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Assessment of low energies and slice depth in the quantification of breast tomosynthesis

Publication ,  Conference
Shafer, CM; Samei, E; Mertelmeier, T; Saunders, RS; Zerhouni, M; Lo, JY
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
September 9, 2008

This study attempts to assess the quantitative potential of breast tomosynthesis imaging. Tomosynthesis might be a feasible replacement for digital mammography, so it is worthwhile to consider whether it can be quantitative like computed tomography (CT), where the image pixel values are expressed in Hounsfield units. For this investigation, plastic tissue-equivalent breast phantoms with 10 lesions of varying density in the center along with a small density calibration phantom of 5 density-varying lesions were imaged under several different conditions. The measured voxel value for each lesion from a reconstructed slice was linearly rescaled based on the calibration phantom and then plotted against the known glandular fraction of each lesion. It was found that the two different energies and the three different lesion depths all produced linear voxel values versus glandularity relationships. Therefore, tomosynthesis has quantitative potential. However, in order to convert each 3D image's voxel values to values that can be interpreted as a certain glandular fraction, one must consider the x-ray tube energy, slice depth, and many other factors of the imaging system and the breast. © 2008 Springer-Verlag Berlin Heidelberg.

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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

530 / 536

Related Subject Headings

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

Citation

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Shafer, C. M., Samei, E., Mertelmeier, T., Saunders, R. S., Zerhouni, M., & Lo, J. Y. (2008). Assessment of low energies and slice depth in the quantification of breast tomosynthesis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5116 LNCS, pp. 530–536). https://doi.org/10.1007/978-3-540-70538-3_74
Shafer, C. M., E. Samei, T. Mertelmeier, R. S. Saunders, M. Zerhouni, and J. Y. Lo. “Assessment of low energies and slice depth in the quantification of breast tomosynthesis.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5116 LNCS:530–36, 2008. https://doi.org/10.1007/978-3-540-70538-3_74.
Shafer CM, Samei E, Mertelmeier T, Saunders RS, Zerhouni M, Lo JY. Assessment of low energies and slice depth in the quantification of breast tomosynthesis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 530–6.
Shafer, C. M., et al. “Assessment of low energies and slice depth in the quantification of breast tomosynthesis.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5116 LNCS, 2008, pp. 530–36. Scopus, doi:10.1007/978-3-540-70538-3_74.
Shafer CM, Samei E, Mertelmeier T, Saunders RS, Zerhouni M, Lo JY. Assessment of low energies and slice depth in the quantification of breast tomosynthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 530–536.

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

530 / 536

Related Subject Headings

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