Computational Bio-Optical Imaging with Novel Sensor Arrays

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Optical imaging is an essential tool for studying life sciences. Existing biomedical optical systems range from microscopes in clinics that use wave optics principles to examine pathological samples at high resolution, to photoplethysmography in everyday smartwatches utilizing diffuse optics technologies for monitoring deep tissue physiology. An optical system, such as a photography solution in a studio, typically consists of three parts: illumination, objects of interest, and recording devices. Over the past decades, thanks to rapid advancements in semiconductor manufacturing, numerous new and exciting optical devices have emerged. These include low-cost, small form-factor LEDs and CMOS camera sensors in budget tablet devices, as well as high-density time-of-flight array detectors in recent generations of iPhones, for example. Moore's Law, on the other hand, has driven significant development in powerful yet inexpensive computational tools. As a result, nowadays, analogous to other medical imaging modalities such as X-ray CT and MRI, multiplexed optical measurements that may not resemble the object of interest can be recorded and post-processed to reconstruct useful images for human perception. In this thesis, several new computational optical imaging techniques at different scales will be discussed. These range from vectorial tomographic microscopies for imaging anisotropic cells and tissue, to high-throughput imaging systems capable of recording eukaryotic colonies at mesoscopic scales, and novel single-photon-sensitive sensing methods for non-invasive imaging of macroscopic transient dynamics deep within turbid volumes.





Xu, Shiqi (2023). Computational Bio-Optical Imaging with Novel Sensor Arrays. Dissertation, Duke University. Retrieved from


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