High-speed polarization-sensitive optical coherence tomography: toward intraoperative cancer imaging

Successful treatment of most breast cancers requires surgical removal of the tumor. The current standard-of-care does not provide any tools for real-time assessment of the tumor cavity. Optical coherence tomography (OCT) has previously been developed for real-time intraoperative use in hopes of aiding tumor margin assessment. While there has been much success, it remains difficult to distinguish between normal stroma tissue and breast cancer. Polarization-sensitive optical coherence tomography (PS-OCT) is an extension of OCT that measures both the intensity and polarization of light returning from the tissue. PS-OCT is sensitive to changes in collagen structure and may provide additional contrast between normal and diseased tissue. This thesis reports the development of a real-time PS-OCT system using a high-speed swept-source laser and parallel processing on a graphics processing unit (GPU). Results of a preliminary imaging study are also reported, demonstrating that PS-OCT provides enhanced contrast between previously indistinguishable fibrous stroma and invasive ductal carcinoma (IDC)

Computational optical coherence tomography for polarization-sensitive imaging, aberration correction, and wavefront measurement

In this thesis, multiple longstanding challenges in optical imaging are solved by the development of new computational imaging methods, where computational imaging does not simply refer to simulation or modeling, but to the entirety of an imaging technology in which significant computation is required to achieve the final image. Of the many optical imaging technologies currently in use, optical coherence tomography (OCT) is distinctive in that it provides coherent measurement of optical scattering within bulk biological tissue. Unfortunately, the optical wavefront is often distorted by defocus and aberration, from either the imaging system or the sample itself, leading to poor image quality. Through a careful consideration of the optical theory and imaging hardware, computational imaging methods can correct these distortions through creative data acquisition and processing schemes. Here, new computational OCT methods are developed from theory to implementation to address three related challenges in optical imaging. First, computational OCT is extended to polarization-sensitive imaging. This provides the improved resolution and imaging depth of computational OCT with the enhanced contrast of polarization-sensitive imaging. Second, computational OCT is combined with hardware-based wavefront correction. This addresses the low signal-to-noise ratio (SNR) limitation of computational OCT and provides improved performance beyond that of hardware-only correction. Lastly, distortion of the optical wavefront is computationally measured directly from the OCT data. This enables both measurement and correction of the optical wavefront throughout biological samples without additional hardware. Together, these results demonstrate the usefulness of computational OCT across a broad range of important imaging scenarios in biology and medicine

Computational optical coherence tomography for polarization-sensitive imaging, aberration correction, and wavefront measurement

In this thesis, multiple longstanding challenges in optical imaging are solved by the development of new computational imaging methods, where computational imaging does not simply refer to simulation or modeling, but to the entirety of an imaging technology in which significant computation is required to achieve the final image. Of the many optical imaging technologies currently in use, optical coherence tomography (OCT) is distinctive in that it provides coherent measurement of optical scattering within bulk biological tissue. Unfortunately, the optical wavefront is often distorted by defocus and aberration, from either the imaging system or the sample itself, leading to poor image quality. Through a careful consideration of the optical theory and imaging hardware, computational imaging methods can correct these distortions through creative data acquisition and processing schemes. Here, new computational OCT methods are developed from theory to implementation to address three related challenges in optical imaging. First, computational OCT is extended to polarization-sensitive imaging. This provides the improved resolution and imaging depth of computational OCT with the enhanced contrast of polarization-sensitive imaging. Second, computational OCT is combined with hardware-based wavefront correction. This addresses the low signal-to-noise ratio (SNR) limitation of computational OCT and provides improved performance beyond that of hardware-only correction. Lastly, distortion of the optical wavefront is computationally measured directly from the OCT data. This enables both measurement and correction of the optical wavefront throughout biological samples without additional hardware. Together, these results demonstrate the usefulness of computational OCT across a broad range of important imaging scenarios in biology and medicine.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Combined hardware and computational optical wavefront correction

