Pancreatic cancer is the fourth leading cause of cancer death in the US. Currently, surgery is the only treatment that offers a chance of cure, however, accurately identifying tumor margins in real-time is difficult. Research has demonstrated that optical spectroscopy can be used to distinguish between healthy and diseased tissue. The design of a single-pixel imaging system for cancer detection is discussed. The system differentiates between healthy and diseased tissue based on differences in the optical reflectance spectra of these regions. In this study, pancreatic tissue samples from 6 patients undergoing Whipple procedures are imaged with the system (total number of tissue sample imaged was N=11). Regions of healthy and unhealthy tissue are determined based on SAM analysis of these spectral images. Hyperspectral imaging results are then compared to white light imaging and histological analysis. Cancerous regions were clearly visible in the hyperspectral images. Margins determined via spectral imaging were in good agreement with margins identified by histology, indicating that hyperspectral imaging system can differentiate between healthy and diseased tissue. After imaging the system was able to detect cancerous regions with a sensitivity of 74.50±5.89% and a specificity of 75.53±10.81%. Possible applications of this imaging system include determination of tumor margins during surgery/biopsy and assistance with cancer diagnosis and staging.
Limitations of current ureteroscope illumination configurations include presence of shadows and hot spots in
images, further degraded by stone debris during laser lithotripsy, which may result in a decrease in stone ablation
efficiency, increase in surgical operation time, and potential collateral tissue trauma. Previous studies have reported
accidental ureteral tissue perforation from Nitinol stone basket wires during Holmium laser lithotripsy, due in part to
poor visibility. Although saline irrigation is routinely used during ureteroscopy to flush stone debris and improve
visibility, sub-optimal illumination may still compound these problems. Current illumination geometries and
sources are inadequate to produce necessary uniform illumination for optimal visibility and safety during
ureteroscopy. By moving away from single and double point source geometry and towards a ring configuration,
illumination becomes more uniform in both axes, reducing shadows and increasing depth discernibility. Uric acid
and calcium oxalate based stones were chosen for illumination and reflection spectroscopy. Porcine ureters were
used as soft tissue samples for comparison. The percent difference in reflection between ureter and stones was
greater than 40% for the wavelength ranges of 470-540 nm, and 600-700 nm, making these spectral regions most
suitable for high contrast illumination, possibly through narrow band imaging techniques via multiple laser sources
and/or optical filters. These improved ureteroscope illumination designs and approaches may potentially reduce
complications due to limited visibility during laser lithotripsy and hence increase patient safety.
We are developing a single-pixel hyperspectral imaging system based on compressive sensing that acquires spatial and spectral information simultaneously. Our spectral imaging system uses autofluorescencent emission from collagen (400 nm) and NAD(P)H (475 nm), as well as, differences in the optical reflectance spectra as diagnostics for differentiating between healthy and diseased tissue. In this study, we demonstrate the ability of our imaging system to discriminate between healthy and damaged porcine epidermal tissue. Healthy porcine epidermal tissue samples (n=11) were imaged ex vivo using our hyperspectral system. The amount of NAD(P)H emission and the reflectance properties were approximately constant across the surface of healthy tissue samples. The tissue samples were then thermally damaged using an 1850 nm thulium fiber laser and re-imaged after laser irradiation. The damaged regions were clearly visible in the hyperspectral images as the thermal damage altered the fluorescent emission of NAD(P)H and changed the scattering properties of the tissue. The extent of the damaged regions was determined based on the hyperspectral images and these estimates were compared to damage extents measured in white light images acquired with a traditional camera. The extent of damage determined via hyperspectral imaging was in good agreement with estimates based on white light imaging indicating that our system is capable of differentiating between healthy and damaged tissue. Possible applications of our single pixel hyperspectral imaging system range from real-time determination of tumor margins during surgery to the use of this technique in the pathology lab to aid with cancer diagnosis and staging.
Pancreatic cancer is the fourth leading cause of cancer death in the United States. Most pancreatic cancer patients will die within the first year of diagnosis, and just 6% will survive five years. Currently, surgery is the only treatment that offers a chance of cure for pancreatic cancer patients. Accurately identifying the tumors margins in real time is a significant difficulty during pancreatic cancer surgery and contributes to the low 5-year survival rate. We are developing a hyperspectral imaging system based on compressive sampling for real-time tumor margin detection to facilitate more effective removal of diseased tissue and result in better patient outcomes. Recent research has shown that optical spectroscopy can be used to distinguish between healthy and diseased tissue and will likely become an important minimally invasive diagnostic tool for a range of diseases. Reflectance spectroscopy provides information about tissue morphology, while laser-induced autofluorescence spectra give accurate information about the content and molecular structure of the emitting tissue. We are developing a spectral imaging system that targets emission from collagen and NAD(P)H as diagnostics for differentiating healthy and diseased pancreatic tissue. In this study, we demonstrate the ability of our camera system to acquire hyperspectral images and its potential application for imaging autofluorescent emission from pancreatic tissue.