Oxygen Saturation Imaging Using LED-Based Photoacoustic System.
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ABSTRACT:
SUBMITTER: Bulsink R
PROVIDER: S-EPMC7795655 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:LED-based photoacoustic imaging has practical value in that it is affordable and rugged; however, this technology has largely been confined to anatomic imaging with limited applications into functional or molecular imaging. Here, we report molecular imaging reactive oxygen and nitrogen species (RONS) with a near-infrared (NIR) absorbing small molecule (CyBA) and LED-based photoacoustic imaging equipment. CyBA produces increasing photoacoustic signal in response to peroxynitrite (ONOO-) and hydrogen peroxide (H2O2) with photoacoustic signal increases of 3.54 and 4.23-fold at 50 µM of RONS at 700 nm, respectively. CyBA is insensitive to OCl-, ˙NO, NO2-, NO3-, tBuOOH, O2-, C4H9O˙, HNO, and ˙OH, but can detect ONOO- in whole blood and plasma. CyBA was then used to detect endogenous RONS in macrophage RAW 246.7 cells as well as a rodent model; these results were confirmed with fluorescence microscopy. Importantly, CyB suffers photobleaching under a Nd:YAG laser but the signal decrease is <2% with the low-power LED-based photoacoustic system and the same radiant exposure time. To the best of our knowledge, this is the first report to describe molecular imaging with an LED-based photoacoustic scanner. This study not only reveals the sensitive photoacoustic detection of RONS but also highlights the utility of LED-based photoacoustic imaging.
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Project description:Photoacoustic imaging (PAI) is a non-invasive, high-resolution hybrid imaging modality that combines optical excitation and ultrasound detection. PAI can image endogenous chromophores (melanin, hemoglobin, etc.) and exogenous contrast agents in different medical applications. However, most current equipment uses sophisticated and complicated OPO lasers with tuning and stability features inconsistent with broad clinical deployment. As the number of applications of PAI in medicine increases, there is an urgent need to make the imaging equipment more compact, portable, and affordable. Here, portable light emitting diode - based photoacoustic imaging (PLED-PAI) was introduced and characterized in terms of system specifications, light source characterizations, photoacoustic spatial/temporal resolution, and penetration. The system uses two LED arrays attached to the sides of a conventional ultrasound transducer. The LED pulse repetition rate is tunable between 1 K Hz, 2 K Hz, 3 K Hz, and 4 K Hz. The axial resolution was 0.268 mm, and the lateral resolution was between 0.55 and 0.59 mm. The system could detect optical absorber (pencil lead) at a depth of 3.2 cm and the detection limits of indocyanine green (ICG) and methylene blue (MB) were 9 μM and 0.78 mM. In vivo imaging of labeled human mesenchymal stem cells was achieved to confirm compatibility with small animal imaging. The characterization we report here may have value to other groups evaluating commercially available photoacoustic imaging equipment.
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Project description:SIGNIFICANCE:Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. AIM:To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. APPROACH:Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. RESULTS:The mean of the absolute difference between the true mean vessel sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. CONCLUSIONS:3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.
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Project description:IMPACT STATEMENT:Noninvasive imaging techniques provide insight into physiology that is complementary to tissue morphology obtained by invasive histology. Optical imaging techniques, such as laser speckle contrast analysis, are used in vivo to longitudinally evaluate vascularization. Despite their high spatial resolution, these techniques have a limited imaging depth. In this study, we demonstrate how a dual LED-based photoacoustic (PA) and ultrasound system can delineate changes in perfusion at depth within scaffolds containing basic fibroblast growth factor. Perfusion changes detected by PA corroborated with vessel density. PA imaging could be a noninvasive and sensitive method for evaluating vascularization at depth in larger constructs.
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Project description:Photoacoustic imaging has shown great potential for guiding minimally invasive procedures by accurate identification of critical tissue targets and invasive medical devices (such as metallic needles). The use of light emitting diodes (LEDs) as the excitation light sources accelerates its clinical translation owing to its high affordability and portability. However, needle visibility in LED-based photoacoustic imaging is compromised primarily due to its low optical fluence. In this work, we propose a deep learning framework based on U-Net to improve the visibility of clinical metallic needles with a LED-based photoacoustic and ultrasound imaging system. To address the complexity of capturing ground truth for real data and the poor realism of purely simulated data, this framework included the generation of semi-synthetic training datasets combining both simulated data to represent features from the needles and in vivo measurements for tissue background. Evaluation of the trained neural network was performed with needle insertions into blood-vessel-mimicking phantoms, pork joint tissue ex vivo and measurements on human volunteers. This deep learning-based framework substantially improved the needle visibility in photoacoustic imaging in vivo compared to conventional reconstruction by suppressing background noise and image artefacts, achieving 5.8 and 4.5 times improvements in terms of signal-to-noise ratio and the modified Hausdorff distance, respectively. Thus, the proposed framework could be helpful for reducing complications during percutaneous needle insertions by accurate identification of clinical needles in photoacoustic imaging.
