Raster-scan Optoacoustic Mesoscopy Research Articles

Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Quality control in clinical raster-scan optoacoustic mesoscopy

Optoacoustic (photoacoustic) mesoscopy bridges the gap between optoacoustic microscopy and macroscopy and enables high-resolution visualization deeper than optical microscopy. Nevertheless, as images may be affected by motion and noise, it is critical to develop methodologies that offer standardization and quality control to ensure that high-quality datasets are reproducibly obtained from patient scans. Such development is particularly important for ensuring reliability in applying machine learning methods or for reliably measuring disease biomarkers. We propose herein a quality control scheme to assess the quality of data collected. A reference scan of a suture phantom is performed to characterize the system noise level before each raster-scan optoacoustic mesoscopy (RSOM) measurement. Using the recorded RSOM data, we develop a method that estimates the amount of motion in the raw data. These motion metrics are employed to classify the quality of raw data collected and derive a quality assessment index (QASIN) for each raw measurement. Using simulations, we propose a selection criterion of images with sufficient QASIN, leading to the compilation of RSOM datasets with consistent quality. Using 160 RSOM measurements from healthy volunteers, we show that RSOM images that were selected using QASIN were of higher quality and fidelity compared to non-selected images. We discuss how this quality control scheme can enable the standardization of RSOM images for clinical and biomedical applications.

Dermal features derived from optoacoustic tomograms via machine learning correlate microangiopathy phenotypes with diabetes stage

Skin microangiopathy has been associated with diabetes. Here we show that skin-microangiopathy phenotypes in humans can be correlated with diabetes stage via morphophysiological cutaneous features extracted from raster-scan optoacoustic mesoscopy (RSOM) images of skin on the leg. We obtained 199 RSOM images from 115 participants (40 healthy and 75 with diabetes), and used machine learning to segment skin layers and microvasculature to identify clinically explainable features pertaining to different depths and scales of detail that provided the highest predictive power. Features in the dermal layer at the scale of detail of 0.1–1 mm (such as the number of junction-to-junction branches) were highly sensitive to diabetes stage. A ‘microangiopathy score’ compiling the 32 most-relevant features predicted the presence of diabetes with an area under the receiver operating characteristic curve of 0.84. The analysis of morphophysiological cutaneous features via RSOM may allow for the discovery of diabetes biomarkers in the skin and for the monitoring of diabetes status.

Opening a window to skin biomarkers for diabetes stage with optoacoustic mesoscopy

Being the largest and most accessible organ of the human body, the skin could offer a window to diabetes-related complications on the microvasculature. However, skin microvasculature is typically assessed by histological analysis, which is not suited for applications to large populations or longitudinal studies. We introduce ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) for precise, non-invasive assessment of diabetes-related changes in the dermal microvasculature and skin micro-anatomy, resolved with unprecedented sensitivity and detail without the need for contrast agents. Providing unique imaging contrast, we explored a possible role for RSOM as an investigational tool in diabetes healthcare and offer the first comprehensive study investigating the relationship between different diabetes complications and microvascular features in vivo. We applied RSOM to scan the pretibial area of 95 participants with diabetes mellitus and 48 age-matched volunteers without diabetes, grouped according to disease complications, and extracted six label-free optoacoustic biomarkers of human skin, including dermal microvasculature density and epidermal parameters, based on a novel image-processing pipeline. We then correlated these biomarkers to disease severity and found statistically significant effects on microvasculature parameters as a function of diabetes complications. We discuss how label-free RSOM biomarkers can lead to a quantitative assessment of the systemic effects of diabetes and its complications, complementing the qualitative assessment allowed by current clinical metrics, possibly leading to a precise scoring system that captures the gradual evolution of the disease.

