Current surgical microscopes are limited in sensitivity for NIR fluorescence. Recent developments in tumor markers attached with NIR dyes require newer, more sensitive imaging systems with high resolution to guide surgical resection. We report on a small, single camera solution enabling advanced image processing opportunities previously unavailable for ultra-high sensitivity imaging of these agents. The system captures both visible reflectance and NIR fluorescence at 300 fps while displaying full HD resolution video at 60 fps. The camera head has been designed to easily mount onto the Zeiss Pentero microscope head for seamless integration into surgical procedures.
We have developed and tested a system for real-time intra-operative optical identification and classification of brain tissues using time-resolved fluorescence spectroscopy (TRFS). A supervised learning algorithm using linear discriminant analysis (LDA) employing selected intrinsic fluorescence decay temporal points in 6 spectral bands was employed to maximize statistical significance difference between training groups. The linear discriminant analysis on in vivo human tissues obtained by TRFS measurements (N = 35) were validated by histopathologic analysis and neuronavigation correlation to pre-operative MRI images. These results demonstrate that TRFS can differentiate between normal cortex, white matter and glioma.
The monochromatic single frame pixel count of a camera is limited by diffraction to the space-bandwidth product, roughly the aperture area divided by the square of the wavelength. We have recently shown that it is possible to approach this limit using multiscale lenses for cameras with space bandwidth product between 1 and 100 gigapixels. When color, polarization, coherence and time are included in the image data cube, camera information capacity may exceed 1 petapixel/second. This talk reviews progress in the construction of DARPA AWARE gigapixel cameras and describes compressive measurement strategies that may be used in combination with multiscale systems to push camera capacity to near physical limits.
Gigapixel-class cameras present new challenges in calibration, mechanical testing, and optical performance evaluation. The AWARE-2 gigapixel camera has nearly one-hundred micro-cameras covering a 120 degree wide by 40
degree tall field of view, with one pixel spanning an 8 arcsec field angle. Viewing the imagery requires stitching
the sub-images together by applying an accurate mapping of registration parameters over the entire field of view.
For this purpose, a testbed has been developed to automatically calibrate and test each micro-camera in the
array. Using translation stages, rotation stages, and a spatial light modulator for object space, this testbed can
project any test scene into a specified micro-camera, building up image quality metrics and a registration look-up
table over the entire array.
We describe the design and performance of a coded aperture spectral imager with a wide spectral range of 320 to 700 nm over 87 channels and 1988-by-1988 pixels of spatial resolution. A custom-designed relay lens was designed and built for the system, including a dispersive prism element in the collimated space of the relay lens. The optical design process, prescription, and performance are reported for the entire system, including calibration and alignment. Simulations of high-resolution spectral images are conducted to verify the reconstruction algorithm and relative resolution of the instrument compared to ground truth data. Measured data were taken with the instrument using both a random coded aperture and standard slit for spatial resolution comparisons. Finally, reconstructed spectral images from the instrument are presented of a sunlight-illuminated flower from 360 to 700 nm.
This work describes numerical methods for the joint reconstruction and segmentation of spectral images
taken by compressive sensing coded aperture snapshot spectral imagers (CASSI). In a snapshot, a CASSI
captures a two-dimensional (2D) array of measurements that is an encoded representation of both spectral
information and 2D spatial information of a scene, resulting in significant savings in acquisition time and data
storage. The double disperser coded aperture snapshot imager (DD-CASSI) is able to capture a hyperspectral
image from which a highly underdetermined inverse problem is solved for the original hyperspectral cube
with regularization terms such as total variation minimization. The reconstruction process decodes the
2D measurements to render a three-dimensional spatio-spectral estimate of the scene, and is therefore an
indispensable component of the spectral imager. In this study, we seek a particular form of the compressed
sensing solution that assumes spectrally homogeneous segments in the two spatial dimensions, and greatly
reduces the number of unknowns. The proposed method generalizes popular active contour segmentation
algorithms such as the Chan-Vese model and also enables one to jointly estimate both the segmentation
membership functions and the spectral signatures of each segment. The results are illustrated on a simulated
Hubble Space Satellite hyperspectral dataset, a real urban hyperspectral dataset, and a real DD-CASSI image
in microscopy.
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