arXiv subjects

Wolfgang Drexler

Publications and source records attributed to Wolfgang Drexler.

13 recordsLinked to original sources

arXiv2026

Quantitative Multi-Modal Optical Coherence Photoacoustic Elastography

We present a novel multi-modal optical coherence photoacoustic elastography framework, which combines two imaging modalities, optical coherence tomography and photoacoustic tomography, to enable complementary absorption-scattering measurements for the extraction of quantitative tissue features via quasi-static elastography. For this, we develop a sophisticated hybrid inversion algorithm for merging the complementary information layers contained in both optical coherence and photoacoustic-based elastography measurements, and perform systematic evaluations to assess the impact of hybrid elastography data on strain and stiffness reconstructions. An extension to a photoacoustic tomography and optical coherence tomography imaging system allows precise elastographic experiments. Studies on a silicone elastomer phantom demonstrate that the combined optical coherence - photoacoustic approach outperforms single-modality optical coherence elastography and photoacoustic elastography, yielding higher strain signal-to-noise ratios and improved stiffness estimates. These results establish the advantage of multi-modal complementary imaging and data merging for accurate, high-resolution elastographic strain and stiffness mapping in both scattering and absorbing materials.

physics.med-ph↗

arXiv2026

Bridging the gap: Using deep learning to reconstruct noise-reduced super-resolved OCT images from gapped spectra

Fourier-domain (FD) optical coherence tomography (OCT) depends on broadband sources to maximize axial resolution and image quality. However, these lasers significantly drive device cost or may be unavailable at desired wavelength and bandwidth ranges. A potential solution lies in integrating multiple, more affordable sources with lower individual bandwidth into a single system. However, difficulties arise if the resulting spectrum exhibits discontinuities. In this letter, we present a method that can combine OCT images from a flexible number of spectra with arbitrary, possibly non-overlapping gaps using a neural network. Compared to low-resolution input images, reconstructed B-scans are super-resolved, preserve even fine and low-contrast details and edges, and exhibit strong noise reduction that increases with the band gap. The proposed method could thereby provide a major step towards high-quality OCT imaging using spectrally disjoint, low-bandwidth sources. In broadband settings, it can be directly applied as a Fourier-domain masked autoencoder for self-supervised image quality enhancement.

physics.optics↗

arXiv2023

Quantitative Parameter Reconstruction from Optical Coherence Tomographic Data

Quantitative tissue information, like the light scattering properties, is considered as a key player in the detection of cancerous cells in medical diagnosis. A promising method to obtain these data is optical coherence tomography (OCT). In this article, we will therefore discuss the refractive index reconstruction from OCT data, employing a Gaussian beam based forward model. We consider in particular samples with a layered structure, meaning that the refractive index as a function of depth is well approximated by a piece-wise constant function. For the reconstruction, we present a layer-by-layer method where in every step the refractive index is obtained via a discretized least squares minimization. For an approximated form of the minimization problem, we present an existence and uniqueness result. The applicability of the proposed method is then verified by reconstructing refractive indices of layered media from both simulated and experimental OCT data.

math.NA↗

arXiv2023

Multimodal imaging of the mouse eye using visible light photoacoustic ophthalmoscopy and near-infrared-II optical coherence tomography

Non-invasive imaging plays a crucial role in diagnosing and studying eye diseases. However, existing photoacoustic ophthalmoscopy (PAOM) techniques in mice have limitations due to handling restrictions, suboptimal optical properties, limited availability of light sources and permissible light fluence at the retina. This study introduces an innovative approach that utilizes Rose Bengal, a contrast agent, to enhance PAOM contrast. This enables visualization of deeper structures like the choroidal microvasculature and sclera in the mouse eye using visible light. The integration of near-infrared-II optical coherence tomography (NIR-II OCT) provides additional tissue contrast and insights into potential NIR-II PAOM capabilities. To optimize imaging, we developed a cost-effective 3D printable mouse eye phantom and a fully 3D printable tip/tilt mouse platform. This solution elevates PAOM to a user-friendly technology, which can be used to address pressing research questions concerning several ocular diseases such as myopia, glaucoma and/or age-related macular degeneration in the future.

