科研

Fast spectroscopy imaging technique in flap surgery

In flap surgery, early identification of flap failure is a precondition of flap salvage and therefore important for flap prognosis. The current clinical method for assessment of flap viability is with the help of laser Doppler imaging by evaluating blood flow. However, this technique is not accurate enough because it only assesses the tissue viability by blood flow, which is just one of the parameters affecting tissue viability.
Diagnostic techniques based on optical spectroscopy have the potential to accurately evaluate tissue viability because these techniques are sensitive, noninvasive and quantitive. However, the common disadvantage is that spectroscopy technique is slow acquisition speed due to the employment of the fiber-optic probe configuration.
In this project, we propose to develop a rapid imaging technique with complementary and more sensitive tissue viability information for the physicians, which will be helpful for better flap design and more accurate timing of flap division.

Fast rconstruction of Raman spectra from narrow-band measurements

Raman spectroscopy has demonstrated great potential in clinical and biomedical applications. But slow data acquisition due to weak Raman signals has prevented its use in measuring events varying with time, especially in an imaging setup.
We have developed a fast Raman spectroscopic imaging technique which is based on spectral reconstruction technique, i.e. Wiener estimation. Instead of taking spectrum at each pixel from the entire image, we only use a few narrow-band filters with much larger bandwidths to get a few Raman images and the full Raman spectrum at each pixel can be reconstructed, as shown in Figure (A). The potential improvement in the speed is dramatic because of the difference in the number of Raman images required between traditional Raman imaging and the proposed strategy. In addition, we built a fast wide-field Raman spectroscopic imaging system based on simultaneous multi-channel image acquisition and spectral reconstruction technique. This technique can be potentially used to inspect both non-biological samples in applications such as quality control in pharmaceutical industry and biological samples in clinical diagnosis and biological science research.

Raman noise removal with extremly low SNR

Raman spectroscopy is a spectroscopic technique that measures the inelastic scattering of photons induced by interaction with molecular bonds. Raman spectroscopy has been widely used in biomedical and clinical applications. However, Raman signal is always very weak and can be easily affect by the noise.
This project aims to remove the noise when the Raman spectra with extremly low SNR.

Software controlling algorithms for CLSM

The relative slow scanning speed of a galvanometer commonly used in a confocal laser scanning microscopy system can dramatically limit the system performance in scanning speed and image quality, if the data collection is simply synchronized with the galvanometric scanning. Several algorithms for the optimization of the galvanometric CLSM system performance are discussed in this work, with various hardware controlling techniques for the image distortion correction such as pixel delay and interlace line switching; increasing signal-to-noise ratio with data binning; or enhancing the imaging speed with region of interest imaging. Moreover, the pixel number can be effectively increased with Acquire-On-Fly scan, which can be used for the imaging of a large field-of-view with a high resolution.

Modeling of PSF of confocal microscope images

With the rapid development of computer and electro-optical microscopy technology, confocal microscopy has been served as a very important tool for biological cellular imaging with higher resolution and better quality. However, the images may be blurred because of the light scattering or distortion in the optical system, which can be modeled as Point Spread Function (PSF).
In this project, we present a method for the estimation of the PSF by using the PSF model. Firstly, extract the centerline by image processing, e.g. gradient anisotropic diffusion, multi-scale vesselness and binary thinning. Then, estimate the PSF by fitting the PSF model using iteration technique. And the result shows that this method is practical and in well performance.

Other projects

Multimodality Confocal imaging and its application in revealing biomedical histopathology
Extraction of blood flow patterns from coronary angiographic data
Extraction of blood flow patterns and calculation of heart function parameters in dual source CT angiography