Thesis Proposal: Shuwen Wei

March 11, 2021 @ 3:00 pm
Thesis Proposal: Shuwen Wei

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Optical coherence tomography signal processing in complex domain

Abstract: Optical coherence tomography (OCT) plays an indispensable role in clinical fields such as ophthalmology and dermatology. Over the past 30 years, OCT has gone through tremendous developments, which come with both hardware improvements and novel signal processing techniques. Hardware improvements such as the use of adaptive optics (AO) and the use of vertical-cavity surface-emitting laser (VCSEL) help push the fundamental limits of OCT imaging capability. Novel signal processing techniques aim to push the imaging capability beyond current hardware architecture limitations. Often, novel signal processing techniques achieve better performances than hardware modifications while keeping the cost to the lowest. The purpose of this dissertation proposal is to develop novel OCT signal processing techniques that provide new imaging capabilities and overcome current imaging limitations.

OCT signal, as the result of the interference between the sample back-scattering light and the reference light, is complex and contains both amplitude and phase information. The amplitude information is mostly used for OCT structural imaging, while the phase information is mostly used for OCT functional imaging. Usually, the amplitude-based methods are more robust since they are less prone to noise, while the phase-based methods are better in quantifying precision measurements since they are more sensitive to micro displacements. This dissertation proposal focuses on three advanced OCT signal processing techniques in both amplitude and phase domain.

The first signal processing technique proposed is the amplitude-based BC-mode OCT image visualization for microsurgery guidance, where multiple sparsely sampled B-scans are combined to generate a single cross-section image with enhanced instrument and tissue layer visibility and reduced shadowing artifacts. The performance of the proposed method is demonstrated by guiding a 30-gauge needle into an ex-vivo human cornea.

The second signal processing technique proposed is the amplitude-based optical flow OCT (OFOCT) for determining accurate velocity fields. Modified continuity constraint is used to compensate the Fourier-domain OCT (FDOCT) sensitivity fall-off. Spatial-temporal smoothness constraints are used to make the optical flow problem well-posed and reduce noises in the velocity fields. The accuracy of the proposed method is verified through phantom flow experiments by using a diluted milk powder solution as the scattering medium, in both cases of advective flow and turbulent flow.

The third signal processing technique proposed is phase-based. A wrapped Gaussian mixture model (WGMM) is proposed to stabilize the phase of swept-source OCT (SSOCT) systems. The OCT signal phase is divided into several components and each component is fully analyzed. The WGMM is developed based on the previous analysis. A closed-form iteration solution of the WGMM is derived using the expectation-maximization (EM) algorithm. The performance of the proposed method is demonstrated through OCT imaging of ex-vivo mice cornea and anterior chamber.

For all the three proposed methods above, process has been made in theoretical modeling, numerical implementations, and experimental verifications. All the algorithms have been implemented in the graphic processing unit (GPU) in the OCT system for real-time data processing. Preliminary results demonstrate good performances of these proposed methods. The final thesis work will include optimizing the proposed methods and applying the implemented algorithms to both ex-vivo and in-vivo biomedical research for the overall system testing and analysis.

Committee Members

  • Jin U. Kang (Advisor), Department of Electrical and Computer Engineering
  • Trac D. Tran, Department of Electrical and Computer Engineering
  • Xingde Li, Department of Biomedical Engineering
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