Assessment of the Severity of Breast Artery Calcification on a Mammogram: Intraoperator and Interoperator Reproducibility

Purpose. To study approaches to the quantitative assessment of breast arterial calcification (BAC) – a new surrogate marker of high cardiovascular risk in women, to determine the most detailed way to quantify BAC and to assess the reproducibility of these parameters.Material and methods. Mammograms of 1,078 women were analyzed for the presence of BAC. The intraoperator reproducibility of the assessment of the severity of BAC using a 12-point scale (Margolies L et al., 2016) was studied by analyzing 20 mammograms by the same operator twice with an interval of at least 2 weeks. Inter-operator reproducibility was studied by analyzing 99 mammograms by two independent operators.Results. When assessing the intraoperative reproducibility of the total score for each mammary gland, the exact coincidence of the results was noted in 70% (95% confidence interval [CI] 53.5-83.4), in cases of difference of no more than 1 point – in 27.5% (95%CI 14.6-43.9), only in 1 case the difference in assessments was 2 points. No systematic error was found between the two measurements (p=1.0), the correlation coefficient was rs=0.973. The assessment of inter-operator reproducibility showed that the exact coincidence of indicators was present in 48.5% (95%CI 41.3-55.7), in 91.4% (95% CI 86.6-94.9) cases, the total score for each the mammary gland differed by no more than 1 point. There was no systematic error between the measurements of the two experts (p=0.438), the correlation coefficient was rs=0.942.Conclusion. A good intraoperator and interoperator reproducibility of indicators of the severity of BAC on a 12-point scale has been shown, which makes it possible to recommend it for use in science and practice

Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering

Perivascular spaces (PVSs) in brain have a close relationship with typical neurological diseases. The quantitative studies of PVSs are meaningful but usually difficult, due to their thin and weak signals and also background noise in the 7 T brain magnetic resonance images (MRI). To clearly distinguish the PVSs in the 7 T MRI, we propose a novel PVS enhancement method based on the Haar transform of non-local cubes. Specifically, we extract a certain number of cubes from a small neighbor to form a cube group, and then perform Haar transform on each cube group. The Haar transform coefficients are processed using a nonlinear function to amplify the weak signals relevant to the PVSs and to suppress the noise. The enhanced image is reconstructed using the inverse Haar transform of the processed coefficients. Finally, we perform a block-matching 4D filtering on the enhanced image to further remove any remaining noise, and thus obtain an enhanced and denoised 7 T MRI for PVS segmentation. We apply two existing methods to complete PVS segmentation, i.e., (1) vesselness-thresholding and (2) random forest classification. The experimental results show that the PVS segmentation performances can be significantly improved by using the enhanced and denoised 7 T MRI