physics.med-ph↗

arXiv2022

Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images

The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in optical coherence tomography en face images of the retina. We examine the effect of domain knowledge driven spatial complexity reduction on the resulting optic nerve head segmentation and localization performance. We present a machine learning approach for segmenting optic nerve head in 2D en face projections of 3D widefield swept source optical coherence tomography scans that enables the automated assessment of large amounts of data. Evaluation on manually annotated 2D en face images of the retina demonstrates that training of a standard U-Net can yield improved optic nerve head segmentation and localization performance when the underlying pixel-level binary classification task is spatially relaxed through domain knowledge.

eess.IV↗

arXiv2022

Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers

Kernel methods are the basis of most classical machine learning algorithms such as Gaussian Process (GP) and Support Vector Machine (SVM). Computing kernels using noisy intermediate scale quantum (NISQ) devices has attracted considerable attention due to recent progress in the design of NISQ devices. However noise and errors on current NISQ devices can negatively affect the predicted kernels. In this paper we utilize two quantum kernel machine learning (ML) algorithms to predict the labels of a Breast Cancer dataset on two different NISQ devices: quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM). We estimate the quantum kernels on the 11 qubit IonQ and the 5 qubit IBMQ Belem quantum devices. Our results demonstrate that the predictive performances of the error mitigated quantum kernel machine learning algorithms improve significantly compared to their non-error mitigated counterparts. On both NISQ devices the predictive performances became comparable to those of noiseless quantum simulators and their classical counterparts

quant-ph↗

arXiv2021

A Quantitative Model for Optical Coherence Tomography

Optical coherence tomography (OCT) is a widely used imaging technique in the micrometer regime, which gained accelerating interest in medical imaging in the last twenty years. In up-to-date OCT literature [5,6] certain simplifying assumptions are made for the reconstructions, but for many applications a more realistic description of the OCT imaging process is of interest. In mathematical models, for example, the incident angle of light onto the sample is usually neglected or a plane wave description for the light-sample interaction in OCT is used, which ignores almost completely the occurring effects within an OCT measurement process. In this article, we make a first step to a quantitative model by considering the measured intensity as a combination of back-scattered Gaussian beams affected by the system. In contrast to the standard plane wave simplification, the presented model includes system relevant parameters such as the position of the focus and the spot size of the incident laser beam, which allow a precise prediction of the OCT data and therefore ultimately serves as a forward model. The accuracy of the proposed model - after calibration of all necessary system parameters - is illustrated by simulations and validated by a comparison with experimental data obtained from a 1300 nm swept-source OCT system.

physics.med-ph↗

arXiv2021

Adaptive optics imaging with a pyramid wavefront sensor for visual science

The pyramid wavefront sensor (P-WFS) has replaced the Shack-Hartmann (SH-) WFS as sensor of choice for high performance adaptive optics (AO) systems in astronomy because of its flexibility in pupil sampling, its dynamic range, and its improved sensitivity in closed-loop application. Usually, a P-WFS requires modulation and high precision optics that lead to high complexity and costs of the sensor. These factors limit the competitiveness of the P-WFS with respect to other WFS devices for AO correction in visual science. Here, we present a cost effective realization of AO correction with a non-modulated PWFS and apply this technique to human retinal in vivo imaging using optical coherence tomography (OCT). P-WFS based high quality AO imaging was, to the best of our knowledge for the first time, successfully performed in 5 healthy subjects and benchmarked against the performance of conventional SH-WFS based AO. Smallest retinal cells such as central foveal cone photoreceptors are visualized and we observed a better quality of the images recorded with the P-WFS. The robustness and versatility of the sensor is demonstrated in the model eye under various conditions and in vivo by high-resolution imaging of other structures in the retina using standard and extended fields of view.

