Warm-started wavefront reconstruction for adaptive optics

Future extreme adaptive optics (ExAO) systems have been suggested with up to 10^5 sensors and actuators. We analyze the computational speed of iterative reconstruction algorithms for such large systems. We compare a total of 15 different scalable methods, including multigrid, preconditioned conjugate-gradient, and several new variants of these. Simulations on a 128×128 square sensor/actuator geometry using Taylor frozen-flow dynamics are carried out using both open-loop and closed-loop measurements, and algorithms are compared on a basis of the mean squared error and floating-point multiplications required. We also investigate the use of warm starting, where the most recent estimate is used to initialize the iterative scheme. In open-loop estimation or pseudo-open-loop control, warm starting provides a significant computational speedup; almost every algorithm tested converges in one iteration. In a standard closed-loop implementation, using a single iteration per time step, most algorithms give the minimum error even in cold start, and every algorithm gives the minimum error if warm started. The best algorithm is therefore the one with the smallest computational cost per iteration, not necessarily the one with the best quasi-static performance

High-density speckle contrast optical tomography (SCOT) for three dimensional tomographic imaging of the small animal brain

High-density speckle contrast optical tomography (SCOT) utilizing tens of thousands of source-detector pairs, was developed for in vivo imaging of blood flow in small animals. The reduction in cerebral blood flow (CBF) due to local ischemic stroke in a mouse brain was transcanially imaged and reconstructed in three dimensions. The reconstructed volume was then compared with corresponding magnetic resonance images demonstrating that the volume of reduced CBF agrees with the infarct zone at twenty-four hours.Peer ReviewedPostprint (author's final draft

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation

Chemotherapeutic drug-specific alteration of microvascular blood flow in murine breast cancer as measured by diffuse correlation spectroscopy

The non-invasive, in vivo measurement of microvascular blood flow has the potential to enhance breast cancer therapy monitoring. Here, longitudinal blood flow of 4T1 murine breast cancer (N=125) under chemotherapy was quantified with diffuse correlation spectroscopy based on layer models. Six different treatment regimens involving doxorubicin, cyclophosphamide, and paclitaxel at clinically relevant doses were investigated. Treatments with cyclophosphamide increased blood flow as early as 3 days after administration, whereas paclitaxel induced a transient blood flow decrease at 1 day after administration. Early blood flow changes correlated strongly with the treatmePeer ReviewedPostprint (published version

Quantitative imaging of the complexity in liquid bubbles' evolution reveals the dynamics of film retraction

The dynamics and stability of thin liquid films have fascinated scientists over many decades. Thin film flows are central to numerous areas of engineering, geophysics, and biophysics and occur over a wide range of length, velocity, and liquid properties scales. In spite of many significant developments in this area, we still lack appropriate quantitative experimental tools with the spatial and temporal resolution necessary for a comprehensive study of film evolution. We propose tackling this problem with a holographic technique that combines quantitative phase imaging with a custom setup designed to form and manipulate bubbles. The results, gathered on a model aqueous polymeric solution, provide an unparalleled insight into bubble dynamics through the combination of full-field thickness estimation, three-dimensional imaging, and fast acquisition time. The unprecedented level of detail offered by the proposed methodology will promote a deeper understanding of the underlying physics of thin film dynamics

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Design Considerations for a Highly Segmented Mirror

Design issues for a 30-m highly segmented mirror are explored, with emphasis on parametric models of simple, inexpensive segments. A mirror with many small segments offers cost savings through quantity production and permits high-order active and adaptive wave-front corrections. For a 30-m f/1.5 paraboloidal mirror made of spherical, hexagonal glass segments, with simple warping harnesses and three-point supports, the maximum segment diameter is ~100 mm, and the minimum segment thickness is ~5 mm. Large-amplitude, low-order gravitational deformations in the mirror cell can be compensated if the segments are mounted on a plate floating on astatic supports. Because gravitational deformations in the plate are small, the segment actuators require a stroke of only a few tens of micrometers, and the segment positions can be measured by a wave-front sensor

Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye

Retinal vascular diseases are important causes of vision loss. A detailed evaluation of the vascular abnormalities facilitates diagnosis and treatment in these diseases. Optical coherence tomography (OCT) angiography using the highly efficient split-spectrum amplitude decorrelation angiography algorithm offers an alternative to conventional dye-based retinal angiography. OCT angiography has several advantages, including 3D visualization of retinal and choroidal circulations (including the choriocapillaris) and avoidance of dye injection-related complications. Results from six illustrative cases are reported. In diabetic retinopathy, OCT angiography can detect neovascularization and quantify ischemia. In age-related macular degeneration, choroidal neovascularization can be observed without the obscuration of details caused by dye leakage in conventional angiography. Choriocapillaris dysfunction can be detected in the nonneovascular form of the disease, furthering our understanding of pathogenesis. In choroideremia, OCT's ability to show choroidal and retinal vascular dysfunction separately may be valuable in predicting progression and assessing treatment response. OCT angiography shows promise as a noninvasive alternative to dye-based angiography for highly detailed, in vivo, 3D, quantitative evaluation of retinal vascular abnormalities.National Institutes of Health (U.S.) (Grant R01-EY023285)National Institutes of Health (U.S.) (Grant R01-EY024544)National Institutes of Health (U.S.) (Grant DP3 DK104397)National Institutes of Health (U.S.) (Grant R01-EY11289)National Institutes of Health (U.S.) (Grant K08-EY021186)National Institutes of Health (U.S.) (Grant T32-EY23211)National Institutes of Health (U.S.) (Grant P30-EY010572)Clinical and Translational Science Award Grant UL1TR000128Research to Prevent Blindness, Inc. (United States) (Grant and Career Development Award CD-NMT-0914-0659-OHSU)United States. Air Force Office of Scientific Research (Foundation Fighting Blindness Career Development Award FA9550-10-1-0551)German Research Foundation (Grant DFG-HO-1791/11-1)German Research Foundation (Grant DFG-GSC80-SAOT