arXiv subjects

George Barbastathis

Publications and source records attributed to George Barbastathis.

At least 19 recordsLinked to original sources

arXiv2026

Quantitative Decomposition of Speckle Decorrelation for Inverse Problems in Complex Wave Scattering

Optical scattering remains one of the richest phenomena to study in classical optics. Wavefronts that are severely distorted due to scattering still carry significant information about the optical path upstream. Decoding typically starts with the autocorrelation function; yet the precise relationship between it and scatterer statistics still remains unexplored, hindering the model-based method for inverse problems. Here, we reveal that decorrelation due to backward scattering may be quantified as two distinct terms: the first expresses scattering from the surface, whereas the second is due to the volume beneath. The two terms encode higher-order statistics of their respective regimes within the specimen; Experimental studies on representative custom-made scatterers match theoretical predictions with an overall L1-error of less than 0.2%, providing an advanced forward model for various model-based inverse approaches. As a proof-of-concept, we present two examples, scatterer particle size estimation and reconstruction of the incident beam profile, to validate this improvement.

physics.optics↗

arXiv2026

Dynamic Coherent Diffractive Imaging Using Only a Support Constraint in the Complex Plane

We show that a bounded temporal increment prior on the sample dynamics is sufficient to reconstruct a time-varying phase object from a near-field diffraction movie, under the thin-film approximation. The time evolution of the field is parameterized by a multiplicative inter-frame update factor, and a bound on its complex-plane support enforces a bounded phase increment and a passive amplitude constraint. Reconstruction of the dynamic field is thereby converted into a feasibility problem with two projection operators: a measurement-domain modulus projection and an object-domain circular-sector projection. We validate the approach experimentally using a spatial light modulator as a calibrated dynamic sample in two cases: a reaction--diffusion phase pattern with spatially expanding extent, and a growing phase pattern whose accumulated phase reaches $10π$. In both cases the reconstructed phase trajectory agrees well with the ground truth. We then apply the same framework, without modification, to in-situ monitoring of a photo-polymer 3D printing process, recovering the spatiotemporal phase induced by polymerization under a spatially patterned blue light. The reconstructed phase trajectory provides an observable for photo-chemical system identification and process control.

physics.optics↗

arXiv2025

Simulation-based Approach for Fast Optimal Control of a Stefan Problem with Application to Cell Therapy

This article describes a new, efficient way of finding control and state trajectories in optimal control problems by reformulation as a system of differential-algebraic equations (DAEs). The optimal control and state vectors can be obtained via simulation of the resulting DAE system with the selected DAE solver, eliminating the need for an optimization solver. Our simulation-based approach is demonstrated and benchmarked against various optimization-based algorithms via four case studies associated with the optimization and control of a Stefan problem for cell therapy. The simulation-based approach is faster than every optimization-based method by more than an order of magnitude while giving similar/better accuracy in all cases. The solution obtained from the simulation-based approach is guaranteed to be optimal provided that at least one constraint or algebraic equation resulting from the reformulation remains active at all times. The proposed technique offers an efficient and reliable framework for optimal control, serving as a promising alternative to the traditional techniques in applications where speed is crucial, e.g., real-time online model predictive control.

eess.SY↗

arXiv2025

AI to Identify Strain-sensitive Regions of the Optic Nerve Head Linked to Functional Loss in Glaucoma

Objective: (1) To assess whether ONH biomechanics improves prediction of three progressive visual field loss patterns in glaucoma; (2) to use explainable AI to identify strain-sensitive ONH regions contributing to these predictions. Methods: We recruited 237 glaucoma subjects. The ONH of one eye was imaged under two conditions: (1) primary gaze and (2) primary gaze with IOP elevated to ~35 mmHg via ophthalmo-dynamometry. Glaucoma experts classified the subjects into four categories based on the presence of specific visual field defects: (1) superior nasal step (N=26), (2) superior partial arcuate (N=62), (3) full superior hemifield defect (N=25), and (4) other/non-specific defects (N=124). Automatic ONH tissue segmentation and digital volume correlation were used to compute IOP-induced neural tissue and lamina cribrosa (LC) strains. Biomechanical and structural features were input to a Geometric Deep Learning model. Three classification tasks were performed to detect: (1) superior nasal step, (2) superior partial arcuate, (3) full superior hemifield defect. For each task, the data were split into 80% training and 20% testing sets. Area under the curve (AUC) was used to assess performance. Explainable AI techniques were employed to highlight the ONH regions most critical to each classification. Results: Models achieved high AUCs of 0.77-0.88, showing that ONH strain improved VF loss prediction beyond morphology alone. The inferior and inferotemporal rim were identified as key strain-sensitive regions, contributing most to visual field loss prediction and showing progressive expansion with increasing disease severity. Conclusion and Relevance: ONH strain enhances prediction of glaucomatous VF loss patterns. Neuroretinal rim, rather than the LC, was the most critical region contributing to model predictions.

