This article is part of the supplement: 1st Scientific Meeting of the Head and Neck Optical Diagnostics Society .

Oral presentation

Spectral scatter scanning system for surgical margin detection

Brian W Pogue¹, Venkat Krishnaswamy¹, Ashley Laughney¹, Keith D Paulsen¹, P Jack Hoopes² and Pilar Beatriz García Allende³

¹  Dartmouth College, Hanover NH 03755, USA

²  Dartmouth Medical School, Hanover NH 03755, USA

³  Universidad de Cantabria, Santander, Spain

corresponding author email

from 1^st Scientific Meeting of the Head and Neck Optical Diagnostics Society
London, UK. 14 March 2009

Head & Neck Oncology 2009, 1(Suppl 1):O12doi:10.1186/1758-3284-1-S1-O12

The electronic version of this abstract is the complete one and can be found online at: http://www.headandneckoncology.org/content/1/S1/O12

Published:

28 July 2009

© 2009 Pogue et al; licensee BioMed Central Ltd.

Oral presentation

Tumour margin detection in real time without the use of additional molecular stain would be desirable. Surgical use of a microscopy tool would be ideal, but most systems focus on microscopic disease evaluation, whereas tools for macroscopic scanning of tissue such as enhanced endoscopy imaging are less well developed. A raster scanning scatter spectroscopy system has been evaluated for imaging the spectral signature remitted from tissue, with an online classification algorithm, which maximizes the ability to identify regions of tumour from regions of normal tissue. The system uses a wide band of wavelengths from 400 nm up to 700 nm, and recovers the scatter power, scatter amplitude, and absorption species, from the reflectance from a 100 micron spot, allowing imaging of tissue a high frame rate. The system uses dark field illumination and spectrometer detection in the emission channel together with a scanning mirror. The early prototypes of the system were tested on pancreas tumours and prostate tumour margin detection, and current work is ongoing in breast cancer margin delineation. The automated tissue classification from the data uses a k-nearest neighbours classification, to provide tissue delineation.

Have something to say? Post a comment on this article!