Hyperspectral (HS) imaging systems have become important tools in an array of fields due, in part, to the superior molecular recognition capabilities provided by high-resolution spectral information. Provided the user has a library of spectral fingerprints representing the individual molecular contents, one may decompose each HS pixel into a sum of its constituent species using a linear least-squares fitting routine with a non-negativity constraint, (i.e., spectral unmixing). This method, while robust, presents a significant computational bottleneck that precludes real-time HS image analysis. In this work, we use GPUs to accelerate the fast non-negative least squares (FNNLS) algorithm and present unmixing analysis results using images acquired from 4 commercial HS imaging systems. In all cases, we demonstrate video-rate speeds (> 15 fps) using one and two NVIDIA GTX 1080Ti GPUs, representing an average data throughput of 2.5 GB/s and 5.0 GB/s, respectively. This implementation enables online HS feature recognition and is easily integrated into computer-based and mobile platforms with current NVIDIA GPU technology. The method is also applied to a hyperspectral fluorescence imaging system to show online 5-color optical biopsy (5 protein biomarkers) in a mouse model of ovarian cancer to monitor responses to PDT.
|