cs.LG↗

arXiv2025

Probabilistically Robust Uncertainty Analysis and Optimal Control of Continuous Lyophilization via Polynomial Chaos Theory

Lyophilization, aka freeze drying, is a process commonly used to increase the stability of various drug products in biotherapeutics manufacturing, e.g., mRNA vaccines, allowing for higher storage temperature. While the current trends in the industry are moving towards continuous manufacturing, the majority of industrial lyophilization processes are still being operated in a batch mode. This article presents a framework that accounts for the probabilistic uncertainty during the primary and secondary drying steps in continuous lyophilization. The probabilistic uncertainty is incorporated into the mechanistic model via polynomial chaos theory (PCT). The resulting PCT-based model is able to accurately and efficiently quantify the effects of uncertainty on several critical process variables, including the temperature, sublimation front, and concentration of bound water. The integration of the PCT-based model into stochastic optimization and control is demonstrated. The proposed framework and case studies can be used to guide the design and control of continuous lyophilization while accounting for probabilistic uncertainty.

cs.CE↗

arXiv2024

xSCYTE: Express Single-frame Cytometer through Tomographic Phase

Rapid, comprehensive, and accurate cell phenotyping without compromising viability, is crucial to many important biomedical applications, including stem-cell therapy, drug screening, and liquid biopsy. Typical image cytometry methods acquire two-dimensional (2D) fluorescence images, where the fluorescence labelling process may damage living cells, and the information from 2D images is not comprehensive enough for precise cell analysis. Although three-dimensional (3D) label-free image cytometry holds great promise, its high throughput development faces several technical challenges. Here, we report eXpress Single-frame CYtometer through Tomographic phasE (xSCYTE), which reconstructs 3D Refractive Index (RI) maps of cells with diffraction-limited resolution. With these high-speed and high-precision imaging capabilities empowered by artificial intelligence, we envision xSCYTE may open up many new avenues of biomedical investigations and industries, such as multi-omic assays and quality control during cellular therapeutic manufacturing.

physics.optics↗

arXiv2024

Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photonic computation at speeds up to 1.36 TOPS while consuming 0.057 pJ/OP. Our system features more than 100 thin-film lithium niobate high-performance components working synergistically, surpassing state-of-the-art systems on this platform. We further demonstrate binary-classification, handwritten-digit classification, and image classification with remarkable accuracy, showcasing our system's capability of executing real algorithms. Finally, we investigate the opportunities offered by combining our system with a hybrid-integrated distributed feedback laser source and a heterogeneous-integrated modified uni-traveling carrier photodiode. Our results illustrate the promise of thin-film lithium niobate as a computational platform, addressing current bottlenecks in both electronic and photonic computation. Its unique properties of high-performance electro-optic weight encoding and conversion, wafer-scale scalability, and compatibility with integrated lasers and detectors, position thin-film lithium niobate photonics as a valuable complement to silicon photonics, with extensions to applications in ultrafast and power-efficient signal processing and ranging.

physics.optics↗

arXiv2024

Real-time Estimation of Bound Water Concentration during Lyophilization with Temperature-based State Observers

Lyophilization (aka freeze drying) has been shown to provide long-term stability for many crucial biotherapeutics, e.g., mRNA vaccines for COVID-19, allowing for higher storage temperature. The final stage of lyophilization, namely secondary drying, entails bound water removal via desorption, in which accurate prediction of bound water concentration is vital to ensuring the quality of the lyophilized product. This article proposes a novel technique for real-time estimation of the residual moisture during secondary drying in lyophilization. A state observer is employed, which combines temperature measurement and mechanistic understanding of heat transfer and desorption kinetics, without requiring any online concentration measurement. Results from both simulations and experimental data show that the observer can accurately estimate the concentration of bound water in real time for all possible concentration levels, operating conditions, and measurement noise. This framework can also be applied for monitoring and control of the residual moisture in other desorption-related processes.

