Differentiation between nerve and adipose tissue using wide-band (350-1,830 nm) in vivo diffuse reflectance spectroscopy

• Published in: Lasers in surgery and medicine 2014-09, Vol.46 (7), p.538-545
• Main Authors: Schols, Rutger M., ter Laan, Mark, Stassen, Laurents P.S., Bouvy, Nicole D., Amelink, Arjen, Wieringa, Fokko P., Alic, Lejla
• Format: Article
• Language: English
• Subjects: adipose tissue; Adipose Tissue - anatomy & histology; Adolescent; Aged; Aged, 80 and over; automated nerve detection; diffuse reflectance spectroscopy; Female; Humans; Male; median nerve; Middle Aged; Nervous System - anatomy & histology; recurrent laryngeal nerve; Spectrum Analysis - methods; tissue spectral analysis; Young Adult
• ISSN: 0196-8092; 1096-9101
• DOI: 10.1002/lsm.22264

Background Intraoperative nerve localization is of great importance in surgery. In certain procedures, where nerves show visual resemblance to surrounding adipose tissue, this can be particularly challenging for the human eye. An example of such a delicate procedure is thyroid and parathyroid surgery, where iatrogenic injury of the recurrent laryngeal nerve can result in transient or permanent vocal problems (0.5–2.0% reported incidence). A camera system, enabling nerve‐specific image enhancement, would be useful in preventing such complications. This might be realized with hyperspectral camera technology using silicon (Si) or indium gallium arsenide (InGaAs) sensor chips. Methods As a first step towards such a camera, we evaluated the performance of diffuse reflectance spectroscopy by analysing spectra collected during 18 thyroid and parathyroid resections. We assessed the contrast information present in two different spectral ranges, for respectively Si and InGaAs sensors. Two hundred fifty three in vivo, wide‐band diffuse reflectance spectra (350–1,830 nm range, 1 nm resolution) were acquired on 52 tissue spots, including nerve (n = 22), muscle (n = 12), and adipose tissue (n = 18). We extracted 36 features from these spectroscopic data: 18 gradients and 18 amplitude differences at predefined points in the tissue spectra. Best distinctive feature combinations were established using binary logistic regression. Classification performance was evaluated in a cross‐validation (CV) approach by leave‐one‐out (LOO). To generalize nerve recognition applicability, we performed a train–test (TT) validation using the thyroid and parathyroid surgery data for training purposes and carpal tunnel release surgery data (10 nerve spots and 5 adipose spots) for classification purposes. Results For combinations of two distinctive spectral features, LOO revealed an accuracy of respectively 78% for Si‐sensors and 95% for InGaAs‐sensors. TT revealed accuracies of respectively 67% and 100%. Conclusions Using diffuse reflectance spectroscopy we have identified that InGaAs sensors are better suited for automated discrimination between nerves and surrounding adipose tissue than Si sensors. Lasers Surg. Med. 46:538–545, 2014. © 2014 Wiley Periodicals, Inc.