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.
Purpose To investigate the relationship between image quality measurements and the clinical performance of digital mammographic systems. Methods Mammograms containing subtle malignant non-calcification lesions and simulated malignant calcification clusters were adapted to appear as if acquired by four types of detector. Observers searched for suspicious lesions and gave these a malignancy score. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). Images of a CDMAM contrast-detail phantom were adapted to appear as if acquired using the same four detectors as the clinical images. The resultant threshold gold thicknesses were compared to the FoMs using a linear regression model and an F-test was used to find if the gradient of the relationship was significantly non-zero. Results The detectors with the best image quality measurement also had the highest FoM values. The gradient of the inverse relationship between FoMs and threshold gold thickness for the 0.25mm diameter disk was significantly different from zero for calcification clusters (p=0.027), but not for non-calcification lesions (p=0.11). Systems performing just above the minimum image quality level set in the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis resulted in reduced cancer detection rates compared to systems performing at the achievable level. Conclusions The clinical effectiveness of mammography for the task of detecting calcification clusters was found to be linked to image quality assessment using the CDMAM phantom. The European Guidelines should be reviewed as the current minimum image quality standards may be too low.
Objectives To compare the performance of different types of detectors in breast cancer detection. Methods A mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). The cancer detection fraction (CDF) was estimated for a representative image set from screening. Results No significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors’ FoMs were significantly lower than for DR detectors. The calcification cluster’s FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15% and 22% lower respectively than for DR detectors. Conclusion Cancer detection is affected by detector type and the use of CR in mammography should be reconsidered.
N umerous pathologic states are characterized by altered vascularity (number of blood vessels per unit tissue volume) or perfusion (expressed as volume blood flow [in milliliters per second] per unit tissue mass [in milliliters per kilogram per second]). No easily available clinical tool exists for quantification of tissue or organ blood flow and perfusion.Perfusion estimation with use of near-infrared spectroscopy, MRI, or CT has been attempted, but there are several challenges for clinical adoption. Near-infrared spectroscopy provides no visual display to indicate the anatomic site where tissue oxygenation is being measured. MRI allows imaging but is inherently expensive, temporally and spatially limited, and dependent on the type of sequence used (arterial spin labeling or blood oxygenation level-dependent). CT requires ionizing radiation and a tracer. US would therefore be an obvious candidate for noninvasive volumetric perfusion imaging that can be performed in most clinical settings. With a validated method, the ability to understand where organ perfusion has changed versus absolute measurements (against a known reference range), or as relative measurements (eg, in response to therapy or surgery, including transplantation), would be valuable for detecting perfusion changes and determining their severity and lead to earlier interventions. These would have obvious applications in emergency care, transplantation, and oncology, where estimation of perfusion would be a useful additional diagnostic tool.Power Doppler US has advantages for flow quantification because of its ability to depict low-velocity signals and multidirectional flow and its lack of aliasing (1). Sources of error include signal attenuation due to depth and patient habitus and tissue inhomogeneity, which reduces the possibility for comparison over time or between patients. To compensate, a technique called
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