No abstract

Digital breast tomosynthesis (DBT) is under consideration to replace or to be used in combination with 2D-mammography in breast screening. The aim of this study was the comparison of the detection of microcalcification clusters by human observers in simulated breast images using 2D-mammography, narrow angle (15°/15 projections) and wide angle (50°/25 projections) DBT. The effects of the cluster height in the breast and the dose to the breast on calcification detection were also tested. Simulated images of 6 cm thick compressed breasts were produced with and without microcalcification clusters inserted, using a set of image modelling tools for 2D-mammography and DBT. Image processing and reconstruction were performed using commercial software. A series of 4-alternative forced choice (4AFC) experiments was conducted for signal detection with the microcalcification clusters as targets. Threshold detectable calcification diameter was found for each imaging modality with standard dose: 2D-mammography: 2D-mammography (165  ±  9 µm), narrow angle DBT (211  ±  11 µm) and wide angle DBT (257  ±  14 µm). Statistically significant differences were found when using different doses, but different geometries had a greater effect. No differences were found between the threshold detectable calcification diameters at different heights in the breast. Calcification clusters may have a lower detectability using DBT than 2D imaging.

Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deeplearning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019-April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines.

Large numbers of acoustic signals from single lipid-shelled Definity® microbubbles have been measured using a calibrated microacoustic system and a two population response observed. Theoretical results based on the Mooney-Rivlin strain softening shell model have been used to identify these populations as primary resonant and off-primary resonant scatter. An experimentally measured size distribution was used to provide the initial resting radius for the simulations, and the responses agree well with the experimental data. In this way, the primary resonant or off-primary resonant behavior of a microbubble can be studied, with potential benefits to both signal processing techniques and microbubble manufacture.

First trimester placental volume measured with 3D ultrasound has been shown to be correlated to adverse pregnancy outcomes This could potentially be used as a screening test to predict the "at risk" pregnancy. However, manual segmentation whilst accurate is very time consuming. Semi-automated methods provide close agreement to manual segmentation but remain significantly operator dependant. To generate a screening tool fully automated placental segmentation is required. In this paper a previously published deep convolutional neural network, Deep Medic, was trained using the output of the semi-automated Random Walker method as the ground truth. A set of 300 ultrasound volumes was used to train, validate and test the neural network. Dice similarity coefficients from the neural network had a median value of 0.73. This work shows the feasibility for applying convolutional neural networks to automating segmentation of 3D ultrasound placental volume.