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

Publication ,  Conference
Chawla, AS; Samei, E; Lo, JY; Mertelmeier, T
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
September 9, 2008

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.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

September 9, 2008

Volume

5116 LNCS

Start / End Page

635 / 642

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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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
Chawla, A. S., E. Samei, J. Y. Lo, and T. Mertelmeier. “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), 5116 LNCS:635–42, 2008. https://doi.org/10.1007/978-3-540-70538-3_88.
Chawla AS, Samei E, Lo JY, Mertelmeier T. 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). 2008. p. 635–42.
Chawla, A. S., et al. “Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 635–42. Scopus, doi:10.1007/978-3-540-70538-3_88.
Chawla AS, Samei E, Lo JY, Mertelmeier T. Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 635–642.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

September 9, 2008

Volume

5116 LNCS

Start / End Page

635 / 642

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences