Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection

2Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Multi-projection imaging technique offers an advantage over single projection imaging techniques in rendering pathology that may be surrounded by a complex cloud of anatomical structures. The process of harnessing the geometrical and statistical dependences between the multiple images available in a multi-projection system to determine the final diagnosis is termed Correlation Imaging (CI). In this study, we are investigating the potential improvement in breast cancer detection via CI. As a key step towards that, the acquisition scheme of CI was first optimized to maximize its diagnostic performance. Toward that end, first a clinically-realistic task was designed and each component of acquisition, namely, the acquisition dose level, the number of projections, and their angular span was systematically changed to determine a specific combination that yielded maximum performance in that task. Finally, the performance of the optimized system was compared with that of standard planar mammography. The results indicated that the performance of CI may potentially be optimized between 15-17 projections spanning an angular arc of 45 o . This optimum performance further improved with increasing dose levels; however, at dose level comparable to mammography, CI provided a factor of 1.1 improvement over mammography. The framework developed in this study to evaluate multi-projections system may be applied to any other multi-projection imaging modality, and by including reconstruction, may be extended to digital breast tomosynthesis and breast computed tomography. © 2008 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Chawla, A. S., Samei, E., Lo, J. Y., & Mertelmeier, T. (2008). Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 635–642). https://doi.org/10.1007/978-3-540-70538-3_88

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free