Optical Signal and Image Processing

Signal Processing

We have developed a novel and fast method for estimating fluorescence impulse response function (fIRF) from noise-corrupted time-domain fluorescence measurements of biological tissue. This method is based on the use of high-order Laguerre basis functions and a constrained least-squares approach that addresses the problem of overfitting due to increased model complexity. This method has been extensively evaluated on fluorescence data from simulation, fluorescent standard dyes, ex vivo tissue samples of atherosclerotic plaques and breast cancer specimens and in vivo oral lesions (carcinomas and lichen planus). Current results demonstrate that this method allows for rapid and accurate deconvolution of multiple channel fluorescence decays without adaptively adjusting the Laguerre scale parameter. We are working with the most emerging software engineering tools, including GPU and CPU parallel programming, to develop real-time fluorescence lifetime estimation algorithms for the translation of our research in clinical environments.

Co-registration of FLIm and IVUS images

We have developed a novel fast 3D IVUS segmentation methodology. The image preprocessing consists of denoising, using the Wiener filter, followed by image smoothing, implemented through the application of the alternating sequential filter on the edge separability metric images. Extraction of the lumen/intima and media/adventitia boundaries is achieved by tracing the gray-scale peaks over the A-lines of the IVUS preprocessed images. Cubic spline interpolation, in both cross-sectional and longitudinal directions, ensures boundary smoothness and continuity. The co-registration is achieved by mapping the FLIm data onto the extracted lumen surface and by using the angular delay between FLIm and IVUS introduced by the multimodal catheter. This methodology has been extensively evaluated on ex-vivo pull-back sequences and currently is under translation for in-vivo measurements.

IVUS segmentation

IVUS segmentation results from an ex vivo human coronary artery. The green boundary is the lumen surface while the blue boundary is the media-adventitia layer.

Video01 Video02

Mapping and visualization

FLIm_IVUS_coronary flim_ivus_cross

Fly-through visualization

Fly-through visualization of fluorescence data from a coronary specimen with a stent placed in a lumen. Lifetime image (right) provides a better contrast for stent visualization than fluorescence intensity (left).

Fluorescence Intensity

Fluorescence Intensity

Fluorescence Lifetime

Fluorescence Lifetime

Related Publications

J. Liu, Y. Sun, J. Qi and L. Marcu. “A novel method for fast and robust estimation of fluorescence decay dynamics using constrained least-squares deconvolution with Laguerre expansion” Physics in Medicine and Biology, 57 (4), 2012. (Link)

D. Gorpas, H. Fatakdawala, J. Bec, D. Ma, D.R. Yankelevich, J. Qi, L. Marcu, “Fluorescence Lifetime Imaging and Intravascular Ultrasound: Co-registration Study using ex vivo Human Coronaries” IEEE Transactions on Medical Imaging, (2014), In press. (Link)

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