arXiv subjects

Vasilis Ntziachristos

Publications and source records attributed to Vasilis Ntziachristos.

18 recordsLinked to original sources

arXiv2026

Conditioning Deep Anatomical Prior Knowledge for Reconstruction of Multispectral Optoacoustic Tomography Images

Accurately delineating tissues and reconstructing their chromophore compositions from Multispectral Optoacoustic Tomography (MSOT) images is a key challenge in optoacoustic imaging. The difficulty arises because light fluence distributions within tissue intrinsically depend on spectral optical properties, making the inverse problem inherently ill-posed. Currently, there is a lack of studies leveraging a priori probabilistic anatomical knowledge to guide tissue segmentation and infer chromophore composition. Moreover, most current studies address these two tasks sequentially, which can result in errors accumulating. through the process. To address these issues, we present Anatomical Priors for Reconstruction of Optoacoustic Tomography (APRECOT), a method that leverages probabilistic models of anatomical structures and tissue properties, to enable simultaneous segmentation of tissues and reconstruction of their bulk chromophore compositions. In this proof-of-concept using in-silico data, we show that incorporating probabilistic anatomical context strongly improves the accuracy of bulk chromophore concentration estimation compared to reference methods that do not use any anatomical context or use sequential strategies. This work represents an essential step towards an MSOT imaging mode that directly provides clinically relevant information, such as imaging tissue oxygenation dynamics or disease-related changes in tissue composition.

eess.IV↗

arXiv2026

DeepLight: A Sobolev-trained Image-to-Image Surrogate Model for Light Transport in Tissue

In optoacoustic imaging, recovering the absorption coefficients of tissue by inverting the light transport remains a challenging problem. Improvements in solving this problem can greatly benefit the clinical value of optoacoustic imaging. Existing variational inversion methods require an accurate and differentiable model of this light transport. As neural surrogate models allow fast and differentiable simulations of complex physical processes, they are considered promising candidates to be used in solving such inverse problems. However, there are in general no guarantees that the derivatives of these surrogate models accurately match those of the underlying physical operator. As accurate derivatives are central to solving inverse problems, errors in the model derivative can considerably hinder high fidelity reconstructions. To overcome this limitation, we present a surrogate model for light transport in tissue that uses Sobolev training to improve the accuracy of the model derivatives. Additionally, the form of Sobolev training we used is suitable for high-dimensional models in general. Our results demonstrate that Sobolev training for a light transport surrogate model not only improves derivative accuracy but also reduces generalization error for in-distribution and out-of-distribution samples. These improvements promise to considerably enhance the utility of the surrogate model in downstream tasks, especially in solving inverse problems.

cs.LG↗

arXiv2024

Bayesian reconstruction of sparse raster-scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy

Hyperspectral optoacoustic microscopy (OAM) enables obtaining images with label-free biomolecular contrast, offering excellent perspectives as a diagnostic tool to assess freshly excised and unprocessed tissues. However, time-consuming raster-scanning image formation currently limits the translation potential of OAM into the clinical setting-for instance, in intraoperative histopathological assessments-where micrographs of excised tissue need to be taken within a few minutes for fast clinical decision-making. Here, we present a non-data-driven computational framework tailored to enable fast OAM by sparse data acquisition and model-based image reconstruction, termed Bayesian raster-computed optoacoustic microscopy (BayROM). Unlike conventional machine learning, BayROM doesn't require training datasets, but instead, it employs 1) optomechanical system properties to define a forward model and 2) prior knowledge of the imaged samples to facilitate reconstructing images based on the sparsely acquired data. We show that BayROM enables acquiring micrographs ten times faster and with structural similarity (SSIM) indices greater than 0.93 compared to conventional raster scanning microscopy, thus facilitating the clinical translation of OAM for fast, label-free intraoperative histopathology.

eess.IV↗

arXiv2024

A Method for Accurate Spatial Focusing Simulation via Numerical Integration and its Application in Optoacoustic Tomography

