Author ORCID Identifier

https://orcid.org/0009-0001-8739-9661

Date of Award

Summer 8-31-2025

Document Type

Open Access Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Integrative Biosciences

First Advisor

Chandra S Yelleswarapu

Second Advisor

Daniel Haehn

Abstract

Access to advanced biomedical imaging technologies remains a significant challenge in resource-limited settings, especially for early disease detection and monitoring of diseases such as malaria, HIV, and other blood-borne diseases. Although point-of-care (POC) devices have gained popularity in global health, many rely on antibody-based tests, lateral flow strips, or optical readouts that often lack quantitative capabilities, sensitivity to early infections, or versatility in different diagnostic targets. In addition, these systems are typically dependent on disposable reagents or manual interpretation, which limits their effectiveness in remote areas. Digital Holographic Microscopy (DHM) presents a promising alternative as a label-free imaging method capable of quantitatively analyzing phase changes in transparent biological samples, such as red blood cells, cancer cells, and tissue cultures. However, traditional DHM setups are bulky and rely heavily on complex optical components and external computing hardware for data processing and visualization, restricting their use outside controlled laboratory environments. Lensless inline DHM (LiDHM) overcomes these limitations by removing the need for objective lenses and intricate optics, utilizing coherent or quasi-coherent illumination, and capturing holograms directly on image sensors. Its straightforward alignment, compact design, and compatibility with affordable, off-the-shelf parts make it ideal for portable, low-cost diagnostic devices. However, inline configurations present specific computational challenges, notably twin-image artifacts and phase-retrieval ambiguities, which must be addressed to unlock the full potential of LiDHM. This dissertation details a comprehensive integration of computational and hardware innovations aimed at enabling clinical-grade LiDHM in a field-deployable format. Central to this effort is the development of the Phase Constraint on the Phase-Only Function (PCOF) framework, a novel reconstruction algorithm that effectively suppresses twin-image artifacts, enhances phase contrast, and maintains structural fidelity without manual tuning. Validation through simulations and experiments showed that the PCOF outperforms conventional angular spectrum and iterative phase-retrieval methods. To translate these advancements into a practical diagnostic tool, a compact, 3D-printed LiDHM system was designed and built, powered by a Raspberry Pi microcomputer with a touchscreen interface. The system features a real-time graphical user interface and supports multiple imaging modes, including optical density (OD), quantitative phase imaging (QPI), and dual-modality visualization, with configurable acquisition modes for full-field, fragmented, or region-specific imaging. Its performance was tested on various biological samples, such as microspheres, optical fibers, and unstained epithelial cells. A key use case is the label-free detection and classification of plasmodium-infected red blood cells (iRBCs). By extracting features such as dry mass, refractive index, and phase change from segmented phase images and using a two-step classification process that combines statistical thresholding with feature-specific filtering, the system achieved high specificity in distinguishing iRBCs from healthy RBCs. In conclusion, this dissertation presents a fully integrated, modular and field-ready LiDHM platform that bridges the gap between high-resolution quantitative imaging and accessible diagnostics. This work advances the potential for the deployment of digital phase imaging to detect infectious diseases and other bloodborne diseases in low-resource and underserved environments.

Comments

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