Fast raster-scan optoacoustic mesoscopy enables assessment of human melanoma microvasculature in vivo

Melanoma is associated with angiogenesis and vascular changes that may extend through the entire skin depth. Three-dimensional imaging of vascular characteristics in skin lesions could therefore allow diagnostic insights not available by conventional visual inspection. Raster-scan optoacoustic mesoscopy (RSOM) images microvasculature through the entire skin depth with resolutions of tens of micrometers; however, current RSOM implementations are too slow to overcome the strong breathing motions on the upper torso where melanoma lesions commonly occur. To enable high-resolution imaging of melanoma vasculature in humans, we accelerate RSOM scanning using an illumination scheme that is coaxial with a high-sensitivity ultrasound detector path, yielding 15 s single-breath-hold scans that minimize motion artifacts. We apply this Fast RSOM to image 10 melanomas and 10 benign nevi in vivo, showing marked differences between malignant and benign lesions, supporting the possibility to use biomarkers extracted from RSOM imaging of vasculature for lesion characterization to improve diagnostics.

Optoacoustic imaging and potential applications of raster-scan optoacoustic mesoscopy in dermatology

Optoacoustic imaging (OAI) is a hybrid imaging modality that integrates the benefits of optical contrast and ultrasound detection. Raster-scan optoacoustic mesoscopy (RSOM) is an emerging OAI method that provides information about several dermatological conditions’ structural, functional, and molecular features. We searched PubMed and Google Scholar databases through September 2021 for contributions relevant to OAI in the English language. This review contains 32 studies and other relevant literature. Several studies indicate that RSOM is helpful in inflammatory skin conditions such as psoriasis and eczema, especially as it allows more accurate quantification of inflammation-related alterations such as changes to the dermal vasculature. In psoriasis, RSOM can provide objective early diagnosis and monitoring of disease activity and treatment efficacy. Multispectral RSOM, a method in which skin is lightened at more than a single wavelength, is beneficial in diagnosing and monitoring hypoxia-associated conditions, such as systemic sclerosis and chronic wounds. OAI techniques can visualize the pathologicvascularization of skin cancers and quantify their oxygenation status which helps differentiate them from normal skin. Also, they can measure the depth of malignant melanoma and detect the metastatic spread of melanoma cells to sentinel lymph nodes. As demonstrated in this contribution, there is a large spectrum of potential applications of OAI imaging, especially RSOM, in diagnosing, treating, and managing skin diseases.

In Vivo Three-Dimensional Raster Scan Optoacoustic Mesoscopy Using Frequency Domain Inversion.

Optoacoustic signals are typically reconstructed into images using inversion algorithms applied in the time-domain. However, time-domain reconstructions can be computationally intensive and therefore slow when large amounts of raw data are collected from an optoacoustic scan. Here we considered a fast weighted ω-k (FWOK) algorithm operating in the frequency domain to accelerate the inversion in raster-scan optoacoustic mesoscopy (RSOM), while seamlessly incorporating impulse response correction with minimum computational burden. We investigated the FWOK performance with RSOM measurements from phantoms and mice in vivo and obtained 360-fold speed improvement over inversions based on the back-projection algorithm in the time-domain. This previously unexplored inversion of in vivo optoacoustic data with impulse response correction in frequency domain reconstructions points to a promising strategy of accelerating optoacoustic imaging computations, toward video-rate tomography.

Optoacoustic imaging in endocrinology and metabolism.

Imaging is an essential tool in research, diagnostics and the management of endocrine disorders. Ultrasonography, nuclear medicine techniques, MRI, CT and optical methods are already used for applications in endocrinology. Optoacoustic imaging, also termed photoacoustic imaging, is emerging as a method for visualizing endocrine physiology and disease at different scales of detail: microscopic, mesoscopic and macroscopic. Optoacoustic contrast arises from endogenous light absorbers, such as oxygenated and deoxygenated haemoglobin, lipids and water, or exogenous contrast agents, and reveals tissue vasculature, perfusion, oxygenation, metabolic activity and inflammation. The development of high-performance optoacoustic scanners for use in humans has given rise to a variety of clinical investigations, which complement the use of the technology in preclinical research. Here, we review key progress with optoacoustic imaging technology as it relates to applications in endocrinology; for example, to visualize thyroid morphology and function, and the microvasculature in diabetes mellitus or adipose tissue metabolism, with particular focus on multispectral optoacoustic tomography and raster-scan optoacoustic mesoscopy. We explain the merits of optoacoustic microscopy and focus on mid-infrared optoacoustic microscopy, which enables label-free imaging of metabolites in cells and tissues. We showcase current optoacoustic applications within endocrinology and discuss the potential of these technologies to advance research and clinical practice.