The spatial sensitivity of an ultrasound transducer, which strongly influences its suitability for different applications, depends on the shape of the transducer surface. Accurate simulation of these spatial effects is important for transducer characterization and design, and for system response modelling in imaging applications. In optoacoustic imaging, broadband transducers are used to capitalize on the rich frequency content of the signals, but their usage makes highly accurate simulations with general wave equation solvers prohibitively memory- and time-intensive. Therefore, specialized tools for simulating the isolated spatial focusing properties described by the spatial impulse response (SIR) have been developed. However, the challenging numerics of the SIR and the necessity to convolve the SIR with the wave shape generated by the optoacoustic absorber to simulate the system response lead to numerical inaccuracies of SIR-based methods. In addition, the approximation error of these methods cannot be controlled a priori. To circumvent the problems associated with the explicit calculation of SIR, we propose directly computing the convolution of the required wave shape with the SIR, which we call the spatial pulse response (SPR). We demonstrate that by utilizing an h-adaptive cubature algorithm, SPR can be computed with significantly higher accuracy than an SIR-based reference method, and the approximation error can be controlled with a tolerance parameter. In addition, the integration of accurate SPR simulations into model-based optoacoustic image reconstruction is shown to improve image contrast and reduce noise artifacts. Precise system characterization and simulation leads to improved imaging performance, ultimately increasing the value of optoacoustic imaging systems for clinical applications.

physics.med-ph↗

arXiv2023

DeepMB: Deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound

Multispectral optoacoustic tomography (MSOT) is a high-resolution functional imaging modality that can non-invasively access a broad range of pathophysiological phenomena by quantifying the contrast of endogenous chromophores in tissue. Real-time imaging is imperative to translate MSOT into clinical imaging, visualize dynamic pathophysiological changes associated with disease progression, and enable in situ diagnoses. Model-based reconstruction affords state-of-the-art optoacoustic images; however, the image quality provided by model-based reconstruction remains inaccessible during real-time imaging because the algorithm is iterative and computationally demanding. Deep learning affords faster reconstruction, but the lack of ground truth training data can lead to reduced image quality for in vivo data. We introduce a framework, termed DeepMB, that achieves accurate optoacoustic image reconstruction for arbitrary input data in 31 ms per image by expressing model-based reconstruction with a deep neural network. DeepMB facilitates accurate generalization to experimental test data through training on signals synthesized from real-world images and ground truth images generated by model-based reconstruction. The framework affords in-focus images for a broad range of anatomical locations because it supports dynamic adjustment of the reconstruction speed of sound during imaging. Furthermore, DeepMB is compatible with the data rates and image sizes of modern multispectral optoacoustic tomography scanners. We evaluate DeepMB on a diverse dataset of in vivo images and demonstrate that the framework reconstructs images 1000 times faster than the iterative model-based reference method while affording near-identical image qualities. Accurate and real-time image reconstructions with DeepMB can enable full access to the high-resolution and multispectral contrast of handheld optoacoustic tomography.

eess.IV↗

arXiv2022

Spotlight on nerves: Portable multispectral optoacoustic imaging of peripheral nerve vascularization and morphology

Various morphological and functional parameters of peripheral nerves and their vascular supply are indicative of pathological changes due to injury or disease. Based on recent improvements in optoacoustic image quality, we explore the ability of multispectral optoacoustic tomography, in tandem with ultrasound imaging (OPUS), to investigate the vascular environment and morphology of peripheral nerves in vivo in a pilot study on healthy volunteers. We showcase the unique ability of optoacoustic imaging to visualize the vasa nervorum by observing intraneurial vessels in healthy nerves in vivo for the first time. In addition, we demonstrate that the label-free spectral optoacoustic contrast of the perfused connective tissue of peripheral nerves can be linked to the endogenous contrast of haemoglobin and collagen. We introduce metrics to analyze the composition of tissue based on its optoacoustic contrast and show that the high-resolution spectral contrast reveals specific differences between nervous tissue and reference tissue in the nerve's surrounding. We discuss how this showcased extraction of peripheral nerve characteristics using multispectral optoacoustic and ultrasound imaging can offer new insights into the pathophysiology of nerve damage and neuropathies, for example, in the context of diabetes.

eess.IV↗

arXiv2022

Enabling the autofocus approach for parameter optimization in planar measurement geometry clinical optoacoustic imaging