In many optical imaging applications, it is necessary to overcome aberrations to obtain high-resolution images. Aberration correction can be performed by either physically modifying the optical wavefront using hardware components, or by modifying the wavefront during image reconstruction using computational imaging. Here we address a longstanding issue in computational imaging: photons that are not collected cannot be corrected. This severely restricts the applications of computational wavefront correction. Additionally, performance limitations of hardware wavefront correction leave many aberrations uncorrected. We combine hardware and computational correction to address the shortcomings of each method. Coherent optical backscattering data is collected using high-speed optical coherence tomography, with aberrations corrected at the time of acquisition using a wavefront sensor and deformable mirror to maximize photon collection. Remaining aberrations are corrected by digitally modifying the coherently-measured wavefront during imaging reconstruction. This strategy obtains high-resolution images with improved signal-to-noise ratio of in vivo human photoreceptor cells with more complete correction of ocular aberrations, and increased flexibility to image at multiple retinal depths, field locations, and time points. While our approach is not restricted to retinal imaging, this application is one of the most challenging for computational imaging due to the large aberrations of the dilated pupil, time-varying aberrations, and unavoidable eye motion. In contrast with previous computational imaging work, we have imaged single photoreceptors and their waveguide modes in fully dilated eyes with a single acquisition. Combined hardware and computational wavefront correction improves the image sharpness of existing adaptive optics systems, and broadens the potential applications of computational imaging methods

Computational high-resolution optical imaging of the living human retina,” Nat. Photonics 9,

High-resolution in vivo imaging is of great importance for the fields of biology and medicine. The introduction of hardwarebased adaptive optics (HAO) has pushed the limits of optical imaging, enabling high-resolution near diffraction-limited imaging of previously unresolvable structures A number of ophthalmoscopes capable of imaging various regions of the living eye have been developed over the years. Focusing primarily on the cornea and retina, these instruments enable the diagnosis and tracking of a wide variety of conditions involving the eye. In particular, optical coherence tomography (OCT) 6,7 has become a standard of care for diagnosing and tracking diseases such as glaucoma and age-related macular degeneration, with research extending into applications such as diabetic retinopathy 8 and multiple sclerosis 9 . When imaging the retina, imperfections of the eye cause patient-specific optical aberrations that degrade the image-forming capabilities of the optical system and possibly limit the diagnostic potential of the imaging modality. As a result of these aberrations, it is known that in the normal uncorrected human eye, diffraction-limited resolution can typically only be achieved with a beam diameter less than 3 mm, resulting in an imaging resolution of only 10-15 µm (ref. 7). With the correction of ocular aberrations, a larger beam could be used (up to ∼7 mm in diameter), achieving a resolution of 2-3 µm (at 842 nm) 3 -this is the accomplishment of hardware-based adaptive optics. Traditionally, HAO incorporates two additional pieces of hardware into an imaging system: a wavefront sensor (WS) and a deformable mirror (DM). The WS estimates the aberrations present in the imaging system (in this case the eye) and the DM corrects the wavefront aberrations. Together, these two pieces of hardware are part of a feedback loop to maintain near diffraction-limited resolution at the time of imaging. Further complicating the system is the need for optics that ensure that the plane introducing the wavefront aberrations (in the case of ophthalmic imaging, this is the cornea) is imaged to the WS and the DM, as well as software to calibrate and coordinate all the hardware involved. In all, the addition of an HAO system can more than double the cost of the underlying imaging modality and, without the possibility of post-acquisition corrections, the full dependence on hardware requires that optimal images are acquired at the time of imaging, potentially lengthening the time required to image the patient/subject. Although much time has been spent on the development of HAO systems, due to these difficulties, commercialization has only now begun with the introduction of the first HAO fundus camera (rtx1, Imagine Eyes). As a result of these difficulties, alternative (computational) approaches to HAO in the human eye have been considered, such as blind or WS-guided deconvolution Here, in combination with an automated computational aberration correction algorithm (Supplementary Section II) and a phase stabilization technique (Supplementary Section III), an en face OCT system

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