Abstract 1476: Vessel normalization following LDH-A knockdown in murine breast tumors

Abstract Abnormal vessel formation plays a major role in tumor progression. Previously, we showed that lactate dehydrogenase A (LDH-A) knockdown (KD) reduces orthotopic 4T1 breast tumor lactate, and has a robust anti-tumor effect through changes in the tumor microenvironment (TME) [1, 2]. Tumors with LDH-A downregulation have lower VEGF-A secretion and reduced formation of new blood vessels [2]. In addition, LDH-A KD cells/tumors displayed reduced HIF-1 signaling, as assessed by BLI using HRE-exGLuc reporter. Changes in the bioluminescence signal corresponded with changes in HIF-1α staining. LDH-A KD tumors also showed less HIF-1α staining compared to control tumors, consistent with less pimonidazole (hypoxia) staining [2]. Murine 4T1 breast cancer cells and tumors with LDH-A knockdown were developed and described [2]. To evaluate the degree of tumor neovascularization and its normalization, we stained tumors for CD31 (endothelial marker) and NG2 (pericytes marker). For pericyte coverage quantification, vessels were counted, and percentage of vessels covered by pericytes were calculated. Blood vessel permeability was assessed by intravenous injection of Evans Blue dye and tumor dye extravasation was quantified. To assess in vivo vascular network over time, a raster-scan optoacoustic mesoscopy (RSOM) was performed. Vessel structure in 4T1 murine tumors changes significantly following LDH-A KD. Normalized LDH-A KD tumor neovascularization was shown using the double immunofluorescence staining for CD31 and NG2, including a more normal vascular morphology, with shorter, more linear (less branched) vessel structure, with a higher percentage of vessels covered by pericytes compared to control tumors. Evans Blue dye extravasation was significantly less (lower vascular permeability) in LDH-A KD tumors compared to control tumors. Applying RSOM to follow the development of the vascular network during tumor growth, we showed better vascularization of LDH-A KD tumors. Furthermore, the number of circulating tumor cells (CTCs) was also significantly lower in blood collected from mice bearing LDH-A KD tumors. We show that LDH-depletion leads to tumor vessel normalization that includes several unique features: structural changes, greater coverage of blood vessel by pericytes and reduced vessel leakiness, leading to changes in the tumor microenvironment, inhibition of metastases formation and prolongation of survival. 1. Rizwan, A., et al., Relationships between LDH-A, lactate, and metastases in 4T1 breast tumors. Clin Cancer Res, 2013. 19(18): p. 5158-69. 2. Serganova, I., et al., LDH-A regulates the tumor microenvironment via HIF-signaling and modulates the immune response. PLOS ONE, 2018. 13(9): p. e0203965. Citation Format: Inna Serganova, Kiranmayi Vemuri, Ivan Cohen, Matthew Lubin, Sheryl Roberts, Masatomo Maeda, Mayuresh Mane, Henry Mann, Thomas Reiner, Eric Chan, Dmitry Yarilin, Katia Manova-Todorova, Roberta Zappasodi, Taha Merghoub, Jason Koutcher, Ronald Blasberg. Vessel normalization following LDH-A knockdown in murine breast tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1476.

Optoacoustic mesoscopy shows potential to increase accuracy of allergy patch testing

Differentiation between irritant and allergic skin reactions in epicutaneous patch testing is based largely on subjective clinical criteria, with the risk of high intraobserver and interobserver variability. Novel dermatological imaging using optoacoustic mesoscopy allows quantitative three-dimensional assessment of microvascular biomarkers. We investigated the potential of optoacoustic imaging to improve the precision of patch test evaluation. Sixty-nine test reactions and 48 healthy skin sections in 52 patients with suspected type IV allergy were examined using raster-scan optoacoustic mesoscopy. We identified biomarkers from the optoacoustic images. Allergic reactions were associated with higher fragmentation of skin vasculature than irritant reactions (19.5 ± 9.7 vs 14.3 ± 3.7 fragments/100 pixels2 ; P < .05), as well as lower ratio of low- to high-frequency acoustic signals (1.6 ± 0.5 vs 2.0 ± 0.6, P < .05). Allergic reactions graded "++" showed higher vessel fragmentation than reactions graded "+" (25.4 ± 13.2 vs 17.1 ± 6.5 fragments/100 pixels2 ; P < .05). A linear model combining the biomarkers fragmentation and frequency ratio could differentiate allergic from irritant test reactions with an area under the receiving operator characteristic curve of 0.80 (95% confidence interval 0.64-0.91), reaching a sensitivity of 81% and specificity of 63%. Optoacoustic mesoscopy shows potential to help in differentiating between allergic and irritant test reactions based on novel biomarkers that may reflect vasodilation, vessel tortuosity, and edema.

