Oxygen Saturation Imaging Using LED-Based Photoacoustic System

Cost-Effective Photoacoustic Imaging Using High-Power Light-Emitting Diodes Driven by an Avalanche Oscillator

Photoacoustic imaging (PAI) is an emerging modality that merges optical and ultrasound imaging to provide high-resolution and functional insights into biological tissues. This technique leverages the photoacoustic effect, where tissue absorbs pulsed laser light, generating acoustic waves that are captured to reconstruct images. While lasers have traditionally been the light source for PAI, their high cost and complexity drive interest towards alternative sources like light-emitting diodes (LEDs). This study evaluates the feasibility of using an avalanche oscillator to drive high-power LEDs in a basic photoacoustic imaging system. An avalanche oscillator, utilizing semiconductor avalanche breakdown to produce high-voltage pulses, powers LEDs to generate short, high-intensity light pulses. The system incorporates an LED array, an ultrasonic transducer, and an amplifier for signal detection. Key findings include the successful generation of short light pulses with sufficient intensity to excite materials and the system's capability to produce detectable photoacoustic signals in both air and water environments. While LEDs demonstrate cost-effectiveness and portability advantages, challenges such as lower power and broader spectral bandwidth compared to lasers are noted. The results affirm that LED-based photoacoustic systems, though currently less advanced than laser-based systems, present a promising direction for affordable and portable imaging technologies.

Photoacoustic imaging of rat kidney tissue oxygenation using second near-infrared wavelengths

Significance

Conventionally, spectral photoacoustic imaging (sPAI) to assess tissue oxygenation (sO 2 ) uses optical wavelengths in the first near-infrared (NIR-I) window. This limits the maximum photoacoustic imaging depth due to the high spectral coloring of biological tissues and has been a major barrier to the clinical translation of the technique.

Aim

We demonstrate the second near-infrared (NIR-II) tissue optical window (950 to 1400 nm) for the assessment of blood and tissuesO 2 .

Approach

The NIR-II PA spectra of oxygenated and deoxygenated hemoglobin were first characterized using a phantom. Optimal wavelengths to minimize spectral coloring were identified. The resulting NIR-II PA imaging methods were then validated in vivo by measuring kidneysO 2 in adult female rats.

Results

sPAI of whole blood, in a phantom, and of blood in kidneys in vivo produced PA spectra proportional to wavelength-dependent optical absorption. Using the NIR-II wavelengths for spectral unmixing resulted in a ∼ 50 % decrease in the error of the estimated bloodsO 2 , compared with conventional NIR-I wavelengths. In vivo measurements of kidneysO 2 validated these findings, with a similar 50% reduction in error when using NIR-II wavelengths versus NIR-I wavelengths at larger illumination depths.

Conclusions

sPAI using NIR-II wavelengths improved the accuracy of tissuesO 2 measurements. This is likely due to reduced scattering, which reduces the attenuation and, therefore, the impact of spectral coloring in this wavelength range. Combined with the increased safe skin exposure fluence limits in this wavelength range, these results demonstrate the potential to use NIR-II wavelengths for quantitative sPAI ofsO 2 from deep heterogeneous tissues.

Quantitative photoacoustic imaging using known chromophores as fluence marker

Photoacoustic imaging offers optical contrast images of human tissue at acoustic resolution, making it valuable for diverse clinical applications. However, quantifying tissue composition via optical contrast remains challenging due to the unknown light fluence within the tissue. Here, we propose a method that leverages known chromophores (e.g., arterial blood) to improve the accuracy of quantitative photoacoustic imaging. By using the optical properties of a known chromophore as a fluence marker and integrating it into the optical inversion process, we can estimate the unknown fluence within the tissue. Experimentally, we demonstrate that this approach successfully recovers both the spectral shape and magnitude of the optical absorption coefficient of an unknown chromophore. Additionally, we show that the fluence marker method enhances conventional optical inversion techniques, specifically (i) a straightforward iterative approach and (ii) a gradient-based method. Our results indicate an improvement in accuracy of up to 24.4% when comparing optical absorption recovery with and without the fluence marker. Finally, we present the method's performance and illustrate its applications in carotid plaque quantification.

Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures

Recent advances in Light Emitting Diode (LED) technology have enabled a more affordable high frame rate photoacoustic imaging (PA) alternative to traditional laser-based PA systems that are costly and have slow pulse repetition rate. However, a major disadvantage with LEDs is the low energy outputs that do not produce high signal-to-noise ratio (SNR) PA images. There have been recent advancements in integrating deep learning methodologies aimed to address the challenge of improving SNR in LED-PA images, yet comprehensive evaluations across varied datasets and architectures are lacking. In this study, we systematically assess the efficacy of various Encoder-Decoder-based CNN architectures for enhancing SNR in real-time LED-based PA imaging. Through experimentation with in vitro phantoms, ex vivo mouse organs, and in vivo tumors, we compare basic convolutional autoencoder and U-Net architectures, explore hierarchical depth variations within U-Net, and evaluate advanced variants of U-Net. Our findings reveal that while U-Net architectures generally exhibit comparable performance, the Dense U-Net model shows promise in denoising different noise distributions in the PA image. Notably, hierarchical depth variations did not significantly impact performance, emphasizing the efficacy of the standard U-Net architecture for practical applications. Moreover, the study underscores the importance of evaluating robustness to diverse noise distributions, with Dense U-Net and R2 U-Net demonstrating resilience to Gaussian, salt and pepper, Poisson, and Speckle noise types. These insights inform the selection of appropriate deep learning architectures based on application requirements and resource constraints, contributing to advancements in PA imaging technology.

In vivo, online label-free monitoring of heterogenous oxygen utilization during phototherapy with real-time ultrasound-guided photoacoustic imaging

Understanding the tumor microenvironment, particularly the vascular density and the availability of oxygen, is key in individualizing treatment approaches and determining their efficacy. While there are many therapies including radiotherapy that are ineffective in hypoxic tumor microenvironments, here we demonstrate the heterogeneous oxygen consumption during photodynamic therapy (PDT), a non-invasive treatment method using localized light to activate a photosensitive drug in the presence of oxygen that has shown high effectiveness in the treatment of various types of tumors, including those presented in head and neck cancer (HNC) patients. While our previous work has demonstrated that blood oxygen saturation (StO2) mapped before and after treatment with ultrasound-guided photoacoustic imaging (US-PAI) can be used as a surrogate marker for the regionalized long-term efficacy of PDT, real-time monitoring of StO2 during PDT could provide additional insights on oxygen consumption and inform dose design for "on the spot" treatment decisions. Specifically, in this work, we integrated the US-PAI transducer probe with PDT light delivery fibers. We tested the setup on murine tumor models intravenously injected with liposomal benzoporphyrin derivative (BPD) photosensitizer at 0.5 mg/kg dose and photodynamic illumination at 100 and 400 mW/cm2 fluence rate. As expected, we observed with our US-PAI StO2 images that the rate of oxygen utilization increases when using a high fluence rate (HFR) light dose. Particularly in the higher fluence rate group, we observed StO2 reaching a minimum mid-light dose, followed by some degree of reoxygenation. US-PAI added the advantage of spatial information to StO2 monitoring, which allowed us to match regions of re-oxygenation during therapy to retained vascular function with immunohistochemistry. Overall, our results have demonstrated the potential of US-PAI for applications in online dosimetry for cancer therapies such as PDT, using oxygen changes to detect regionalized physiological vascular response in the tumor microenvironment.

