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Dual-energy contrast-enhanced breast tomosynthesis: optimization of beam quality for dose and image quality.

Publication ,  Journal Article
Samei, E; Saunders, RS
Published in: Phys Med Biol
October 7, 2011

Dual-energy contrast-enhanced breast tomosynthesis is a promising technique to obtain three-dimensional functional information from the breast with high resolution and speed. To optimize this new method, this study searched for the beam quality that maximized image quality in terms of mass detection performance. A digital tomosynthesis system was modeled using a fast ray-tracing algorithm, which created simulated projection images by tracking photons through a voxelized anatomical breast phantom containing iodinated lesions. The single-energy images were combined into dual-energy images through a weighted log subtraction process. The weighting factor was optimized to minimize anatomical noise, while the dose distribution was chosen to minimize quantum noise. The dual-energy images were analyzed for the signal difference to noise ratio (SdNR) of iodinated masses. The fast ray-tracing explored 523 776 dual-energy combinations to identify which yields optimum mass SdNR. The ray-tracing results were verified using a Monte Carlo model for a breast tomosynthesis system with a selenium-based flat-panel detector. The projection images from our voxelized breast phantom were obtained at a constant total glandular dose. The projections were combined using weighted log subtraction and reconstructed using commercial reconstruction software. The lesion SdNR was measured in the central reconstructed slice. The SdNR performance varied markedly across the kVp and filtration space. Ray-tracing results indicated that the mass SdNR was maximized with a high-energy tungsten beam at 49 kVp with 92.5 µm of copper filtration and a low-energy tungsten beam at 49 kVp with 95 µm of tin filtration. This result was consistent with Monte Carlo findings. This mammographic technique led to a mass SdNR of 0.92 ± 0.03 in the projections and 3.68 ± 0.19 in the reconstructed slices. These values were markedly higher than those for non-optimized techniques. Our findings indicate that dual-energy breast tomosynthesis can be performed optimally at 49 kVp with alternative copper and tin filters, with reconstruction following weighted subtraction. The optimum technique provides best visibility of iodine against structured breast background in dual-energy contrast-enhanced breast tomosynthesis.

Duke Scholars

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

October 7, 2011

Volume

56

Issue

19

Start / End Page

6359 / 6378

Location

England

Related Subject Headings

  • Radiography, Dual-Energy Scanned Projection
  • Quality Control
  • Photons
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Monte Carlo Method
  • Models, Biological
  • Image Processing, Computer-Assisted
  • Humans
  • Female
 

Citation

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Samei, E., & Saunders, R. S. (2011). Dual-energy contrast-enhanced breast tomosynthesis: optimization of beam quality for dose and image quality. Phys Med Biol, 56(19), 6359–6378. https://doi.org/10.1088/0031-9155/56/19/013
Samei, Ehsan, and Robert S. Saunders. “Dual-energy contrast-enhanced breast tomosynthesis: optimization of beam quality for dose and image quality.Phys Med Biol 56, no. 19 (October 7, 2011): 6359–78. https://doi.org/10.1088/0031-9155/56/19/013.
Samei, Ehsan, and Robert S. Saunders. “Dual-energy contrast-enhanced breast tomosynthesis: optimization of beam quality for dose and image quality.Phys Med Biol, vol. 56, no. 19, Oct. 2011, pp. 6359–78. Pubmed, doi:10.1088/0031-9155/56/19/013.
Journal cover image

Published In

Phys Med Biol

DOI

EISSN

1361-6560

Publication Date

October 7, 2011

Volume

56

Issue

19

Start / End Page

6359 / 6378

Location

England

Related Subject Headings

  • Radiography, Dual-Energy Scanned Projection
  • Quality Control
  • Photons
  • Phantoms, Imaging
  • Nuclear Medicine & Medical Imaging
  • Monte Carlo Method
  • Models, Biological
  • Image Processing, Computer-Assisted
  • Humans
  • Female