Hybrid Modality Fusion of Planar Scintigrams and CT Topograms to Localize Sentinel Lymph Nodes in Breast Lymphoscintigraphy: Technical Description and Phantom Studies

Dickinson, Renée L. and Erwin, William D. and Stevens, Donna M. and Bidaut, Luc and Mar, Martha V. and Macapinlac, Homer A. and Wendt, Richard E. (2010) Hybrid Modality Fusion of Planar Scintigrams and CT Topograms to Localize Sentinel Lymph Nodes in Breast Lymphoscintigraphy: Technical Description and Phantom Studies. International Journal of Molecular Imaging, 2011 . pp. 1-10. ISSN 2090-1712

Full content URL: http://doi.org/10.1155/2011/298102

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Abstract

Lymphoscintigraphy is a nuclear medicine procedure that is used to detect sentinel lymph nodes (SLNs). This project sought to investigate fusion of planar scintigrams with CT topograms as a means of improving the anatomic reference for the SLN localization. Heretofore, the most common lymphoscintigraphy localization method has been backlighting with a 57Co sheet source. Currently, the most precise method of localization through hybrid SPECT/CT increases the patient absorbed dose by a factor of 34 to 585 (depending on the specific CT technique factors) over the conventional 57Co backlighting. The new approach described herein also uses a SPECT/CT scanner, which provides mechanically aligned planar scintigram and CT topogram data sets, but only increases the dose by a factor of two over that from 57Co backlighting. Planar nuclear medicine image fusion with CT topograms has been proven feasible and offers a clinically suitable compromise between improved anatomic details and minimally increased radiation dose.

Additional Information:© 2011 Renée L. Dickinson et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords:sentinel lymph nodes, SLN
Subjects:F Physical Sciences > F350 Medical Physics
Divisions:College of Science > School of Computer Science
ID Code:29485
Deposited On:13 Aug 2018 13:43

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