ECE PhD Dissertation Defense: Alex Matlock
- 1:00 pm on Friday, June 18, 2021
- 3:00 pm on Friday, June 18, 2021
Title: Model and Learning-Based Strategies for Intensity Diffraction Tomography
Presenter: Alex Matlock
Date: June 18, 2021
Time: 1:00PM - 3:00PM
Meeting ID: 948 6494 6502
Advisor: Professor Lei Tian (ECE, BME)
Committee: Professor Selim Ünlü (ECE, MSE, BME), Professor Jerome Mertz (BME, ECE, Physics), Professor Ji-Xin Cheng (ECE, BME, MSE)
Intensity Diffraction Tomography (IDT) is a recently developed quantitative phase imaging tool with significant potential for biological imaging applications. This modality captures intensity images from a scattering sample under diverse illumination and reconstructs the object's volumetric permittivity contrast using linear inverse scattering models. IDT requires no through-focus sample scans or exogenous contrast agents for 3D object recovery and can be easily implemented with a standard microscope equipped with an off-the-shelf LED array. These factors make IDT ideal for biological research applications where easily implementable setups providing native sample morphological information are highly desirable. Given this modality's recent development, IDT suffers from a number of limitations preventing its widespread adoption: 1) large measurement datasets with long acquisition times limiting its temporal resolution, 2) model-based constraints preventing the evaluation of multiple-scattering samples, and 3) low axial resolution preventing the recovery of fine axial structures such as organelles and other subcellular structures. These factors limit IDT to primarily thin, static objects, and its unknown accuracy and sensitivity metrics cast doubt on the technology's quantitative recovery of morphological features.
This thesis addresses the limitations of IDT through advancements provided from model and learning-based strategies. The model-based advancements guide new computational illumination strategies for high volume-rate imaging as well as investigate new imaging geometries, while the learning-based enhancements to IDT present an efficient method for recovering multiple-scattering biological specimens. These advancements place IDT in the optimal position of being an easily implementable, computationally efficient phase imaging modality recovering high-resolution volumes of complex, living biological samples in their native state.
We first discuss two illumination strategies for high-speed IDT. The first strategy develops a multiplexed illumination framework based on IDT's linear model enabling hardware-limited 4Hz volume-rate imaging of living biological samples. This implementation is hardware-agnostic, allowing for fast IDT to be added to any existing setup containing programmable illumination hardware. While sacrificing some reconstruction quality, this multiplexed approach recovers high-resolution features in live cell cultures, worms, and embryos highlighting IDT's potential across numerous ranges of biological imaging.
Following this illumination scheme, we discuss a hardware-based solution for live sample imaging using ring-geometry LED arrays. Inspired from the linear model, this hardware modification optimally captures the object's information in each LED illumination allowing for high-quality object volumes to be reconstructed from as few as eight intensity images. This small image requirement allows IDT to achieve camera-limited 10Hz volume rate imaging of live biological samples without motion artifacts. We show the capabilities of this annular illumination IDT setup on live worm samples. This low-cost solution for IDT's speed shows huge implications for enabling any biological imaging lab to easily study the form and function of biological samples of interest in their native state.
Next, we present a learning-based approach to expand IDT to recovering multiple-scattering samples.
IDT's linear model provides efficient computation of an object's 3D volume but fails to recover quantitative information in the presence of highly scattering samples. We introduce a lightweight neural network architecture, trained only on simulated natural image-based objects, that corrects the linear model estimates and improves the recovery of both weakly and strongly scattering samples. This implementation maintains the computational efficiency of IDT while expanding its reconstruction capabilities allowing for more generic imaging of biological samples.
Finally, we discuss an investigation of the IDT modality for reflection mode imaging. IDT traditionally captures only low axial resolution information because it cannot capture the backscattered fields from the object that contain rich information regarding the fine details of the object's axial structures. Here, we investigated whether a reflection-mode IDT implementation was possible for recovering high axial resolution structures from this backscattered light. We develop the model, imaging setup, and rigorously evaluate the reflection case in simulation and experiment to show the possibility for reflection IDT. While this imaging geometry ultimately requires a nonlinear model for 3D imaging, we show the technique provides enhanced sensitivity to the object's structures in a complementary fashion to transmission-based IDT.