Ex vivo human teeth imaging with various photoacoustic imaging systems

Dental caries cause pain and if not diagnosed, it may lead to the loss of teeth in extreme cases. Dental X-ray imaging is the gold standard for caries detection; however, it cannot detect hidden caries. In addition, the ionizing nature of X-ray radiation is another concern. Hence, other alternate imaging modalities like photoacoustic (PA) imaging are being explored for dental imaging. Here, we demonstrate the feasibility of acoustic resolution photoacoustic microscopy (ARPAM) to image a tooth with metal filling, circular photoacoustic computed tomography (cPACT) to acquire images of teeth with caries and pigmentation, and linear array-based photoacoustic imaging (lPACT) of teeth with caries and pigmentation. The cavity measured with lPACT imaging is compared with the X-ray computed tomography image. The metal filling and its boundaries are clearly seen in the ARPAM image. cPACT images at 1064 nm were a better representative of the tooth surface compared to the images acquired at 532 nm. It was possible to detect the cavities present in the dentine when lPACT imaging was used. The PA signal from the pigmented caries on the lateral surface (occlusion view) of the tooth was high when imaged using the lPACT system.

Parallel diffusion models promote high detail-fidelity photoacoustic microscopy in sparse sampling

Reconstructing sparsely sampled data is fundamental for achieving high spatiotemporal resolution photoacoustic microscopy (PAM) of microvascular morphology in vivo. Convolutional networks (CNN) and generative adversarial networks (GAN) have been introduced to high-speed PAM, but due to the use of upsampling in CNN-based networks to restore details and the instability in GAN training, they struggle to learn the entangled microvascular network structure and vascular texture features, resulting in only achieving low detail-fidelity imaging of microvascular. The diffusion models is richly sampled and can generate high-quality images, which is very helpful for the complex vascular features in PAM. Here, we propose an approach named parallel diffusion models (PDM) with parallel learning of Noise task and Image task, where the Noise task optimizes through variational lower bounds to generate microvascular structures that are visually realistic, and the Image task improves the fidelity of the generated microvascular details through image-based loss. With only 1.56% of fully sampled pixels from photoacoustic human oral data, PDM achieves an LPIPS of 0.199. Additionally, using PDM in high-speed 16x PAM prevents breathing artifacts and image distortion issues caused by low-speed sampling, reduces the standard deviation of the Row-wise Self-Correlation Coefficient, and maintains high image quality. It achieves high confidence in reconstructing detailed information from sparsely sampled data and will promote the application of reconstructed sparsely sampled data in realizing high spatiotemporal resolution PAM.

U-Net enhanced real-time LED-based photoacoustic imaging

Photoacoustic (PA) imaging is hybrid imaging modality with good optical contrast and spatial resolution. Portable, cost-effective, smaller footprint light emitting diodes (LEDs) are rapidly becoming important PA optical sources. However, the key challenge faced by the LED-based systems is the low light fluence that is generally compensated by high frame averaging, consequently reducing acquisition frame-rate. In this study, we present a simple deep learning U-Net framework that enhances the signal-to-noise ratio (SNR) and contrast of PA image obtained by averaging low number of frames. The SNR increased by approximately four-fold for both in-class in vitro phantoms (4.39 ± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We also demonstrate the noise invariancy of the network and discuss the downsides (blurry outcome and failure to reduce the salt & pepper noise). Overall, the developed U-Net framework can provide a real-time image enhancement platform for clinically translatable low-cost and low-energy light source-based PA imaging systems.

Improvement of LED-based photoacoustic imaging using lag-coherence factor (LCF) beamforming

BACKGROUND

Owing to its portability, affordability, and energy-efficiency, LED-based photoacoustic (PA) imaging is increasingly becoming popular when compared to its laser-based alternative, mainly for superficial vascular imaging applications. However, this technique suffers from low SNR and thereby limited imaging depth. As a result, visual image quality of LED-based PA imaging is not optimal, especially in sub-surface vascular imaging applications.

PURPOSE

Combination of linear ultrasound (US) probes and LED arrays are the most common implementation in LED-based PA imaging, which is currently being explored for different clinical imaging applications. Traditional delay-and-sum (DAS) is the most common beamforming algorithm in linear array-based PA detection. Side-lobes and reconstruction-related artifacts make the DAS performance unsatisfactory and poor for a clinical-implementation. In this work, we explored a new weighting-based image processing technique for LED-based PAs to yield improved image quality when compared to the traditional methods.

