure parameters resulting in FOM values near the maximum ones, however, there were several examples where AEC performance could be improved.

Optimization of exposure parameters (target, filter, and kVp) in digital mammography necessitates maximization of the image signal‐to‐noise ratio (SNR), while simultaneously minimizing patient dose. The goal of this talk is to compare, for each of the major commercially available full field mammography (FFDM) systems, the impact of the selection of technique factors on image SNR and radiation dose for a range of breast thickness and tissue types. The comparison will be based on the results of a multi‐center phantom study. The five commercial FFDM systems tested, the Senographe 2000D from GE Healthcare, the Mammomat Novation from Siemens, and the Selenia from Hologic, the Fischer Senoscan, and Fuji's 5000MA used with a Lorad M‐IV mammography unit, are located at five different university test sites. Performance was assessed using all available x‐ray target and filter combinations and nine different phantom types (three compressed thicknesses, and three tissue composition types). Each phantom type was also imaged using the automatic exposure control (AEC) of each system to identify the exposure parameters used under automated image acquisition. The figure of merit (FOM) used to compare technique factors is the ratio of the square of the image SNR to the mean glandular dose (MGD). The results show that, for a given target/filter combination, in general FOM is a slowly changing function of kVp, with stronger dependence on the choice of target/filter combination. In all cases the FOM was a decreasing function of kVp at the top of the available range of kVp settings, indicating that higher tube voltages would produce no further performance improvement. For a given phantom type, the exposure parameter set resulting in the highest FOM value was system‐specific, depending on both the set of available target/filter combinations, and on the receptor type. Noise performance differed noticeably among the FFDM systems and played an important role in determining relative FOM values. In most cases, the AECs of the FFDM systems successfully identified exposure parameters resulting in FOM values near the maximum ones, however there were several examples where AEC performance could be improved. Educational Objectives: 1. become familiar with the effect of changing kVp, target material, and filtration on the mean glandular dose for a variety of breast. 2. become familiar with the effect of changing kVp, target material, and filtration on image signal and noise for specific commercial FFDM systems. 3. learn how the exposure technique factors selected for a variety of breast types by the AECs of current FFDM systems compare with the technique factors resulting in optimal FOM values.

The SenoScan full-field digital mammography scanner uses a scanning slot detector that is 10 mm wide and 220 mm long. The X-ray beam is collimated to just outside the area of the detector. One important advantage of slot scanning is its inherent scatter rejection. As previously reported, the SenoScan slot scatter rejection is better than that obtained using a 3.5:1 mammography grid, and somewhat worse than that with a 5:1 grid. Additional scatter reduction can potentially improve the contrast in images of thick breasts. We evaluate a custom-designed grid for the slot scanning system. The grid is one-dimensional, offering scatter rejection along the longitudinal axis of the detector. We evaluate the reduction in scatter fraction, grid absorption and changes in the signal-difference-to-noise ratio (SDNR). Based on phantom studies, our results show effective scatter reduction by the grid with minimal reduction of SDNR. Grid absorption and scatter elimination do not necessarily lead to an increase in patient dose, especially if there is a improvement in the number of digital values in the image that are within the useful dynamic range of the detector. A benefit of removing the scatter contribution is an improvement in system dynamic range, because electronic detector gain adjustments can compensate for the drop in the digital pixel values.