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Real-time GPU accelerated hyperspectral unmixing (Conference Presentation)
Author(s): Eric M. Kercher; Julia Tatz; Qianqian Fang; Bryan Q. Spring

Paper Abstract

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.

Paper Details

Date Published: 14 August 2019
PDF
Proc. SPIE 11070, 17th International Photodynamic Association World Congress, 11070B9 (14 August 2019); doi: 10.1117/12.2527922
Show Author Affiliations
Eric M. Kercher, Northeastern Univ. (United States)
Julia Tatz, Northeastern Univ. (United States)
Qianqian Fang, Northeastern Univ. (United States)
Bryan Q. Spring, Northeastern Univ. (United States)


Published in SPIE Proceedings Vol. 11070:
17th International Photodynamic Association World Congress
Tayyaba Hasan, Editor(s)

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