METHODS

We are proposing a lag-coherence factor (LCF), which is fundamentally based on the combination of the spatial auto-correlation of the detected PA signals. In LCF, the numerator contains lag-delay-multiply-and-sum (DMAS) beamformer instead of a conventional DAS beamformer. A spatial auto-correlation operation is performed between the detected US array signals before using DMAS beamformer. We evaluated the new method on both tissue-mimicking phantom (2D) and human volunteer imaging (3D) data acquired using a commercial LED-based PA imaging system.

RESULTS

Our novel correlation-based weighting technique showed LED-based PA image quality improvement when it is combined with conventional DAS beamformer. Both phantom and human volunteer imaging results gave a direct confirmation that by introducing LCF, image quality was improved and this method could reduce side-lobes and artifacts when compared to the DAS and coherence-factor (CF) approaches. Signal-to-noise ratio, generalized contrast-to-noise ratio, contrast ratio and spatial resolution were evaluated and compared with conventional beamformers to assess the reconstruction performance in a quantitative way. Results show that our approach offered image quality enhancement with an average signal-to-noise ratio and spatial resolution improvement of around 20% and 25% respectively, when compared with conventional CF based DAS algorithm.

CONCLUSIONS

Our results demonstrate that the proposed LCF based algorithm performs better than the conventional DAS and CF algorithms by improving signal-to-noise ratio and spatial resolution. Therefore, our new weighting technique could be a promising tool to improve the performance of LED-based PA imaging and thus accelerate its clinical translation.

Adaptive coherent weighted averaging algorithm for enhancement of photoacoustic tomography images of brain

One common method to improve the low signal-to-noise ratio of the photoacoustic (PA) signal generated from weak absorbers or absorbers located in deep tissue is to acquire signal multiple times from the same region and perform averaging. However, pulse-to-pulse laser fluctuations together with differences in the beam profile of the pulses create undeterministic multiple scattering processes in the tissue. This phenomenon consequently induces a spatiotemporal displacement in the PA signal samples which in turn deteriorates the effectiveness of signal averaging. Here, we present an adaptive coherent weighted averaging algorithm to adjust the locations and values of PA signal samples for more efficient signal averaging. The proposed method is evaluated in a linear array-based PA imaging setup of ex vivo sheep brain.

Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.

Design and characterization of color printed polyurethane films as biomedical phantom layers

We propose a new, user-friendly and accessible approach for fabricating thin phantoms with controllable absorption properties in magnitude, spectral shape, and spatial distribution. We utilize a standard office laser color printer to print on polyurethane thin films (40 - 60 μm), commonly available as medical film dressings and ultrasound probe covers. We demonstrate that the optical attenuation and absorption of the printed films correlate linearly with the printer input settings (opacity), which facilitates a systematic phantom design. The optical and acoustic properties of these polyurethane films are similar to biological tissue. We argue that these thin phantoms are applicable to a wide range of biomedical applications. Here, we introduce two potential applications: (1) homogeneous epidermal melanin phantoms and (2) spatially resolved absorbers for photoacoustic imaging. We characterize the thin phantoms in terms of optical properties, thickness, microscopic structure, and reproducibility of the printing process.

