Yahui Wang

Quantitative optical coherence elastography based on fiber-optic probe for in situ measurement of tissue mechanical properties

We developed a miniature quantitative optical coherence elastography (qOCE) instrument with an integrated Fabry-Perot force sensor, for in situ elasticity measurement of biological tissue. The technique has great potential for biomechanics modeling and clinical diagnosis. We designed the fiber-optic qOCE probe that was used to exert a compressive force to deform tissue at the tip of the probe. Using the space-division multiplexed optical coherence tomography (OCT) signal detected by a spectral domain OCT engine, we were able to quantify the probe deformation that was proportional to the force applied, and to quantify the tissue deformation corresponding to the external stimulus. Simultaneous measurement of force and displacement allowed us to extract Youngs modulus of biological tissue. We experimentally calibrated our qOCE instrument, and validated its effectiveness on tissue mimicking phantoms and biological tissues.

Robust motion tracking based on adaptive speckle decorrelation analysis of OCT signal

Speckle decorrelation analysis of optical coherence tomography (OCT) signal has been used in motion tracking. In our previous study, we demonstrated that cross-correlation coefficient (XCC) between Ascans had an explicit functional dependency on the magnitude of lateral displacement (δx). In this study, we evaluated the sensitivity of speckle motion tracking using the derivative of function XCC(δx) on variable δx. We demonstrated the magnitude of the derivative can be maximized. In other words, the sensitivity of OCT speckle tracking can be optimized by using signals with appropriate amount of decorrelation for XCC calculation. Based on this finding, we developed an adaptive speckle decorrelation analysis strategy to achieve motion tracking with optimized sensitivity. Briefly, we used subsequently acquired Ascans and Ascans obtained with larger time intervals to obtain multiple values of XCC and chose the XCC value that maximized motion tracking sensitivity for displacement calculation. Instantaneous motion speed can be calculated by dividing the obtained displacement with time interval between Ascans involved in XCC calculation. We implemented the above-described algorithm in real-time using graphic processing unit (GPU) and demonstrated its effectiveness in reconstructing distortion-free OCT images using data obtained from a manually scanned OCT probe. The adaptive speckle tracking method was validated in manually scanned OCT imaging, on phantom as well as in vivo skin tissue.

Noise adaptive wavelet thresholding for speckle noise removal in optical coherence tomography

Optical coherence tomography (OCT) is based on coherence detection of interferometric signals and hence inevitably suffers from speckle noise. To remove speckle noise in OCT images, wavelet domain thresholding has demonstrated significant advantages in suppressing noise magnitude while preserving image sharpness. However, speckle noise in OCT images has different characteristics in different spatial scales, which has not been considered in previous applications of wavelet domain thresholding. In this study, we demonstrate a noise adaptive wavelet thresholding (NAWT) algorithm that exploits the difference of noise characteristics in different wavelet sub-bands. The algorithm is simple, fast, effective and is closely related to the physical origin of speckle noise in OCT image. Our results demonstrate that NAWT outperforms conventional wavelet thresholding.

Quantitative optical coherence elastography based on fiber-optic probe with integrated Fabry-Perot force sensor

Optical coherence tomography (OCT) is a versatile imaging technique and has great potential in tissue characterization for breast cancer diagnosis and surgical guidance. In addition to structural difference, cancerous breast tissue is usually stiffer compared to normal adipose breast tissue. However, previous studies on compression optical coherence elastography (OCE) are qualitative rather than quantitative. It is challenging to identify the cancerous status of tissue based on qualitative OCE results obtained from different measurement sessions or from different patients. Therefore, it is critical to develop technique that integrates structural imaging and force sensing, for quantitative elasticity characterization of breast tissue. In this work, we demonstrate a quantitative OCE (qOCE) microsurgery device which simultaneously quantifies force exerted to tissue and measures the resultant tissue deformation. The qOCE system is based on a spectral domain OCT engine operated at 1300 nm and a probe with an integrated Febry-Perot (FP) interferometric cavity at its distal end. The FP cavity is formed by the cleaved end of the lead-in fiber and the end surface of a GRIN lens which allows light to incident into tissue for structural imaging. The force exerted to tissue is quantified by the change of FP cavity length which is interrogated by a fiber-optic common-paths phase resolved OCT system with sub-nanometer sensitivity. Simultaneously, image of the tissue structure is acquired from photons returned from tissue through the GRIN lens. Tissue deformation is obtained through Doppler analysis. Tissue elasticity can be quantified by comparing the force exerted and tissue deformation.

Quantitative Optical Coherence Elastography for Robust Stiffness Assessment

We demonstrated the capability of quantitative optical coherence elastography (qOCE) for robust assessment of material stiffness under different boundary conditions using the reaction force and displacement field established in the sample.

Depth resolved optical coherence elastography based on fiber-optic probe with integrated Fabry-Perot force sensor

We performed depth resolved elasticity measurement based on a quantitative optical coherence elastography system with integrated Fabry-Perot force sensor. We performed depth resolved stiffness measurement on a muti-layer phantom under different axial force.

Entropy analysis of OCT signal for automatic tissue characterization

Optical coherence tomography (OCT) signal can provide microscopic characterization of biological tissue and assist clinical decision making in real-time. However, raw OCT data is noisy and complicated. It is challenging to extract information that is directly related to the pathological status of tissue through visual inspection on huge volume of OCT signal streaming from the high speed OCT engine. Therefore, it is critical to discover concise, comprehensible information from massive OCT data through novel strategies for signal analysis. In this study, we perform Shannon entropy analysis on OCT signal for automatic tissue characterization, which can be applied in intraoperative tumor margin delineation for surgical excision of cancer. The principle of this technique is based on the fact that normal tissue is usually more structured with higher entropy value, compared to pathological tissue such as cancer tissue. In this study, we develop high-speed software based on graphic processing units (GPU) for real-time entropy analysis of OCT signal.