eess.SY↗

arXiv2024

Mechanistic Modeling and Analysis of Thermal Radiation in Conventional, Microwave-assisted, and Hybrid Freeze Drying for Biopharmaceutical Manufacturing

In freeze drying, thermal radiation has a significant effect on the drying process of vials located near the corner and edge of the trays, resulting in non-uniformity of the products. Understanding and being able to predict the impact of thermal radiation are therefore critical to accurate determination of the drying process endpoint given the variation in heat transfer of each vial. This article presents a new mechanistic model that describes complex thermal radiation during primary drying in conventional, microwave-assisted, and hybrid freeze drying. Modeling of thermal radiation employs the diffuse gray surface model and radiation network approach, which systematically and accurately incorporates simultaneous radiation exchange between every surface including the chamber wall and vials, allowing the framework to be seamlessly applied for analyzing various freeze-dryer designs. Model validation with data from the literature shows accurate prediction of the drying times for all vials, including inner, edge, and corner vials. The validated model is demonstrated for thermal radiation analysis and parametric studies to guide the design and optimization of freeze dryers.

cs.CE↗

arXiv2024

Sensitivity fields and parameter estimation from dielectric objects

The quantitative phase image formation process is posed as a problem of parameter estimation from intensity measurements. This approach is inclusive of traditional pixel-oriented imaging, where the sought parameters are the pixel values. The resulting optimization process to find the parameters is then seen to depend on the Sensitivity Field: this is the gradient of the scattered field with respect to the parameters, and it turns out to obey a scattering relationship that is analogous to that of the original scattered field. Examples are given from several regimes of scattering strength.

physics.class-ph↗

arXiv2024

Introducing the Biomechanics-Function Relationship in Glaucoma: Improved Visual Field Loss Predictions from intraocular pressure-induced Neural Tissue Strains

Objective. (1) To assess whether neural tissue structure and biomechanics could predict functional loss in glaucoma; (2) To evaluate the importance of biomechanics in making such predictions. Design, Setting and Participants. We recruited 238 glaucoma subjects. For one eye of each subject, we imaged the optic nerve head (ONH) using spectral-domain OCT under the following conditions: (1) primary gaze and (2) primary gaze with acute IOP elevation. Main Outcomes: We utilized automatic segmentation of optic nerve head (ONH) tissues and digital volume correlation (DVC) analysis to compute intraocular pressure (IOP)-induced neural tissue strains. A robust geometric deep learning approach, known as Point-Net, was employed to predict the full Humphrey 24-2 pattern standard deviation (PSD) maps from ONH structural and biomechanical information. For each point in each PSD map, we predicted whether it exhibited no defect or a PSD value of less than 5%. Predictive performance was evaluated using 5-fold cross-validation and the F1-score. We compared the model's performance with and without the inclusion of IOP-induced strains to assess the impact of biomechanics on prediction accuracy. Results: Integrating biomechanical (IOP-induced neural tissue strains) and structural (tissue morphology and neural tissues thickness) information yielded a significantly better predictive model (F1-score: 0.76+-0.02) across validation subjects, as opposed to relying only on structural information, which resulted in a significantly lower F1-score of 0.71+-0.02 (p < 0.05). Conclusion: Our study has shown that the integration of biomechanical data can significantly improve the accuracy of visual field loss predictions. This highlights the importance of the biomechanics-function relationship in glaucoma, and suggests that biomechanics may serve as a crucial indicator for the development and progression of glaucoma.

eess.IV↗

arXiv2023

Automated Translation and Accelerated Solving of Differential Equations on Multiple GPU Platforms

