Diffuse optical tomography (DOT) is an imaging technique that utilizes near-infrared (NIR) light to probe biological tissue and ultimately recover the optical parameters of the tissue. Broadly, the process for image reconstruction in DOT involves three parts: (1) the detected measurements, (2) the modeling of the medium being imaged, and (3) the algorithm that incorporates (1) and (2) to finally estimate the optical properties of the medium.
These processes have long been established in the DOT field but are also known to suffer drawbacks. The measurements themselves tend to be susceptible to experimental noise that could degrade reconstructed image quality. Furthermore, depending on the DOT configuration being utilized, the total number of measurements per capture can get very large and add additional computational burden to the reconstruction algorithms. DOT algorithms are reliant on accurate modeling of the medium, which includes solving a light propagation model and/or generating a so-called sensitivity matrix. This process tends to be complex and computationally intensive and, furthermore, does not take into account real system characteristics and fluctuations. Similarly, the inverse algorithms typically utilized in DOT also often take on a high computational volume and complexity, leading to long reconstruction times, and have limited accuracy depending on the measurements, forward model, and experimental system.
The purpose of this dissertation is to address and develop computational methods, especially incorporating deep learning, to improve each of these components. First, I evaluated several time-domain data features involving the Mellin and Laplace transforms to incorporate measurements that were robust to noise and sensitive at depth for reconstruction. Furthermore, I developed a method to find the optimal values to use for different imaging depths and scenarios. Second, I developed a neural network that can directly learn the forward problem and sensitivity matrix for simulated and experimental measurements, which allows the computational forward model to adapt to the system's characteristics. Finally, I employed learning-based approaches based on the previous results to solve the inverse problem to recover the optical parameters in a high-speed manner.
Each of these components were validated and tested with numerical simulations, phantom experiments, and a variety of in vivo data. Altogether, the results presented in this dissertation depict how these computational approaches lead to an improvement in DOT reconstruction quality, speed, and versatility. It is the ultimate hope that these methods, algorithms, and frameworks developed as a part of this dissertation can be directly used on future data to further validate the research presented here and to further validate DOT as a valuable imaging tool across many applications.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/g96y-jf28 |
| Date | January 2024 |
| Creators | Wang, Fay |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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