Optical features of human skin revealed by optoacoustic mesoscopy in the visible and short-wave infrared regions.

Detailed assessment of skin conditions or the efficacy of skin treatments could greatly benefit from noninvasively assessing the distribution of cutaneous and subcutaneous structures and biomolecules. We considered ultrawideband raster scan optoacoustic mesoscopy with an extended wavelength range from visible to short-wave infrared and observed previously unseen high-resolution images of lipids colocalized with water, melanin, and hemoglobin distribution in human skin. Based on this contrast, the technique resolves subcutaneous fat, the pilosebaceous unit with complete hair strand and bulb, dermal microvasculature, and epidermal structures. We further visualize melanoidins that form via the Maillard reaction in the ultrathin stratum corneum layer, analyze their absorption spectrum, and separate them from the melanin layer. The suggested method may allow novel interrogation of skin conditions, possibly impacting diagnostics and medical and cosmetic treatments.

Fully automated identification of skin morphology in raster-scan optoacoustic mesoscopy using artificial intelligence.

Identification of morphological characteristics of skin lesions is of vital importance in diagnosing diseases with dermatological manifestations. This task is often performed manually or in an automated way based on intensity level. Recently, ultra-broadband raster-scan optoacoustic mesoscopy (UWB-RSOM) was developed to offer unique cross-sectional optical imaging of the skin. A machine learning (ML) approach is proposed here to enable, for the first time, automated identification of skin layers in UWB-RSOM data. The proposed method, termed SkinSeg, was applied to coronal UWB-RSOM images obtained from 12 human participants. SkinSeg is a multi-step methodology that integrates data processing and transformation, feature extraction, feature selection, and classification. Various image features and learning models were tested for their suitability at discriminating skin layers including traditional machine learning along with more advanced deep learning algorithms. An support vector machines-based postprocessing approach was finally applied to further improve the classification outputs. Random forest proved to be the most effective technique, achieving mean classification accuracy of 86.89% evaluated based on a repeated leave-one-out strategy. Insights about the features extracted and their effect on classification accuracy are provided. The highest accuracy was achieved using a small group of four features and remained at the same level or was even slightly decreased when more features were included. Convolutional neural networks provided also promising results at a level of approximately 85%. The application of the proposed postprocessing technique was proved to be effective in terms of both testing accuracy and three-dimensional visualization of classification maps. SkinSeg demonstrated unique potential in identifying skin layers. The proposed method may facilitate clinical evaluation, monitoring, and diagnosis of diseases linked to skin inflammation, diabetes, and skin cancer.

Skin Surface Detection in 3D Optoacoustic Mesoscopy Based on Dynamic Programming.

Optoacoustic (photoacoustic) mesoscopy offers unique capabilities in skin imaging and resolves skin features associated with detection, diagnosis, and management of disease. A critical first step in the quantitative analysis of clinical optoacoustic images is to identify the skin surface in a rapid, reliable, and automated manner. Nevertheless, most common edge- and surface-detection algorithms cannot reliably detect the skin surface on 3D raster-scan optoacoustic mesoscopy (RSOM) images, due to discontinuities and diffuse interfaces in the image. We present herein a novel dynamic programming approach that extracts the skin boundary as a 2D surface in one single step, as opposed to consecutive extraction of several independent 1D contours. A domain-specific energy function is introduced, taking into account the properties of volumetric optoacoustic mesoscopy images. The accuracy of the proposed method is validated on scans of the volar forearm of 19 volunteers with different skin complexions, for which the skin surface has been traced manually to provide a reference. In addition, the robustness and the limitations of the method are demonstrated on data where the skin boundaries are low-contrast or ill-defined. The automatic skin surface detection method can improve the speed and accuracy in the analysis of quantitative features seen on the RSOM images and accelerate the clinical translation of the technique. Our method can likely be extended to identify other types of surfaces in the RSOM and other imaging modalities.

