Design of a 3D Mammography System in the Age of Personalized Medicine

Abstract

Digital breast tomosynthesis (DBT) or 3D mammography has been shown to offer higher sensitivity and specificity for breast cancer detection relative to 2D digital mammography. However, studies have demonstrated that the benefits of DBT are limited to non-calcification findings. We have developed new tomosynthesis system designs to enhance the visibility of calcifications based on the principle of super-resolution (SR). We published a manuscript describing a Fourier-based image-quality metric for high-frequency star-pattern phantoms for investigating SR [IEEE TMI 2021 40(3):1055-64]. Using this phantom, we showed that there are anisotropies in SR in current clinical systems. Additionally, we investigated how the magnification call-back exam can be optimized for 3D imaging with SR, and analyzed how the anisotropies in SR vary with the use of magnification. More specifically, a system design was analyzed with a secondary component of scanning motion in the posteroanterior (PA) direction; this motion eliminates the anisotropies in SR. We also explored how the use of PA source motion improves the accuracy of the separation between fibroglandular (dense) and adipose tissue, as well as the segmentation of the 3D breast outline, in voxel breast phantoms. This new design offers additional advantages in terms of Fourier-space sampling and signal-to-noise ratio (SNR).

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2021
Accession Number
AD1145474

Entities

People

  • Raymond J Acciavatti

Organizations

  • University of Pennsylvania

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Breast Cancer
  • Computational Science
  • Computer Science
  • Detectors
  • Diagnostic Imaging
  • Electronic Mail
  • Health Services
  • Image Processing
  • Imaging Techniques
  • Information Science
  • Medical Personnel
  • Network Science
  • Personalized Medicine
  • Radiography
  • Supervised Machine Learning
  • Tomography

Fields of Study

  • Medicine
  • Physics

Readers

  • Image Processing and Computer Vision.
  • Medical Imaging.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Space