To assess vascular responses of the human hand to inspiratory gasps and hand cooling, two imaging "remote
sensing" instruments were utilized: 1) a high-resolution infrared (IR) imaging camera and 2) a full-field laser
perfusion imager (FLPI). Data analysis was performed on the data sets collected simultaneously from both
A non-localized drop of both FLPI and IR signals was observed at ~0.5-2.0 min after gasp onset. Spontaneous
oscillations, much below the human cardiac and respiratory frequencies, were observed with both imagers. The
dominant oscillations for both imaging modalities centered around 0.01Hz. Spectral frequencies, their power, and
the duration of temperature oscillations (bursts) for different hand areas changed in time and were spatially
heterogeneous. The highest spatial correlation between the two data sets was found between the mean IR
derivative image and the mean original FLPI image for the baseline conditions. Heterogeneous images of the
human hand were consistently detected non-invasively by both instruments. After cooling, a temperature
elevation of ~0.5ºC was seen as a spotted pattern mainly in the thenar and hypothenar areas. A generalized
increase in perfusion over the same areas was observed in FLPI images.
Both IR and FLPI imagers sensitively identify vasoconstrictor responses induced by inspiratory gasp and hand
cooling maneuvers. The specificity to physiological changes and high imaging rate for both instruments, coupled
with the current ease of use of optical cameras in clinical settings, make the described combination of two
instruments an ideal imaging approach to studying the dynamics of thermal and perfusion heterogeneity in human
The National Institute of Standards and Technology and the National Institutes of Health have started a collaborative study on the development of lighting that will provide enhanced, tissue-specific contrast with respect to its surroundings. In this paper we describe existing NIST technologies utilized for this project such as a computational model for color rendering and a new spectrally tunable lighting technology. We will also describe the calibration and validation procedure of a hyperspectral camera system. Finally, we show examples of imaged tissues under various lighting conditions.
Complex challenges of optical imaging in diagnostics and surgical treatment require accurate image
registration/stabilization methods that remove only unwanted motions. An SIAROI algorithm is proposed for real-time
subpixel registration sequences of intraoperatively acquired infrared (thermal) brain images. SIAROI algorithm is based
upon automatic, localized Subpixel Image Autocorrelation and a user-selected Region of Interest (ROI). Human
expertise about unwanted motions is added through a user-outlined ROI, using a low-accuracy free-hand paintbrush.
SIAROI includes: (a) propagating the user-outlined ROI by selecting pixels in the second image of the sequence, using
the same ROI; (b) producing SROI (sub-pixel ROI) by converting each pixel to k=NxN subpixels; (c) producing new
SROI in the second image by shifting SROI within plus or minus 6k subpixels; (d) finding an optimal autocorrelation
shift (x,y) within 12N that minimizes the Standard Deviation of Differences of Pixel Intensities (SDDPI) between
corresponding ROI pixels in both images, (e) shifting the second image by (x,y), repeating (a)-(e) for successive images
(t,t1). In experiments, a user quickly outlined non-deformable ROI (such as bone) in the first image of a sequence.
Alignment of 100 brain images (25600x25600 pixel search, after every pixel was converted to 100 sub-pixels), took ~3
minutes, which is 200 times faster (with a 0.1=ROI/image ratio) than global auto-correlation. SIAROI improved frame
alignment by a factor of two, relative to a Global Auto-correlation and Tie-points-based registration methods, as
measured by reductions in the SDDPI.