Abstract
Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.
Highlights
An encouraging progress has been demonstrated by Kut et al to differentiate infiltrated human brain cancer from non-cancer brain tissues by using a real-time, attenuation mapping method[21]
Since both exponential fitting method (EF) and log-and-fitting method (LF) methods require an accurate detection of the tissue surface from the OCT signals, we found the FD method is very robust in characterizing OCT signals even with incorrect surface detection, while with EF and LF methods this will produce inaccurate attenuation coefficients
The FD method is significantly robust against the effects of incorrect tissue surface detection and produced attenuation coefficients which are closer to its true value at ∼3 mm−1. Such robustness from the FD method is consistent with our theoretical predictions as detailed in Supplementary Information, where we show that an incorrect detection of the sample surface, i.e., z0, has no effect on our attenuation computations with the FD method
Summary
An encouraging progress has been demonstrated by Kut et al to differentiate infiltrated human brain cancer from non-cancer brain tissues by using a real-time, attenuation mapping method[21]. For intraoperative detection of brain cancer, a robust, accurate, and high-speed OCT attenuation mapping is needed to improve surgical results. Since both EF and LF methods require an accurate detection of the tissue surface from the OCT signals, we found the FD method is very robust in characterizing OCT signals even with incorrect surface detection, while with EF and LF methods this will produce inaccurate attenuation coefficients This robustness is especially important in in vivo surgical applications. With GPU-based CUDA C/C++implementation, our algorithm can be applied to high-speed and accurate optical attenuation mapping in real time with superb robustness, which is imperative for visualizing brain cancer infiltration during surgery
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