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

Visual inertial odometry enabled 3D ultrasound and photoacoustic imaging

There is an increasing need for 3D ultrasound and photoacoustic (USPA) imaging technology for real-time monitoring of dynamic changes in vasculature or molecular markers in various malignancies. Current 3D USPA systems utilize expensive 3D transducer arrays, mechanical arms or limited-range linear stages to reconstruct the 3D volume of the object being imaged. In this study, we developed, characterized, and demonstrated an economical, portable, and clinically translatable handheld device for 3D USPA imaging. An off-the-shelf, low-cost visual odometry system (the Intel RealSense T265 camera equipped with simultaneous localization and mapping technology) to track free hand movements during imaging was attached to the USPA transducer. Specifically, we integrated the T265 camera into a commercially available USPA imaging probe to acquire 3D images and compared it to the reconstructed 3D volume acquired using a linear stage (ground truth). We were able to reliably detect 500 µm step sizes with 90.46% accuracy. Various users evaluated the potential of handheld scanning, and the volume calculated from the motion-compensated image was not significantly different from the ground truth. Overall, our results, for the first time, established the use of an off-the-shelf and low-cost visual odometry system for freehand 3D USPA imaging that can be seamlessly integrated into several photoacoustic imaging systems for various clinical applications.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

Photoacoustic imaging to monitor outcomes during hyperbaric oxygen therapy: validation in a small cohort and case study in a bilateral chronic ischemic wound

Hyperbaric oxygen therapy (HBO2) is a common therapeutic modality that drives oxygen into hypoxic tissue to promote healing. Here, ten patients undergoing HBO2 underwent PA oximetry of the left radial artery and forearm pre- and post-HBO2; this cohort validated the use of PA imaging in HBO2. There was a significant increase in radial artery oxygenation after HBO2 (p = 0.002) in the validation cohort. We also include a case study: a non-diabetic male in his 50s (HB 010) presenting with bilateral ischemic and gangrenous wounds. HB 010 showed higher perfusion and oxygen saturation on the right foot than the left after HBO2 which correlated with independent surgical observations. Imaging assisted with limb salvage treatment. Hence, this work shows that PA imaging can measure changes in arterial oxygen saturation due to HBO2; it can also produce 3D maps of tissue oxygenation and evaluate response to therapy during HBO2.

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

SIGNIFICANCE

Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

AIM

Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium.

APPROACH

Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets.

RESULTS

The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction.

CONCLUSIONS

The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Validation of photoacoustic/ultrasound dual imaging in evaluating blood oxygen saturation

Photoacoustic imaging (PAI) was performed to evaluate oxygen saturation (sO₂) of blood-mimicking phantoms, femoral arteries in beagles, and radial arteries in humans at various sO₂ plateaus. The accuracy (root mean square error, RMSE) of PAI sO₂ compared with reference sO₂ was calculated. In blood-mimicking phantoms, PAI achieved an accuracy of 1.49% and a mean absolute error (MAE) of 1.09% within 25 mm depth, and good linearity (R = 0.968; p < 0.001) was obtained between PAI sO₂ and reference sO₂. In canine femoral arteries, PAI achieved an accuracy of 2.16% and an MAE of 1.58% within 8 mm depth (R = 0.965; p < 0.001). In human radial arteries, PAI achieved an accuracy of 3.97% and an MAE of 3.28% in depth from 4 to 14 mm (R = 0.892; p < 0.001). For PAI sO₂ evaluation at different depths in healthy volunteers, the RMSE accuracy of PAI sO₂ increased from 2.66% to 24.96% with depth increasing from 4 to 14 mm. Through the multiscale method, we confirmed the feasibility of the hand-held photoacoustic/ultrasound (PA/US) in evaluating sO₂. These results demonstrate the potential clinical value of PAI in evaluating blood sO₂. Consequently, protocols for verifying the feasibility of medical devices based on PAI may be established.

Mapping the architecture of the temporal artery with photoacoustic imaging for diagnosing giant cell arteritis

Photoacoustic (PA) imaging is rapidly emerging as a promising clinical diagnostic tool. One of the main applications of PA imaging is to image vascular networks in humans. This relies on the signal obtained from oxygenated and deoxygenated hemoglobin, which limits imaging of the vessel wall itself. Giant cell arteritis (GCA) is a treatable, but potentially sight- and life-threatening disease, in which the artery wall is infiltrated by leukocytes. Early intervention can prevent complications making prompt diagnosis of importance. Temporal artery biopsy is the gold standard for diagnosing GCA. We present an approach to imaging the temporal artery using multispectral PA imaging. Employing minimally supervised spectral analysis, we produce histology-like images where the artery wall is clearly discernible from the lumen and further differentiate between PA spectra from biopsies diagnosed as GCA- and GCA+ in 77 patients.

