Two-view tomosynthesis outperforms 2D mammography but only for readers with the least experience. The benefits were seen for both masses and microcalcification. No differences in classification accuracy was seen between and 2D mammography and single-view tomosynthesis.

Screening is the only proven approach to reduce the mortality of breast cancer, but significant numbers of breast cancers remain undetected even when all quality assurance guidelines are implemented. With the increasing adoption of digital mammography systems, image processing may be a key factor in the imaging chain. Although to our knowledge statistically significant effects of manufacturer-recommended image processings have not been previously demonstrated, the subjective experience of our radiologists, that the apparent image quality can vary considerably between different algorithms, motivated this study. This article addresses the impact of five such algorithms on the detection of clusters of microcalcifications. A database of unprocessed (raw) images of 200 normal digital mammograms, acquired with the Siemens Novation DR, was collected retrospectively. Realistic simulated microcalcification clusters were inserted in half of the unprocessed images. All unprocessed images were subsequently processed with five manufacturer-recommended image processing algorithms (Agfa Musica 1, IMS Raffaello Mammo 1.2, Sectra Mamea AB Sigmoid, Siemens OPVIEW v2, and Siemens OPVIEW v1). Four breast imaging radiologists were asked to locate and score the clusters in each image on a five point rating scale. The free-response data were analyzed by the jackknife free-response receiver operating characteristic (JAFROC) method and, for comparison, also with the receiver operating characteristic (ROC) method. JAFROC analysis revealed highly significant differences between the image processings (F = 8.51, p < 0.0001), suggesting that image processing strongly impacts the detectability of clusters. Siemens OPVIEW2 and Siemens OPVIEW1 yielded the highest and lowest performances, respectively. ROC analysis of the data also revealed significant differences between the processing but at lower significance (F = 3.47, p = 0.0305) than JAFROC. Both statistical analysis methods revealed that the same six pairs of modalities were significantly different, but the JAFROC confidence intervals were about 32% smaller than ROC confidence intervals. This study shows that image processing has a significant impact on the detection of microcalcifications in digital mammograms. Objective measurements, such as described here, should be used by the manufacturers to select the optimal image processing algorithm.

The realistic appearance of the 3D models of microcalcification clusters, whether malignant or benign clusters, was confirmed for 2D digital mammography images and the breast tomosynthesis datasets; this database of clusters is suitable for use in future observer performance studies related to the detectability of microcalcification clusters. Such studies include comparing 2D digital mammography to breast tomosynthesis and comparing different reconstruction algorithms.

The signal difference-to-noise ratio (SDNR) between aluminium sheets and a homogeneous background was measured for various radiation qualities and breast thicknesses to determine the optimal radiation quality when using a Novation DR mammography system. Breast simulating phantoms, with a thickness from 2 cm to 7 cm, and aluminium sheet, with a thickness of 0.2 mm, were used. Three different combinations of anode/filter material and a wide range of tube voltages were employed for each phantom thickness. Each radiation quality was studied using three different dose levels. The tungsten (W) anode and rhodium (Rh) filter combination achieved the specified SDNR at the lowest mean glandular dose for all the phantom thicknesses and X-ray tube voltages. The difference between the doses for different anode/filter combinations increased with the phantom thickness. For a 5-cm phantom, with a peak tube voltage of 27 kV and a SDNR of 5, the mean glandular dose associated with the use of W/Rh was reduced by 49% when compared to the molybdenum/molybdenum (Mo/Mo) anode/filter combination and by 33% when compared to Mo/Rh. Based on these measurements, the use of the W/Rh anode/filter can be recommended. It remains important, however, to select the appropriate dose level.

Medical device manufacturers are increasingly applying artificial intelligence (AI) to innovate their products and to improve patient outcomes. Health institutions are also developing their own algorithms, to address specific needs for which no commercial product exists.Although AI-based algorithms offer good prospects for improving patient outcomes, their wide adoption in clinical practice is still limited. The most significant barriers to the trust required for wider implementation are safety and clinical performance assurance .Qualified medical physicist experts (MPEs) play a key role in safety and performance assessment of such tools, before and during integration in clinical practice. As AI methods drive clinical decision-making, their quality should be assured and tested. Occasionally, an MPE may be also involved in the in-house development of such an AI algorithm. It is therefore important for MPEs to be well informed about the current regulatory framework for Medical Devices.The new European Medical Device Regulation (EU MDR), with date of application set for 26 of May 2021, imposes stringent requirements that need to be met before such tools can be applied in clinical practice.The objective of this paper is to give MPEs perspective on how the EU MDR affects the development of AIbased medical device software. We present our perspective regarding how to implement a regulatory roadmap, from early-stage consideration through design and development, regulatory submission, and post-market surveillance. We have further included an explanation of how to set up a compliant quality management system to ensure reliable and consistent product quality, safety, and performance .

Multivariate analysis revealed that lesion size, distance to carina, and segment were predictors of diagnostic yield. The presence of a bronchus sign substantially increased the diagnostic yield through the visualization yield.

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To assess current perceptions, practices and education needs pertaining to artificial intelligence (AI) in the medical physics field. Methods: A web-based survey was distributed to the European Federation of Organizations for Medical Physics (EFOMP) through social media and email membership list. The survey included questions about education, personal knowledge, needs, research and professionalism around AI in medical physics. Demographics information were also collected. Responses were stratified and analysed by gender, type of institution and years of experience in medical physics. Statistical significance (p < 0.05) was assessed using paired t-test. Results: 219 people from 31 countries took part in the survey. 81% (n = 177) of participants agreed that AI will improve the daily work of Medical Physics Experts (MPEs) and 88% (n = 193) of respondents expressed the need for MPEs of specific training on AI. The average level of AI knowledge among participants was 2.3 ± 1.0 (mean ± standard deviation) in a 1-to-5 scale and 96% (n = 210) of participants showed interest in improving their AI skills. A significantly lower AI knowledge was observed for female participants (2.0 ± 1.0), compared to male responders (2.4 ± 1.0). 64% of participants indicated that they are not involved in AI projects. The percentage of female leading AI projects was significantly lower than the male counterparts (3% vs 19%). Conclusions: AI was perceived as a positive resource to support MPEs in their daily tasks. Participants demonstrated a strong interest in improving their current AI-related skills, enhancing the need for dedicated training for MPEs.