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Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer.

Publication ,  Journal Article
Chawla, AS; Saunders, RS; Singh, S; Lo, JY; Samei, E
Published in: Acad Radiol
April 2009

RATIONALE AND OBJECTIVES: Correlation imaging (CI) is a form of multiprojection imaging in which multiple images of a patient are acquired from slightly different angles. Information from these images is combined to make the final diagnosis. A critical factor affecting the performance of CI is its data acquisition scheme, because nonoptimized acquisition may distort pathologic indicators. The authors describe a computer-aided detection (CADe) methodology to optimize the acquisition scheme of CI for superior diagnostic accuracy. MATERIALS AND METHODS: Images from 106 subjects were used. For each subject, 25 angular projections of a single breast were acquired. Projection images were supplemented with a simulated 3-mm three-dimensional lesion. Each projection was then processed using a traditional CADe algorithm at high sensitivity, followed by the reduction of false-positives by combining the geometric correlation information available from the multiple images. The performance of the CI system was determined in terms of free-response receiver-operating characteristic curves and the areas under receiver-operating characteristic curves. For optimization, the components of acquisition, such as the number of projections and their angular span, were systematically changed to investigate which of the many possible combinations maximized the obtainable CADe sensitivity and specificity. RESULTS: The performance of the CI system was improved by increasing the angular span. Increasing the number of angular projections beyond a certain number did not improve performance. Maximum performance was obtained between 7 and 10 projections spanning a maximum angular arc of 45 degrees . CONCLUSION: The findings suggest the existence of an optimum acquisition scheme for CI of the breast. CADe results confirmed earlier predictions on the basis of observer models. An optimized CI system may be an important diagnostic tool for improved breast cancer detection.

Duke Scholars

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

April 2009

Volume

16

Issue

4

Start / End Page

456 / 463

Location

United States

Related Subject Headings

  • Statistics as Topic
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Imaging, Three-Dimensional
  • Humans
 

Citation

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Chicago
ICMJE
MLA
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Chawla, A. S., Saunders, R. S., Singh, S., Lo, J. Y., & Samei, E. (2009). Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer. Acad Radiol, 16(4), 456–463. https://doi.org/10.1016/j.acra.2008.09.013
Chawla, Amarpreet S., Robert S. Saunders, Swatee Singh, Joseph Y. Lo, and Ehsan Samei. “Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer.Acad Radiol 16, no. 4 (April 2009): 456–63. https://doi.org/10.1016/j.acra.2008.09.013.
Chawla AS, Saunders RS, Singh S, Lo JY, Samei E. Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer. Acad Radiol. 2009 Apr;16(4):456–63.
Chawla, Amarpreet S., et al. “Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer.Acad Radiol, vol. 16, no. 4, Apr. 2009, pp. 456–63. Pubmed, doi:10.1016/j.acra.2008.09.013.
Chawla AS, Saunders RS, Singh S, Lo JY, Samei E. Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer. Acad Radiol. 2009 Apr;16(4):456–463.
Journal cover image

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

April 2009

Volume

16

Issue

4

Start / End Page

456 / 463

Location

United States

Related Subject Headings

  • Statistics as Topic
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • Pattern Recognition, Automated
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Imaging, Three-Dimensional
  • Humans