physics.med-ph↗

arXiv2021

Challenges for Optical Flow Estimates in Elastography

In this paper, we consider visualization of displacement fields via optical flow methods in elastographic experiments consisting of a static compression of a sample. We propose an elastographic optical flow method (EOFM) which takes into account experimental constraints, such as appropriate boundary conditions, the use of speckle information, as well as the inclusion of structural information derived from knowledge of the background material. We present numerical results based on both simulated and experimental data from an elastography experiment in order to demonstrate the relevance of our proposed approach.

math.NA↗

arXiv2020

Multi-channel swept source optical coherence tomography concept based on photonic integrated circuits

In this paper, we present a novel concept for a multi-channel swept source optical coherence tomography (OCT) system based on photonic integrated circuits (PICs). At the core of this concept is a low-loss polarization dependent path routing approach allowing for lower excess loss compared to previously shown PIC-based OCT systems, facilitating a parallelization of measurement units. As a proof of concept for the low-loss path routing, a silicon nitride PIC-based single-channel swept source OCT system operating at 840 nm was implemented and used to acquire in-vivo tomograms of a human retina. The fabrication of the PIC was done via CMOS-compatible plasma-enhanced chemical vapor deposition to allow future monolithic co-integration with photodiodes and read-out electronics. A performance analysis using the results of the implemented photonic building blocks shows a potential tenfold increase of the acquisition speed for a multi-channel system compared to an ideal lossless single-channel system with the same signal-to-noise ratio.

physics.app-ph↗

arXiv2020

Displacement field estimation from OCT images utilizing speckle information with applications in quantitative elastography

In this paper, we consider the problem of estimating the internal displacement field of an object which is being subjected to a deformation, from Optical Coherence Tomography (OCT) images before and after compression. For the estimation of the internal displacement field we propose a novel algorithm, which utilizes particular speckle information to enhance the quality of the motion estimation. We present numerical results based on both simulated and experimental data in order to demonstrate the usefulness of our approach, in particular when applied for quantitative elastography, when the material parameters are estimated in a second step based on the internal displacement field.

math.NA↗

arXiv2015

Non-Equispaced Grid Sampling in Photoacoustics with a Non-Uniform FFT

To obtain the initial pressure from the collected data on a planar sensor arrangement in photoacoustic tomography, there exists an exact analytic frequency domain reconstruction formula. An efficient realization of this formula needs to cope with the evaluation of the data's Fourier transform on a non-equispaced mesh. In this paper, we use the non-uniform fast Fourier transform to handle this issue and show its feasibility in 3D experiments with real and synthetic data. This is done in comparison to the standard approach that uses linear, polynomial or nearest neighbor interpolation. Moreover, we investigate the effect and the utility of flexible sensor location to make optimal use of a limited number of sensor points. The computational realization is accomplished by the use of a multi-dimensional non-uniform fast Fourier algorithm, where non-uniform data sampling is performed both in frequency and spatial domain. Examples with synthetic and real data show that both approaches improve image quality.

physics.med-ph↗

arXiv2015

Computational Realization of a Non-Equidistant Grid Sampling in Photoacoustics with a Non-Uniform FFT

To obtain the initial pressure from the collected data on a planar sensor arrangement in Photoacoustic tomography, there exists an exact analytic frequency domain reconstruction formula. An efficient realization of this formula needs to cope with the evaluation of the datas Fourier transform on a non-equispaced mesh. In this paper, we use the non-uniform fast Fourier transform to handle this issue and show its feasibility in 3D experiments. This is done in comparison to the standard approach that uses polynomial interpolation. Moreover, we investigate the effect and the utility of flexible sensor location on the quality of photoacoustic image reconstruction. The computational realization is accomplished by the use of a multi-dimensional non-uniform fast Fourier algorithm, where non-uniform data sampling is performed both in frequency and spatial domain. We show that with appropriate sampling the imaging quality can be significantly improved. Reconstructions with synthetic and real data show the superiority of this method.

math.NA↗