3D Ultrasound-Guided Photoacoustic Imaging to Monitor the Effects of Suboptimal Tyrosine Kinase Inhibitor Therapy in Pancreatic Tumors

Pancreatic cancer is a disease with an incredibly poor survival rate. As only about 20% of patients are eligible for surgical resection, neoadjuvant treatments that can relieve symptoms and shrink tumors for surgical resection become critical. Many forms of treatments rely on increased vulnerability of cancerous cells, but tumors or regions within the tumors that may be hypoxic could be drug resistant. Particularly for neoadjuvant therapies such as the tyrosine kinase inhibitors utilized to shrink tumors, it is critical to monitor changes in vascular function and hypoxia to predict treatment efficacy. Current clinical imaging modalities used to obtain structural and functional information regarding hypoxia or oxygen saturation (StO2) do not provide sufficient depth penetration or require the use of exogenous contrast agents. Recently, ultrasound-guided photoacoustic imaging (US-PAI) has garnered significant popularity, as it can noninvasively provide multiparametric information on tumor vasculature and function without the need for contrast agents. Here, we built upon existing literature on US-PAI and demonstrate the importance of changes in StO2 values to predict treatment response, particularly tumor growth rate, when the outcomes are suboptimal. Specifically, we image xenograft mouse models of pancreatic adenocarcinoma treated with suboptimal doses of a tyrosine kinase inhibitor cabozantinib. We utilize the US-PAI data to develop a multivariate regression model that demonstrates that a therapy-induced reduction in tumor growth rate can be predicted with 100% positive predictive power and a moderate (58.33%) negative predictive power when a combination of pretreatment tumor volume and changes in StO2 values pretreatment and immediately posttreatment was employed. Overall, our study indicates that US-PAI has the potential to provide label-free surrogate imaging biomarkers that can predict tumor growth rate in suboptimal therapy.

Correction: Bulsink et al. Oxygen Saturation Imaging Using LED-Based Photoacoustic System. Sensors 2021, 21, 283

The authors wish to make the following corrections to this paper [...].

Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR.

In-vivo functional and structural retinal imaging using multiwavelength photoacoustic remote sensing microscopy

Many important eye diseases as well as systemic disorders manifest themselves in the retina. Retinal imaging technologies are rapidly growing and can provide ever-increasing amounts of information about the structure, function, and molecular composition of retinal tissue in-vivo. Photoacoustic remote sensing (PARS) is a novel imaging modality based on all-optical detection of photoacoustic signals, which makes it suitable for a wide range of medical applications. In this study, PARS is applied for in-vivo imaging of the retina and estimating oxygen saturation in the retinal vasculature. To our knowledge, this is the first time that a non-contact photoacoustic imaging technique is applied for in-vivo imaging of the retina. Here, optical coherence tomography is also used as a well-established retinal imaging technique to navigate the PARS imaging beams and demonstrate the capabilities of the optical imaging setup. The system is applied for in-vivo imaging of both microanatomy and the microvasculature of the retina. The developed system has the potential to advance the understanding of the ocular environment and to help in monitoring of ophthalmic diseases.

Impact of skin tone on photoacoustic oximetry and tools to minimize bias

The major optical absorbers in tissue are melanin and oxy/deoxy-hemoglobin, but the impact of skin tone and pigmentation on biomedical optics is still not completely understood or adequately addressed. Melanin largely governs skin tone with higher melanin concentration in subjects with darker skin tones. Recently, there has been extensive debate on the bias of pulse oximeters when used with darker subjects. Photoacoustic (PA) imaging can measure oxygen saturation similarly as pulse oximeters and could have value in studying this bias. More importantly, it can deconvolute the signal from the skin and underlying tissue. Here, we studied the impact of skin tone on PA signal generation, depth penetration, and oximetry. Our results show that subjects with darker skin tones exhibit significantly higher PA signal at the skin surface, reduced penetration depth, and lower oxygen saturation compared to subjects with lighter skin tones. We then suggest a simple way to compensate for these signal differences.

