Session Index

Optical coherence tomography (OCT) is a non-invasive optical imaging technology that can provide real-time and two- or three-dimensional information about the sample architecture based on intrinsic optical scattering contrast. The emergence of the second generation of OCT – Fourier-domain (OCT) with so-called Fourier-domain interferometry has revolutionized OCT in biomedical applications, leveraging the increased imaging speed, improved detection sensitivity, and extended imaging range, which also leads to adding the functional information of the imaging tissue, for example, OCT angiography (OCTA) and polarization-sensitive OCT (PS-OCT). In addition, the FD-OCT also further reduced the system complexity, beneficial to the broader commercialization of the OCT technology. In addition to exploring the feasibility of OCT in various biomedical applications, the interest in using OCT for industry non-destructive inspection has increased recently, for example, the volumetric inspection of defects present within semiconductor chips or wafers. In this talk, I will review our recent work on developing high-speed OCT technology for various biomedical applications and share preliminary results on using OCT for volumetric imaging of semiconductor wafers.

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Full-field optical coherence tomography (FF-OCT) achieve non-invasive and high spatial resolution in cell imaging. Presently, we have developed a method to capture dynamic signals in cells using the FF-OCT system. We analyze different dynamic characteristics of cells within the frequency domain, allowing for the generation of color-coded dynamic FF-OCT image through a customized analysis program. This approach enables the exploration of various dynamic characteristics manifested by cells, thereby enhancing comprehension of the samples.

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Full-field optical coherence tomography (FF-OCT) can achieve cellular-resolution imaging non-invasively. Integrating a high-frame-rate CCD sensor and a high-brightness broadband source can pinpoint the dynamic physiological signals with micron resolution within the 3-D imaging volume at a millisecond time scale. As a demonstration, human corneal epithelial cells (HCECs) were cultivated and used as the test sample for the 3-D dynamic FF-OCT system. The dynamic behavior of the HCECs was analyzed in the frequency domain and coded by colors. The 3-D color-coded D-FF-OCT can well reveal spatial-temporal physiological cellular activities of the HCECs.

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This study utilizes multiphoton excited fluorescence microscopy (MPEFM) to scan sections of fibrotic mouse lungs. By employing time-correlated single photon counting (TCSPC), fluorescence lifetime information can be directly obtained. This allows recording the fluorescence lifetime values for each pixel, which are then used to reconstruct a fluorescence lifetime image (FLIM) with spatial resolution of submicron. Finally, the phasor analysis method is applied to identify the presence of collagen type I and type III within the images.

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During the treatment for periodontitis, dental calculus removal is necessary. We have proposed the DAM algorithm previously for lesion identification, which enables the non-contact evaluation during the operation. However, dental calculus delineation was still flawed. Therefore, we utilized the power of polarization-sensitive optical coherence tomography and evaluated the contrast called degree of polarization uniformity for dental calculus visualization.

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With the rising annual prevalence of high myopia patients, the patients are often coupled with myopia choroidal neovascularization (mCNV) exhibiting the potential to induce visual impairment. This study endeavors to harness optical coherence tomography (OCT) and OCT angiography (OCTA) imaging data to build machine learning models capable of distinguishing between active and inactive mCNV cases. The ultimate objective is to curtail the necessity for frequent recourse to fluorescein angiography (FAG).

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In order to address the limitations of circular phantoms and improve the versatility of the reconstruction approach via diffuse optical imaging (DOI), transfer learning is utilized in this work. Transfer learning is employed to adapt the previously developed architecture(s) to handle elliptical phantoms in DOI. By leveraging the knowledge and pre-trained weights obtained from the circular phantom dataset, the network is fine-tuned using the newly acquired elliptical phantom dataset. This approach can potentially enhance the realism and accuracy of DOT imaging, enabling more precise characterization of biological tissues and structures.

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Biophotonics tools offer noninvasive and easily applicable tools for the detection of alternations in structural and biochemical compositions of tissues and cells, which may indicate the presence of disease. The present work discusses about developing reliable, sensitive and objective biophotonics tools for various biomedical applications. Our methods have the added advantage of being label-free, objective because of the fact that diagnostic evaluation is by statistical methods, eliminating errors from lack of experience, fatigue factor, and subjectivity in the decision making.

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