Generating synthetic mammograms for stationary 3D mammography

Connor Puett; Christina Inscoe; Jianping Lu; Yueh Lee M.D.; Otto Zhou

doi:10.1117/12.2513017

1 March 2019 Generating synthetic mammograms for stationary 3D mammography

Connor Puett, Christina Inscoe, Jianping Lu, Yueh Lee M.D., Otto Zhou

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 1094807 (2019) https://doi.org/10.1117/12.2513017
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Purpose. Investigate synthetic mammography approaches for carbon nanotube (CNT)-enabled stationary digital breast tomosynthesis (sDBT). Methods. Projection images of breast-mimicking phantoms containing soft-tissue masses and microcalcification clusters collected by sDBT were used to develop weighted-intensity forward-projection algorithms that generated a synthetic mammogram from the reconstructed 3D image space. Reconstruction was accomplished by an adapted fan-volume modification of the simultaneous iterative reconstruction technique. Detectability indices were used to quantify mass and calcification visibility. The image processing chain was then applied to projection views collected by sDBT on women with “suspicious” breast lesions detected by standard screening 2D digital mammography. Results. Quantifying detectability allowed correlation between the visibility of clinically-important image features and the order of the polynomial weighting function used during forward projection. The range of weighted functions exists between the extremes of an average-intensity projection (zero-order) and maximum-intensity projection (infinite-order), with lower order weights emphasizing soft-tissue features and higher-order weights emphasizing calcifications. Application of these algorithms to patient images collected by sDBT, coupled with dense-artifact reduction and background equalization steps, produced synthetic mammograms on which additional post-processing approaches can be explored, with the actual mammogram providing a reference for comparison. Conclusions. An image-processing chain with adjustable weighting during forward projection, dense-artifact reduction, and background equalization can yield a range of sDBT-based synthetic mammograms that display clinically-important features differently, potentially improving the ability to appreciate the association of masses and calcifications.

Citation Download Citation

Connor Puett, Christina Inscoe, Jianping Lu, Yueh Lee M.D., and Otto Zhou "Generating synthetic mammograms for stationary 3D mammography", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094807 (1 March 2019); https://doi.org/10.1117/12.2513017

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