We demonstrate a high-performance vendor-agnostic method for massively parallel solving of ensembles of ordinary differential equations (ODEs) and stochastic differential equations (SDEs) on GPUs. The method is integrated with a widely used differential equation solver library in a high-level language (Julia's DifferentialEquations.jl) and enables GPU acceleration without requiring code changes by the user. Our approach achieves state-of-the-art performance compared to hand-optimized CUDA-C++ kernels while performing 20--100$\times$ faster than the vectorizing map (vmap) approach implemented in JAX and PyTorch. Performance evaluation on NVIDIA, AMD, Intel, and Apple GPUs demonstrates performance portability and vendor-agnosticism. We show composability with MPI to enable distributed multi-GPU workflows. The implemented solvers are fully featured -- supporting event handling, automatic differentiation, and incorporation of datasets via the GPU's texture memory -- allowing scientists to take advantage of GPU acceleration on all major current architectures without changing their model code and without loss of performance. We distribute the software as an open-source library https://github.com/SciML/DiffEqGPU.jl

cs.DC↗

arXiv2023

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

physics.optics↗

arXiv2023

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen; and rotation of the specimen to acquire multiple projections covering the 3D spatial frequency domain. Two types of rotational scanning are possible: tomographic and laminographic. For flat, extended samples, for which the full 180 degree coverage is not possible, the latter is preferable because it provides better coverage of the 3D spatial frequency domain compared to limited-angle tomography. It is also because the amount of attenuation through the sample is approximately the same for all projections. However, both techniques are time consuming because of extensive acquisition and computation time. Here, we demonstrate the acceleration of ptycho-laminographic reconstruction of integrated circuits with 16-times fewer angular samples and 4.67-times faster computation by using a physics-regularized deep self-supervised learning architecture. We check the fidelity of our reconstruction against a densely sampled reconstruction that uses full scanning and no learning. As already reported elsewhere [Zhou and Horstmeyer, Opt. Express, 28(9), pp. 12872-12896], we observe improvement of reconstruction quality even over the densely sampled reconstruction, due to the ability of the self-supervised learning kernel to fill the missing cone.

eess.IV↗

arXiv2023

Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

eess.IV↗

arXiv2023

The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage

$\bf{Purpose}$: To describe the 3D structural changes in both connective and neural tissues of the optic nerve head (ONH) that occur concurrently at different stages of glaucoma using traditional and AI-driven approaches. $\bf{Methods}$: We included 213 normal, 204 mild glaucoma (mean deviation [MD] $\ge$ -6.00 dB), 118 moderate glaucoma (MD of -6.01 to -12.00 dB), and 118 advanced glaucoma patients (MD < -12.00 dB). All subjects had their ONHs imaged in 3D with Spectralis optical coherence tomography. To describe the 3D structural phenotype of glaucoma as a function of severity, we used two different approaches: (1) We extracted human-defined 3D structural parameters of the ONH including retinal nerve fiber layer (RNFL) thickness, lamina cribrosa (LC) shape and depth at different stages of glaucoma; (2) we also employed a geometric deep learning method (i.e. PointNet) to identify the most important 3D structural features that differentiate ONHs from different glaucoma severity groups without any human input. $\bf{Results}$: We observed that the majority of ONH structural changes occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using PointNet, we also found that 3D ONH structural changes were present in both neural and connective tissues. In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC. As the severity of glaucoma increased, these changes became more diffuse (i.e. widespread), particularly in the LC. $\bf{Conclusions}$: In this study, we were able to uncover complex 3D structural changes of the ONH in both neural and connective tissues as a function of glaucoma severity. We hope to provide new insights into the complex pathophysiology of glaucoma that might help clinicians in their daily clinical care.

cs.LG↗

arXiv2022

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Noninvasive X-ray imaging of nanoscale three-dimensional objects, e.g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of complex electromagnetic field through ICs; and tomographic scanning, which collects complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach trained to provide accurate reconstructions of ICs despite incomplete measurements, using a dramatically reduced amount of angular scanning. Training process includes regularizing priors based on typical IC patterns and the physics of X-ray propagation. We demonstrate that APT with 12-time reduced angles achieves fidelity comparable to the gold standard with the original set of angles. With the same set of reduced angles, APT also outperforms baseline reconstruction methods. In our experiments, APT achieves 108-time aggregate reduction in data acquisition and computation without compromising quality. We expect our physics-assisted machine learning framework could also be applied to other branches of nanoscale imaging.

eess.IV↗

arXiv2022

Noise-resilient approach for deep tomographic imaging

We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.

eess.IV↗