Thesis (Ph. D.)--University of Florida, 1999. / Title from first page of PDF file. Document formatted into pages; contains xiv, 137 p.; also contains graphics. Vita. Includes bibliographical references (p. 129-136).

Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisors: Alan C. Bovik and Lawrence K. Cormack. Vita. Includes bibliographical references.

Dissertation (Ph.D.) -- Worcester Polytechnic Institute. / Keywords: MRI; Image reconstruction; Image registration; Medical image. Includes bibliographical references (leaves 115-137).

<p dir="ltr">Advancements in machine learning are narrowing the gap in visual capabilities between machines and healthcare professionals, resulting in a transformation of the way we understand and address health challenges. Despite these advances, underlying limitations persist in addressing real-world problems, particularly in the precise capture of biological and physiological information. This is primarily because traditional trichromatic cameras fall short of representing reflectance spectra due to their limited spectral information. To overcome these limitations, hyperspectral imaging has emerged as a powerful tool for biomedical applications. By collecting a wealth of information at different wavelengths, hyperspectral imaging provides a comprehensive view of electromagnetic spectra, allowing non-invasive clinical analysis for accurate diagnostics. Snapshot hyperspectral imaging, in particular, is a competitive alternative to traditional cameras as it can capture a hyperspectral image in a single shot without the need for scanning individual wavelengths. Here, we introduce a computational snapshot hyperspectral imaging method, achieved through the integration of a machine learning approach with a streamlined optical system. We design an explainable machine learning algorithm by incorporating optical and biological knowledge into the algorithm. Therefore, the algorithm can reconstruct hyperspectral images with high spectralspatial resolution comparable to those of scientific spectrometers, despite the use of sparse information captured from the optical system. To demonstrate its versatility in biomedical applications, we extract hemodynamic parameters of peripheral microcirculation from embryonic model systems, tissue phantom samples, and human conjunctivas. Furthermore, we validate high accuracy of the results using conventional hyperspectral imaging and functional near-infrared spectroscopy. This learning-powered imaging method, characterized by high resolution and simplified hardware requirements, has the potential to offer solutions for various biomedical challenges by surpassing the constraints of conventional cameras and hyperspectral imaging.</p>

<p dir="ltr">Single molecule localization microscopy (SMLM) has become an essential tool in imaging nanoscale biological structures. It breaks the diffraction limit by utilizing photo-switchable or photo-convertible fluorophores to obtain isolated single molecule emission patterns (i.e. PSFs) and subsequently localize the molecule’s position with a precision down to ~ 20 to 80 nm laterally-axially. However, optical aberrations compromise its spatial resolution. Additionally, conventional SMLM algorithms require sparse activation to reduce emission pattern overlap, which restricts imaging speed and temporal resolution, thus limiting its utility in dynamic live cell imaging. In this study, we first conducted a comprehensive quantitative analysis of the theoretical precision limits for position and wavefront distortion measurements in the presence of aberrations, which enhances our understanding of aberration effects in SMLM and lays the groundwork for developing more effective aberration correction methods. To improve temporal resolution, we developed a high-density single molecule localization algorithm that utilizes deep learning to analyze molecule blinking data. This approach allows us to achieve high localization precision and resolve structures at tens of nanometers resolution, even with highly overlapped blinking data. Validated by both simulated and high-density experimental data, our algorithm successfully resolves the complex structures of various cellular organelles and captures rapid dynamic movements in live cells. This work addresses the knowledge gap about aberrations in SMLM and expands its applications to more dynamic and detailed studies of cellular processes.</p>

Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Hu, Xiaoping; Committee Member: Brummer, Marijn; Committee Member: Duong, Tim; Committee Member: Keilholz, Shella; Committee Member: Schumacher, Eric. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Tissue optical properties, absorption, scattering and fluorescence, reveal important information about health, and holds the potential for non-invasive diagnosis and therefore earlier treatment for many diseases. On the other hand, tissue structure determines its function. Studying tissue structural properties helps us better understand structure-function relationship. Optical imaging is an ideal tool to study these tissue properties. However, conventional optical imaging techniques have limitations, such as not being able to quantitatively evaluate tissue absorption and scattering properties and only providing volumetrically averaged quantities with no depth control capability. To better study tissue properties, we integrated spatial frequency domain imaging (SFDI) with conventional reflectance imaging modalities. SFDI is a non-invasive, non-contact wide-field imaging technique which utilizes structured illumination to probe tissues. SFDI imaging is able to accurately quantify tissue optical properties. By adjusting spatial frequency, the imaging depth can be tuned which allows for depth controlled imaging. Especially at high spatial frequency, SFDI reflectance image is more sensitive to tissue scattering property than absorption property. The imaging capability of SFDI allows for studying tissue properties from a whole new perspective. In our study, we developed both benchtop and handheld SFDI imaging systems to accommodate different applications. By evaluating tissue optical properties, we corrected attenuation in fluorescence imaging using an analytical model; and we quantified optical and physical properties of skin diseases. By imaging at high spatial frequency, we demonstrated that absorption in fluorescence imaging can also be reduced because of a reduced imaging depth. This correction can be performed in real-time at 19 frames/second. Furthermore, fibrous structures orientation from the superficial layer can be accurately quantified in a multi-layered sample by limiting imaging depth. Finally, we color rendered SFDI reflectance image at high spatial frequency to reveal structural changes in skin lesions.

No description available.

<p>'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption. </p><p>This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications. </p><p>Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level.</p><p>Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,\lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions.</p><p>Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke.</p><p>Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.</p> / Dissertation

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 48-51). Also available on the World Wide Web.