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Proceedings Paper

Optimized acquisition scheme for multi-projection correlation imaging of breast cancer
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Paper Abstract

We are reporting the optimized acquisition scheme of multi-projection breast Correlation Imaging (CI) technique, which was pioneered in our lab at Duke University. CI is similar to tomosynthesis in its image acquisition scheme. However, instead of analyzing the reconstructed images, the projection images are directly analyzed for pathology. Earlier, we presented an optimized data acquisition scheme for CI using mathematical observer model. In this article, we are presenting a Computer Aided Detection (CADe)-based optimization methodology. Towards that end, images from 106 subjects recruited for an ongoing clinical trial for tomosynthesis were employed. For each patient, 25 angular projections of each breast were acquired. Projection images were supplemented with a simulated 3 mm 3D lesion. Each projection was first processed by a traditional CADe algorithm at high sensitivity, followed by a reduction of false positives by combining geometrical correlation information available from the multiple images. Performance of the CI system was determined in terms of free-response receiver operating characteristics (FROC) curves and the area under ROC curves. For optimization, the components of acquisition such as the number of projections, and their angular span were systematically changed to investigate which one of the many possible combinations maximized the sensitivity and specificity. Results indicated that the performance of the CI system may be maximized with 7-11 projections spanning an angular arc of 44.8°, confirming our earlier findings using observer models. These results indicate that an optimized CI system may potentially be an important diagnostic tool for improved breast cancer detection.

Paper Details

Date Published: 27 March 2008
PDF: 8 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691528 (27 March 2008); doi: 10.1117/12.773174
Show Author Affiliations
Amarpreet S. Chawla, Duke Univ. (United States)
Ehsan Samei, Duke Univ. (United States)
Robert S. Saunders, Duke Univ. (United States)
Joseph Y. Lo, Duke Univ. (United States)
Swatee Singh, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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