Non-invasive imaging in dermatology and the unique potential of raster-scan optoacoustic mesoscopy.

In recent years, several non‐invasive imaging methods have been introduced to facilitate diagnostics and therapy monitoring in dermatology. The microscopic imaging methods are restricted in their penetration depth, while the mesoscopic methods probe deeper but provide only morphological, not functional, information. ‘Raster‐scan optoacoustic mesoscopy’ (RSOM), an emerging new imaging technique, combines deep penetration with contrast based on light absorption, which provides morphological, molecular and functional information. Here, we compare the capabilities and limitations of currently available dermatological imaging methods and highlight the principles and unique abilities of RSOM. We illustrate the clinical potential of RSOM, in particular for non‐invasive diagnosis and monitoring of inflammatory and oncological skin diseases.

Motion Quantification and Automated Correction in Clinical RSOM.

Raster-scan optoacoustic mesoscopy (RSOM) offers high-resolution non-invasive insights into skin pathophysiology, which holds promise for disease diagnosis and monitoring in dermatology and other fields. However, RSOM is quite vulnerable to vertical motion of the skin, which can depend on the part of the body being imaged. Motion correction algorithms have already been proposed, but they are not fully automated, they depend on anatomical segmentation pre-processing steps that might not be performed successfully, and they are not site- specific. Here, we determined for the first time the magnitude of the micrometric vertical skin displacements at different sites on the body that affect RSOM. The quantifi- cation of motion allowed us to develop a site-specific correction algorithm. The algorithm is fully automated and does not need prior anatomical information. We found that the magnitude of the vertical motion depends strongly on the site of imaging and is caused by breathing, heart beating, and arterial pulsation. The developed algorithm resulted in more than 2-fold improvement in the signal-to-noise ratio of the reconstructed images at every site tested. Proposing an effective automated motion correction algorithm paves the way for realizing the full clinical potential of RSOM.

Assessing hyperthermia-induced vasodilation in human skin in vivo using optoacoustic mesoscopy.

The aim of this study was to explore the unique imaging abilities of optoacoustic mesoscopy to visualize skin structures and microvasculature with the view of establishing a robust approach for monitoring heat-induced hyperemia in human skin in vivo. Using raster-scan optoacoustic mesoscopy (RSOM), we investigated whether optoacoustic (photoacoustic) mesoscopy can identify changes in skin response to local heating at microvasculature resolution in a cross-sectional fashion through skin in the human forearm. We visualized the heat-induced hyperemia for the first time with single-vessel resolution throughout the whole skin depth. We quantified changes in total blood volume in the skin and their correlation with local heating. In response to local heating, total blood volume increased 1.83- and 1.76-fold, respectively, in the volar and dorsal aspects of forearm skin. We demonstrate RSOM imaging of the dilation of individual vessels in the skin microvasculature, consistent with hyperemic response to heating at the skin surface. Our results demonstrate great potential of RSOM for elucidating the morphology, functional state and reactivity of dermal microvasculature, with implications for diagnostics and disease monitoring. Image: Cross-sectional view of skin microvasculature dilated in response to hyperthermia.

Assessing nailfold microvascular structure with ultra-wideband raster-scan optoacoustic mesoscopy

Nailfold capillaroscopy, based on bright-field microscopy, is widely used to diagnose systemic sclerosis (SSc). However it cannot reveal information about venules and arterioles lying deep under the nailfold, nor can it provide detailed data about surface microvasculature when the skin around the nail is thick. These limitations reflect the fact that capillaroscopy is based on microscopy methods whose penetration depth is restricted to about 200 μm. We investigated whether ultra-wideband raster-scan optoacoustic mesoscopy (UWB-RSOM) can resolve small capillaries of the nailfold in healthy volunteers and compared the optoacoustic data to conventional capillaroscopy examinations. We quantified UWB-RSOM-resolved capillary density and capillary diameter as features that relate to SSc biomarkers, and we obtained the first three-dimensional, in vivo images of the deeper arterioles and venules. These results establish the potential of UWB-RSOM for analyzing SSc-relevant markers.