In optoacoustic (photoacoustic) tomography, several parameters related to tissue and detector features are needed for image formation, but they may not be known a priori. An autofocus (AF) algorithm is generally used to estimate these parameters. However, the algorithm works iteratively, therefore, it is impractical for clinical imaging with systems featuring planar geometry due to long reconstruction times. We have developed a fast autofocus (FAF) algorithm for optoacoustic systems with planar geometry that is much simpler computationally than the conventional AF algorithm. We show that the FAF algorithm required about 5 sec. to provide accurate estimates of the speed of sound in simulated data and experimental data obtained using an imaging system that is poised to enter the clinic. The applicability of FAF for estimating other image formation parameters is discussed. We expect the FAF algorithm to contribute decisively to the clinical use of optoacoustic tomography systems with planar geometry.

physics.med-ph↗

arXiv2021

Self-Supervised Learning from Unlabeled Fundus Photographs Improves Segmentation of the Retina

Fundus photography is the primary method for retinal imaging and essential for diabetic retinopathy prevention. Automated segmentation of fundus photographs would improve the quality, capacity, and cost-effectiveness of eye care screening programs. However, current segmentation methods are not robust towards the diversity in imaging conditions and pathologies typical for real-world clinical applications. To overcome these limitations, we utilized contrastive self-supervised learning to exploit the large variety of unlabeled fundus images in the publicly available EyePACS dataset. We pre-trained an encoder of a U-Net, which we later fine-tuned on several retinal vessel and lesion segmentation datasets. We demonstrate for the first time that by using contrastive self-supervised learning, the pre-trained network can recognize blood vessels, optic disc, fovea, and various lesions without being provided any labels. Furthermore, when fine-tuned on a downstream blood vessel segmentation task, such pre-trained networks achieve state-of-the-art performance on images from different datasets. Additionally, the pre-training also leads to shorter training times and an improved few-shot performance on both blood vessel and lesion segmentation tasks. Altogether, our results showcase the benefits of contrastive self-supervised pre-training which can play a crucial role in real-world clinical applications requiring robust models able to adapt to new devices with only a few annotated samples.

eess.IV↗

arXiv2021

Disentangling the frequency content in optoacoustics

Signals acquired by optoacoustic tomography systems have broadband frequency content that encodes information about structures on different physical scales. Concurrent processing and rendering of such broadband signals may result in images with poor contrast and fidelity due to a bias towards low frequency contributions from larger structures. This problem cannot be addressed by filtering different frequency bands and reconstructing them individually, as this procedure leads to artefacts due to its incompatibility with the entangled frequency content of signals generated by structures of different sizes. Here we introduce frequency-band model-based (fbMB) reconstruction to separate frequency-band-specific optoacoustic image components during image formation, thereby enabling structures of all sizes to be rendered with high fidelity. In order to disentangle the overlapping frequency content of image components, fbMB uses soft priors to achieve an optimal trade-off between localization of the components in frequency bands and their structural integrity. We demonstrate that fbMB produces optoacoustic images with improved contrast and fidelity, which reveal anatomical structures in in vivo images of mice in unprecedented detail. These enhancements further improve the accuracy of spectral unmixing in small vasculature. By offering a precise treatment of the frequency components of optoacoustic signals, fbMB improves the quality, accuracy, and quantification of optoacoustic images and provides a method of choice for optoacoustic reconstructions.

eess.IV↗

arXiv2021

Elongated focus optoacoustic microscopy with matched Bessel beam illumination and ultra-broadband axicon detection

Current optoacoustic microscopy configurations often have narrow focal ranges that limit their use for fast volumetric imaging applications. Here, the focal range of optoacoustic microscopes is extended by matching the elongated optical illumination profile of a Bessel beam with the pencil beam acoustic sensitivity profile of a broadband axicon detector. An inverted optoacoustic microscope was developed with interchangeable optical illumination and acoustic detection units to assess the imaging depths and resolutions retained with several combinations of illumination and detection profiles. Matching Bessel illumination with axicon detection extends the depth-of-focus 17-fold over traditional configurations. Imaging a tilted mouse ear with the matched Bessel-axicon configuration revealed vasculature over an imaging depth exceeding 4.2 mm with optical resolution, while affording a 6-fold increase in imaging volume over the same scanning duration compared to configurations employing standard Gaussian illumination, demonstrating this approach's promise for increasing applications for optoacoustic microscopy in preclinical research.