| S-EPMC9048160 | biostudies-literature
Project description:SignificanceThe multispectral imaging-based tissue oxygen saturation detecting (TOSD) system offers deeper penetration ( ∼2 to 3 mm) and comprehensive tissue oxygen saturation ( StO2 ) assessment and recognizes the wound healing phase at a low cost and computational requirement. The potential for miniaturization and integration of TOSD into telemedicine platforms could revolutionize wound care in the challenging pandemic era.AimWe aim to validate TOSD's application in detecting StO2 by comparing it with wound closure rates and laser speckle contrast imaging (LSCI), demonstrating TOSD's ability to recognize the wound healing process.ApproachUtilizing a murine model, we compared TOSD with digital photography and LSCI for comprehensive wound observation in five mice with 6-mm back wounds. Sequential biochemical analysis of wound discharge was investigated for the translational relevance of TOSD.ResultsTOSD demonstrated constant signals on unwounded skin with differential changes on open wounds. Compared with LSCI, TOSD provides indicative recognition of the proliferative phase during wound healing, with a higher correlation coefficient to wound closure rate (TOSD: 0.58; LSCI: 0.44). StO2 detected by TOSD was further correlated with proliferative phase angiogenesis markers.ConclusionsOur findings suggest TOSD's enhanced utility in wound management protocols, evaluating clinical staging and therapeutic outcomes. By offering a noncontact, convenient monitoring tool, TOSD can be applied to telemedicine, aiming to advance wound care and regeneration, potentially improving patient outcomes and reducing healthcare costs associated with chronic wounds.
| S-EPMC11321076 | biostudies-literature
Project description:BackgroundVascular structure, blood oxygen saturation, and melanin status of the epidermis are chromophore factors related to light absorption. Therefore, they are likely to be related to skin appearance. Thus, it is important to measure these internal skin features and understand their characteristics. Thus, we aimed to analyze the individual differences and aging changes in the skin by measuring the internal skin characteristics, such as vascular structure, oxygen saturation, and the 3D distribution of melanin in the epidermis, using a noninvasive photoacoustic (PA) measurement method.Materials and methodsA PA measurement device was used as a noninvasive measurement method. Eighty Japanese women aged between 20 and 60 years were enrolled. The target area was the buccal region of the face.ResultsThe blood vessel structure showed a decrease in fine vessels with age, with a stronger tendency observed in the dermis layer, and the volume of blood vessels was larger in the dermis layer than in the dermal-subcutaneous fat boundary layer. Oxygen saturation showed a similar decreasing trend with age in all depths examined. Melanin condition as the torus-like pattern structure tended to increase with age.ConclusionPA measurements revealed the characteristics of several chromophores, providing a new skin aging mechanism.
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Project description:Intraoperative monitoring of tissue oxygen saturation (StO2 ) has potentially important applications in procedures such as organ transplantation or colorectal surgery, where successful reperfusion affects the viability and integrity of repaired tissues. In this paper a liquid crystal tuneable filter-based multispectral imaging (MSI) laparoscope is described. Motion-induced image misalignments are reduced, using feature-based registration, before regression of the tissue reflectance spectra to calculate relative quantities of oxy- and deoxyhaemoglobin. The laparoscope was validated in vivo, during porcine abdominal surgery, by making parallel MSI and blood gas measurements of the small bowel vasculature. Ischaemic conditions were induced by local occlusion of the mesenteric arcade and monitored using the system. The MSI laparoscope was capable of measuring StO2 over a wide range (30-100%) with a temporal error of ± 7.5%. The imager showed sensitivity to spatial changes in StO2 during dynamic local occlusions, as well as tracking the recovery of tissues post-occlusion.
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Project description:Label free imaging of oxygenation distribution in tissues is highly desired in numerous biomedical applications, but is still elusive, in particular in sub-epidermal measurements. Eigenspectra multispectral optoacoustic tomography (eMSOT) and its Bayesian-based implementation have been introduced to offer accurate label-free blood oxygen saturation (sO2) maps in tissues. The method uses the eigenspectra model of light fluence in tissue to account for the spectral changes due to the wavelength dependent attenuation of light with tissue depth. eMSOT relies on the solution of an inverse problem bounded by a number of ad hoc hand-engineered constraints. Despite the quantitative advantage offered by eMSOT, both the non-convex nature of the optimization problem and the possible sub-optimality of the constraints may lead to reduced accuracy. We present herein a neural network architecture that is able to learn how to solve the inverse problem of eMSOT by directly regressing from a set of input spectra to the desired fluence values. The architecture is composed of a combination of recurrent and convolutional layers and uses both spectral and spatial features for inference. We train an ensemble of such networks using solely simulated data and demonstrate how this approach can improve the accuracy of sO2 computation over the original eMSOT, not only in simulations but also in experimental datasets obtained from blood phantoms and small animals (mice) in vivo. The use of a deep-learning approach in optoacoustic sO2 imaging is confirmed herein for the first time on ground truth sO2 values experimentally obtained in vivo and ex vivo.
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Project description:Photoacoustic (PA) imaging has shown tremendous promise for imaging tumor vasculature and its function at deeper penetration depths without the use of exogenous contrast agents. Traditional PA imaging systems employ expensive and bulky class IV lasers with low pulse repetition rate, due to which its availability for preclinical cancer research is hampered. In this study, we evaluated the capability of a Light-Emitting Diode (LED)-based PA and ultrasound (US) imaging system for monitoring heterogeneous microvasculature in tumors (up to 10 mm in depth) and quantitatively compared the PA images with gold standard histology images. We used a combination of a 7 MHz linear array US transducer and 850 nm excitation wavelength LED arrays to image blood vessels in a subcutaneous tumor model. After imaging, the tumors were sectioned and stained for endothelial cells to correlate with PA images across similar cross-sections. Analysis of 30 regions of interest in tumors from different mice showed a statistically significant R-value of 0.84 where the areas with high blood vessel density had high PA response while low blood vessel density regions had low PA response. Our results confirm that LED-based PA and US imaging can provide 2D and 3D images of tumor vasculature and the potential it has as a valuable tool for preclinical cancer research.
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