Motion correction in optoacoustic mesoscopy

Raster-scan optoacoustic mesoscopy (RSOM), also termed photoacoustic mesoscopy, offers novel insights into vascular morphology and pathophysiological biomarkers of skin inflammation in vivo at depths unattainable by other optical imaging methods. Using ultra-wideband detection and focused ultrasound transducers, RSOM can achieve axial resolution of 4 micron and lateral resolution of 20 micron to depths of several millimeters. However, motion effects may deteriorate performance and reduce the effective resolution. To provide high-quality optoacoustic images in clinical measurements, we developed a motion correction algorithm for RSOM. The algorithm is based on observing disruptions of the ultrasound wave front generated by the vertical movement of the melanin layer at the skin surface. From the disrupted skin surface, a smooth synthetic surface is generated, and the offset between the two surfaces is used to correct for the relative position of the ultrasound detector. We test the algorithm in measurements of healthy and psoriatic human skin and achieve effective resolution up to 5-fold higher than before correction. We discuss the performance of the correction algorithm and its implications in the context of multispectral mesoscopy.

Universal weighted synthetic aperture focusing technique (W-SAFT) for scanning optoacoustic microscopy

Optoacoustic microscopy (OAM) is a hybrid imaging method that can achieve high spatial resolution at superficial depths through use of focused illumination; it can be adapted for imaging with ultrasonic resolution at much greater depths where the excitation light is diffuse. These two distinct modes of operation can be further combined to create a highly scalable technique that can image at multiple penetration scales by gradually exchanging microscopic optical resolution in superficial tissue layers with ultrasonic resolution at diffuse (macroscopic) depths. However, OAM commonly employs scanning acquisition geometries that impede the effective use of synthetic aperture focusing techniques due to varying illumination patterns and non-uniformity of the excitation light field. Here we present a universal framework for scanning optoacoustic microscopy that uses a weighted synthetic aperture focusing technique (W-SAFT) to create a uniform imaging sensitivity across microscopic, mesoscopic, and macroscopic penetration regimes. Robust performance of the new multi-scale reconstruction methodology is showcased with simulations and synthetic phantoms, and validated with experimental data acquired from a highly scattering juvenile zebrafish specimen. It is shown that consideration of the light fluence is vital for maintaining the optically dictated lateral resolution at ballistic depths while optimizing the resolution of out-of-focus ultrasonic data; additionally, the dynamic-range compression facilitates the visualization across the entire imaged volume. The newly introduced W-SAFT reconstruction framework is universally applicable to a wide palette of scanning-based optoacoustic imaging techniques employing non-uniform and/or varying illumination, such as acoustic resolution and hybrid focus microscopy, raster-scan optoacoustic mesoscopy, as well as tomographic approaches using scanning of focused array transducers.

Precision assessment of label-free psoriasis biomarkers with ultra-broadband optoacoustic mesoscopy

Imaging plays a critical role in the diagnosis and assessment of dermatological conditions. However, optical or optoacoustic microscopy techniques are limited to visualizing superficial skin features owing to strong photon scattering, whereas ultrasound methods, which can probe deeper-seated tissue, lack the contrast to image pathophysiological mechanisms in detail. Here, we demonstrate that raster-scan optoacoustic mesoscopy (RSOM) implemented in ultra-broadband (10–180 MHz) detection mode bridges the depth capabilities of ultrasound and the resolution range and high contrast of optical methods in clinical dermatology. Using tomographic reconstruction and frequency equalization to represent low and high spatial-frequency components, we visualize skin morphology and vascular patterns in the dermis and sub-dermis of psoriasis patients, enabling quantification of inflammation and other biomarkers of psoriasis without the need for contrast agents. Implemented in a handheld device, we showcase how label-free biomarkers detected by RSOM correlate with clinical score. The method can also be extended to assess a larger spectrum of dermatological conditions. Ultra-broadband optoacoustic mesoscopy implemented in a handheld device enables the visualization of vascular patterns in the dermis and sub-dermis of psoriasis patients, and the quantification of inflammatory biomarkers of psoriasis.