A Cost-Efficient Multiwavelength LED-Based System for Quantitative Photoacoustic Measurements

The unique ability of photoacoustic (PA) sensing to provide optical absorption information of biomolecules deep inside turbid tissues with high sensitivity has recently enabled the development of various novel diagnostic systems for biomedical applications. In many cases, PA setups can be bulky, complex, and costly, as they typically require the integration of expensive Q-switched nanosecond lasers, and also presents limited wavelength availability. This article presents a compact, cost-efficient, multiwavelength PA sensing system for quantitative measurements, by utilizing two high-power LED sources emitting at central wavelengths of 444 and 628 nm, respectively, and a single-element ultrasonic transducer at 3.5 MHz for signal detection. We investigate the performance of LEDs in pulsed mode and explore the dependence of PA responses on absorber's concentration and applied energy fluence using tissue-mimicking phantoms demonstrating both optical absorption and scattering properties. Finally, we apply the developed system on the spectral unmixing of two absorbers contained at various relative concentrations in the phantoms, to provide accurate estimations with absolute deviations ranging between 0.4 and 12.3%. An upgraded version of the PA system may provide valuable in-vivo multiparametric measurements of important biomarkers, such as hemoglobin oxygenation, melanin concentration, local lipid content, and glucose levels.

Comparison of photoacoustic imaging and histopathological examination in determining the dimensions of 52 human melanomas and nevi ex vivo

Surgical excision followed by histopathological examination is the gold standard for the diagnosis and staging of melanoma. Reoperations and unnecessary removal of healthy tissue could be reduced if non-invasive imaging techniques were available for presurgical tumor delineation. However, no technique has gained widespread clinical use to date due to shallow imaging depth or the absence of functional imaging capability. Photoacoustic (PA) imaging is a novel technology that combines the strengths of optical and ultrasound imaging to reveal the molecular composition of tissue at high resolution. Encouraging results have been obtained from previous animal and human studies on melanoma, but there is still a lack of clinical data. This is the largest study of its kind to date, including 52 melanomas and nevi. 3D multiwavelength PA scanning was performed ex vivo, using 59 excitation wavelengths from 680 nm to 970 nm. Spectral unmixing over this broad wavelength range, accounting for the absorption of several tissue chromophores, provided excellent contrast between healthy tissue and tumor. Combining the results of spectral analysis with spatially resolved information provided a map of the tumor borders in greater detail than previously reported. The tumor dimensions determined with PA imaging were strongly correlated with those determined by histopathological examination for both melanomas and nevi.

Technical validation studies of a dual-wavelength LED-based photoacoustic and ultrasound imaging system

Recent advances in high power, pulsed, light emitting diodes (LEDs) have shown potential as fast, robust and relatively inexpensive excitation sources for photoacoustic imaging (PAI), yet systematic characterization of performance for biomedical imaging is still lacking. We report here technical and biological validation studies of a commercial dual-wavelength LED-based PAI and ultrasound system. Phantoms and small animals were used to assess temporal precision. In phantom studies, we found high temporal stability of the LED-based PAI system, with no significant drift in performance observed during 6 h of operation or over 30 days of repeated measurements. In vivo dual-wavelength imaging was able to map the dynamics of changes in blood oxygenation during oxygen-enhanced imaging and reveal the kinetics of indocyanine green contrast agent inflow after intravenous administration (Tmax∼6 min). Taken together, these studies indicate that LED-based excitation could be promising for future application in functional and molecular PAI.

Biomedical Photoacoustic Imaging and Sensing Using Affordable Resources

The photoacoustic (PA) effect, also called the optoacoustic effect, was discovered in the 1880s by Alexander Graham Bell and has been utilized for biomedical imaging and sensing applications since the early 1990s [...].