physics.optics↗

arXiv2021

Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue

Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture complex characteristics of signal and noise. Moreover, they often involve time-consuming processing steps that are unsuited for real-time imaging applications. In this work, we develop and demonstrate a discriminative deep learning (DL) approach to separate electrical noise from optoacoustic signals prior to image reconstruction. The proposed DL algorithm is based on two key features. First, it learns spatiotemporal correlations in both noise and signal by using the entire optoacoustic sinogram as input. Second, it employs training based on a large dataset of experimentally acquired pure noise and synthetic optoacoustic signals. We validated the ability of the trained model to accurately remove electrical noise on synthetic data and on optoacoustic images of a phantom and the human breast. We demonstrate significant enhancements of morphological and spectral optoacoustic images reaching 19% higher blood vessel contrast and localized spectral contrast at depths of more than 2 cm for images acquired in vivo. We discuss how the proposed denoising framework is applicable to clinical multispectral optoacoustic tomography and suitable for real-time operation.

eess.IV↗

arXiv2020

A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images

Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis. However, both the manual and the automated analysis of such images are challenging, because the RSOM images have very low contrast, poor signal to noise ratio, and systematic overlaps between the absorption spectra of melanin and hemoglobin. Nonetheless, the segmentation of the epidermis layer is a crucial step for many downstream medical and diagnostic tasks, such as vessel segmentation or monitoring of cancer progression. We propose a novel, shape-specific loss function that overcomes discontinuous segmentations and achieves smooth segmentation surfaces while preserving the same volumetric Dice and IoU. Further, we validate our epidermis segmentation through the sensitivity of vessel segmentation. We found a 20 $\%$ improvement in Dice for vessel segmentation tasks when the epidermis mask is provided as additional information to the vessel segmentation network.

eess.IV↗

arXiv2020

Optoacoustic flow-cytometry with light scattering referencing

In analogy to the development of fluorescent proteins, innovative tools for screening optoacoustic cell labels could lead to tailored protein labels for OA, imparting novel ways to visualize biological structure and function. Optoacoustic imaging emerges towards a highly promising modality for life sciences and medical practise with advantageous capabilities such as great accessible depth, and 3D studying of living tissue. The development of novel labels with molecular specificity could significantly enhance the optoacoustic contrast, specificity, and sensitivity and allow optoacoustic to interrogate tissues not amenable to the fluorescence method. We report on an optoacoustic flow cytometer (OAFC) prototype, developed for screening optoacoustic reporter genes. The cytometer concurrently records light scattering for referencing purposes. Since recording light scattering is completely independent from OA, we believe it to be a more reliable referencing method than e.g. fluorescence or ultrasound-backscatter. Precise characterization of our OAFC prototype showcases its ability to optoacoustically characterize objects in-flow that are in the size range of single cells. We apply the OAFC to distinguish individual E. coli cells based on optoacoustic properties of their expressed chromoproteins read in-flow using microfluidic arrangements and achieved precisions over 90%. We discuss how the light scattering referenced OAFC method offers a critical step towards routine measurement of optoacoustic properties of single-cells and could pave the way for identifying genetically encoded optoacoustic reporters, by transferring working concepts of the fluorescence field.

