Cone beam breast CT with a flat panel detector- Simulation, implementation and demonstration

Chris C. Shaw, Lingyun Chen, Mastafa C. Altunbas, Shuju Tu, Xinming Liu, Tian Peng Wang, Chao Jen Lai, S. Cheenu Kappadath, Yang Meng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

This paper describes our experiences in the simulation, implementation and application of a flat panel detector based cone beam computed tomography (CT) imaging system for dedicated 3-D breast imaging. In our simulation study, the breast was analytically modeled as a cylinder of breast tissue loosely molded into cylindrical shape with embedded soft tissue masses and calcifications. Attenuation coefficients for various types of breast tissue, soft tissue masses and calcifications were estimated for various kVp's to generate simulated image signals. Projection images were computed to incorporate x-ray attenuation, geometric magnification, x-ray detection, detector blurring, image pixelization and digitization. Based on the x-ray kVp/filtration used, transmittance through the phantom, detective quantum efficiency (DQE), exposure level, and imaging geometry, the photon fluence was estimated and used to compute the quantum noise level on a pixel-by-pixel basis for various dose levels at the isocenter. This estimated noise level was then used with a random number generator to generate and add a fluctuation component to the noiseless transmitted image signal. The noise carrying projection images were then convolved with a Gaussian-like kernel, computed from measured 1-D line spread function (LSF) to simulate detector blurring. Additional 2-D Gaussian filtering was applied to the projection images and tested for improving the detection of soft tissue masses and calcifications in the reconstructed images. Reconstruction was performed using the Feldkamp filtered backprojection algorithm. All simulations were performed on a 24 PC (2.4 GHz Dual-Xeon CPU) cluster with MPI parallel programming. With 600 mrads mean glandular dose (MGD) at the phantom center, soft tissue masses as small as 1 mm in diameter could be visualized in a 10 cm diameter 50% glandular 50% adipose or fatter breast tissue, and 2 mm or larger masses were visible in a 100% glandular 0% adipose breast tissue. We have also demonstrated that 0.15 mm or larger calcification could be detected with a 100 μm detector pixel size while 0.2 mm or larger calcifications were visible with a pixel size of 200 μm. Our simulation study has shown that the cone-beam CT breast imaging can provide reasonable good quality and detectability at a dose level similar to that of two views mammography. For imaging experiments, a stationary x-ray source and detector, a stationary gantry, rotating phantom system was constructed to demonstrate cone beambreast CT imaging. Breast specimens from mastectomy were imaged to demonstrate the superior tissue contrast that can be achieved with the cone beam CT technique. Various phantoms were imaged to demonstrate that calcifications as small as 280 μm could be imaged at 80 kVp with a voxel size of 140 μm with an estimated isocenter dose of 1.8 rad.

Original languageEnglish (US)
Title of host publicationProceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4461-4464
Number of pages4
ISBN (Print)0780387406, 9780780387409
DOIs
StatePublished - 2005
Event2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 - Shanghai, China
Duration: Sep 1 2005Sep 4 2005

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume7 VOLS
ISSN (Print)0589-1019

Other

Other2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005
Country/TerritoryChina
CityShanghai
Period9/1/059/4/05

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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