Prostate cancer is the most diagnosed form of cancer among American men and, in vast proportion, the standard of care treatment includes radical prostatectomy. Important risk factors associated with prostatectomies are the presence of post-surgery residual prostate tissue and positive cancer margins, potentially leading to recurrences. Prostate histopathology analysis following the procedure is used to determine follow-up treatment. However, only a limited fraction of the prostate margins can be sampled, which can lead to suboptimal evaluation and treatment. Here we present the development of a wide-field multimodal imaging system designed to quantify intrinsic tissue fluorescence and map scattering and absorption coefficients using spatial frequency domain imaging (SFDI). The system allows targeting of suspicious prostate regions to guide histopathology analysis, aiming to improve diagnostic accuracy and treatment planning. Tissue excitation for endogenous fluorescence is achieved with a 405 nm laser diode and, for SFDI, a digital light projector transmits structured white light used to reconstruct tissue optical properties (absorption, scattering) between 420 and 720 nm. A light transport model-based quantification algorithm then corrects the fluorescence spectra for tissue attenuation, lending a biomarker that correlates with local fluorophore concentrations. Spectral and spatial calibration of both modalities was done on optical phantoms and validation of the fluorescence quantification on biological tissue. Finally, imaging results are presented for 5 human prostates interrogated with the system, along with spatially-registered histopathology analyses. Future work involves massive data acquisition and development of artificial intelligence models for tissue classification (prostate, non-prostate; healthy, cancerous) and adaptation for intraoperative use.

Date Published: 4 March 2019
PDF
Proc. SPIE 10871, Multimodal Biomedical Imaging XIV, 108710B (4 March 2019); doi: 10.1117/12.2510067

Published in SPIE Proceedings Vol. 10871:
Multimodal Biomedical Imaging XIV
Fred S. Azar; Xavier Intes; Qianqian Fang, Editor(s)