physics.bio-ph↗

arXiv2019

Sub-micron silicon-on-insulator resonator for ultrasound detection

Point-like broadband ultrasound detection can significantly increase the resolution of ultrasonography and optoacoustic (photoacoustic) imaging, yet current ultrasound detectors cannot be miniaturised sufficiently. Piezoelectric transducers lose sensitivity quadratically with size reduction, while optical micro-ring resonators and Fabry-Pérot etalons fail to adequately confine light at dimensions smaller than $\sim$ 50 microns. Micromachining methods have been used to generate arrays of capacitive and piezoelectric transducers, but at bandwidths of only a few MHz and dimensions not smaller than 70 microns. Here we use the widely available silicon-on-insulator (SOI) platform to develop the worlds smallest ultrasound detector with a sub-micron sensing area of 220 $\times$ 500 nanometers. The SOI-based optical resonator design can provide per-area sensitivity that is $10^{4}$-fold higher than for micro-ring resonators and $10^{8}$-fold higher than for piezoelectric detectors. We also demonstrate ultra-wide bandwidth reaching 230 MHz and conduct the first imaging based on an SOI ultrasound detector. The technology showcased is suitable for manufacturing ultra-dense detector arrays ($\geq125$ $detectors\backslash mm^{2}$), which have the potential to revolutionise ultrasonography and optoacoustic imaging.

physics.app-ph↗

arXiv2019

Maximum entropy based non-negative optoacoustic tomographic image reconstruction

Objective:Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of the work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging. Methods: We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior based fluence correction. Results: We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms and in-vivo samples. Conclusion: The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures. Significance: Our method can enable quantitative optoacoustic imaging, and has the potential to improve pre-clinical and translational imaging applications.

physics.med-ph↗

arXiv2015

Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues

Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation by tissue optical properties, an effect that causes spectral corruption. Predictions of the spectral variations of light fluence in tissue are challenging since the spatial distribution of optical properties in tissue cannot be resolved in high resolution or with high accuracy by current methods. Spectral corruption has fundamentally limited the quantification accuracy of optical and optoacoustic methods and impeded the long sought-after goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical but still unattainable target for the assessment of oxygenation in physiological processes and disease. We discover a new principle underlying light fluence in tissues, which describes the wavelength dependence of light fluence as an affine function of a few reference base spectra, independently of the specific distribution of tissue optical properties. This finding enables the introduction of a previously undocumented concept termed eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively account for wavelength dependent light attenuation without explicit knowledge of the tissue optical properties. We validate eMSOT in more than 2000 simulations and with phantom and animal measurements. We find that eMSOT can quantitatively image tissue sO2 reaching in many occasions a better than 10-fold improved accuracy over conventional spectral optoacoustic methods. Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor; revealing so far unattainable tissue physiology patterns. Last, we related eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2 images and tissue perfusion and hypoxia maps obtained by correlative histological analysis.

physics.med-ph↗

arXiv2015

Functional optoacoustic neuro-tomography (FONT) for whole-brain monitoring of calcium indicators

Non-invasive observation of spatiotemporal neural activity of large neural populations distributed over entire brains is a longstanding goal of neuroscience. We developed a real-time volumetric and multispectral optoacoustic tomography platform for imaging of neural activation deep in scattering brains. The system can record 100 volumetric frames per second across a 200mm3 field of view and spatial resolutions below 70um. Experiments performed in immobilized and freely swimming larvae and in adult zebrafish brains demonstrate, for the first time, the fundamental ability to optoacoustically track neural calcium dynamics in animals labeled with genetically encoded calcium indicator GCaMP5G, while overcoming the longstanding penetration barrier of optical imaging in scattering brains. The newly developed platform offers unprecedented capabilities for functional whole-brain observations of fast calcium dynamics; in combination with optoacoustics' well-established capacity in resolving vascular hemodynamics, it could open new vistas in the study of neural activity and neurovascular coupling in health and disease.

q-bio.NC↗

arXiv2014

High contrast imaging of reversibly switchable fluorescent proteins via temporally unmixed Multispectral Optoacoustic Tomography (tuMSOT)

Photocontrol of reversibly switchable fluorescent proteins (RSFPs) was used to program optoacoustic signal time courses that were temporally unmixed to increase the proteins contrast-to-noise-ratios (CNRs) in optoacoustic imaging. In this way, two variants of the RSFP Dronpa with very similar optoacoustic spectra could be readily discriminated in the presence of highly absorbing blood. Addition of temporal unmixing to multispectral optoacoustic tomography (tuMSOT) in conjunction with synthetic or genetically controlled photochromic contrast agents and customized photoswitching schedules can increase the performance of multiplexed and high contrast molecular optoacoustic imaging.

physics.optics↗