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Proceedings Paper

Library-based scatter correction for dedicated cone beam breast CT: a feasibility study
Author(s): Linxi Shi; Srinivasan Vedantham; Andrew Karellas; Lei Zhu
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Paper Abstract

Purpose: Scatter errors are detrimental to cone-beam breast CT (CBBCT) accuracy and obscure the visibility of calcifications and soft-tissue lesions. In this work, we propose practical yet effective scatter correction for CBBCT using a library-based method and investigate its feasibility via small-group patient studies. Method: Based on a simplified breast model with varying breast sizes, we generate a scatter library using Monte-Carlo (MC) simulation. Breasts are approximated as semi-ellipsoids with homogeneous glandular/adipose tissue mixture. On each patient CBBCT projection dataset, an initial estimate of scatter distribution is selected from the pre-computed scatter library by measuring the corresponding breast size on raw projections and the glandular fraction on a first-pass CBBCT reconstruction. Then the selected scatter distribution is modified by estimating the spatial translation of the breast between MC simulation and the clinical scan. Scatter correction is finally performed by subtracting the estimated scatter from raw projections. Results: On two sets of clinical patient CBBCT data with different breast sizes, the proposed method effectively reduces cupping artifact and improves the image contrast by an average factor of 2, with an efficient processing time of 200ms per conebeam projection. Conclusion: Compared with existing scatter correction approaches on CBBCT, the proposed library-based method is clinically advantageous in that it requires no additional scans or hardware modifications. As the MC simulations are pre-computed, our method achieves a high computational efficiency on each patient dataset. The library-based method has shown great promise as a practical tool for effective scatter correction on clinical CBBCT.

Paper Details

Date Published: 5 April 2016
PDF: 6 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978330 (5 April 2016); doi: 10.1117/12.2217327
Show Author Affiliations
Linxi Shi, Georgia Institute of Technology (United States)
Srinivasan Vedantham, Univ. of Massachusetts Medical School (United States)
Andrew Karellas, Univ. of Massachusetts Medical School (United States)
Lei Zhu, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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