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Objective: To design a device that can support the breast during phase-contrast tomography, and characterise its fit parameterisation and comfort rating., Methods: 27 participants were recruited to trial a system for breast support during simulated phase contrast imaging, including being positioned on a prone imaging table while wearing the device. Participants underwent a photogrammetry analysis to establish the geometric parameterisations. All participants trialled a single-cup design while 14 participants also trialled a double-cup with suction holder and all completed a series of questionnaires to understand subjective comfort., Results: Photogrammetry revealed significant positive correlations between bra cup volume and measured prone volume ( p < 0.001), and between "best fit" single-cup holder volume and measured prone volume ( p < 0.005). Both holders were suitable devices in terms of subjective comfort and immobilisation while stationary. However, some re-engineering to allow for quick, easy fitting in future trials where rotation through the radiation beam will occur is necessary. Light suction was well-tolerated when required., Conclusion: All participants indicated the table and breast support devices were comfortable, and they would continue in the trial., Advances in Knowledge: Phase contrast tomography is an emerging breast imaging modality and clinical trials are commencing internationally. This paper describes the biomedical engineering designs, in parallel with optimal imaging, that are necessary to measure breast volume so that adequate breast support can be achieved. Breast support devices have implications for comfort, motion correction and maximising breast tissue visualisation.

The information captured by the gist signal, which refers to radiologists' first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 (p = 0.82, CI: -0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 (p < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62-0.86), 0.79 (CI: 0.63-0.89), and 0.88 (CI: 0.79-0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection., (© 2021. The Author(s).)