Photoacoustic Imaging System Research Articles

Optimizing quantitative photoacoustic imaging systems: The Bayesian Cramér-Rao bound approach

Abstract Quantitative photoacoustic computed tomography (qPACT) is an emerging medical imaging modality that carries the promise of high-contrast, fine-resolution imaging of clinically relevant quantities like hemoglobin concentration and blood-oxygen saturation. However, qPACT image reconstruction is governed by a multiphysics, partial differential equation (PDE) based inverse problem that is highly non-linear and severely ill-posed. Compounding the di culty of the problem is the lack of established design standards for qPACT imaging systems, as there is currently a proliferation of qPACT system designs for various applications and it is unknown which ones are optimal or how to best modify the systems under various design constraints. This work introduces a novel computational approach for the optimal experimental design (OED) of qPACT imaging systems based on the Bayesian Cramér-Rao bound (CRB). Our approach incorporates several techniques to address challenges associated with forming the bound in the infinite-dimensional function space setting of qPACT, including priors with trace-class covariance operators and the use of the variational adjoint method to compute derivatives of the log-likelihood function needed in the bound computation. The resulting Bayesian CRB based design metric is computationally efficient and independent of the choice of estimator used to solve the inverse problem. The efficacy of the bound in guiding experimental design was demonstrated in a numerical study of qPACT design schemes under a stylized two-dimensional imaging geometry. To the best of our knowledge, this is the first work to propose a Bayesian CRB-based design for systems governed by PDEs.


Clinical application of photoacoustic imaging for cervical precursor lesion detection.

Early diagnosis of a precursor lesion in the uterine cervix is an essential factor in uterine cervical cancer prevention. Although colposcopy is an established procedure for detecting high-risk patients, its accuracy and reproducibility are relatively low. Some supportive or alternative techniques to improve the early diagnosis of a precursor lesion have been studied, and correct diagnosis with high reliability using a minimally invasive, cost-effective technique has been pursued. This study aimed to examine the possibility of using photoacoustic (PA) imaging as a supportive technique to improve the accuracy of early diagnosis of cervical precursor lesions. A PA imaging system for microvessels was used to detect angiogenesis in severe lesions. A total of 21 patients who underwent surgical treatment and 114 outpatients who visited our colposcopy clinic were examined. A retrospective evaluation of PA images was performed as follows: (i) pathological assessment of the specific PA findings and (ii) retrospective evaluation of the severe lesion detection rate through PA. PA image evaluation and pathological findings showed dense angiogenesis in a severe precursor lesion appearing as a "hot spot" in the PA image. A comparison with colposcopy findings was performed for accuracy evaluation, and the detection rate of severe lesions using PA was relatively high (positive predictive value, 84.5%; negative predictive value, 82.1%). Our results indicate the possibility of using PA imaging for early diagnosis of severe cervical precursor lesions. With its ability to yield quantitative information, PA imaging can improve ultrasound diagnosis.

Evaluation of Postoperative Anastomotic Patency in Lymphaticovenular Anastomosis Using Photoacoustic Imaging.

Lymphaticovenular anastomosis (LVA) is a surgical technique used to alleviate lymphedema by bypassing the lymphatic and venous vessels and facilitating lymphatic fluid drainage. Accurate evaluation of anastomotic patency is crucial for assessing LVA outcomes. Traditional near-infrared fluorescence lymphography has limitations, including fluorescence diffusion in subcutaneous fat and difficulty evaluating areas beneath the dermal backflow. Photoacoustic imaging (PAI) is a potential alternative for high-resolution visualization of lymphatic and blood vessels. We aimed to evaluate the utility of PAI for assessing LVA patency. Using the LUB0 PAI system, we examined patients who underwent LVA. Imaging was conducted using subcutaneously injected indocyanine green (ICG) to visualize lymphatic vessels. Results showed clear patency in some cases, inability to evaluate it in others, and confirmed occlusion in certain instances. While PAI provides valuable insights, challenges remain, including the potential for ambiguous results from the intermittent nature of lymphatic flow and difficulty visualizing low-ICG-concentration lymphatic vessels. Nonetheless, PAI offers a promising method for detailed 3D evaluation of anastomoses. It may improve surgical outcomes and contribute to future evidence in the field. Further advancements, including real-time video assessment, may enhance the accuracy and reliability of LVA patency evaluation.

Picosecond photoacoustic generation of ultrasounds with composites of graphene-decorated gold nanoparticles

Photoacoustic or light-to-pressure transducer materials exhibit tremendous potential across various disciplines and applications, embracing theragnostic ultrasounds and permeabilization of biological barriers for drug delivery or for gene transfection. Here, we report the photoacoustic response in the picosecond excitation regime of graphene and gold nanoparticles decorated graphene dispersed in a polydimethylsiloxane polymer matrix. A novel decoration method was developed to nucleate Au nanoparticles of 25 nm on the graphene surface without reducing agent. We observed that the picosecond excitation enforced by the stress confinement generates high-frequency waves, with bandwidths of −6 dB and −20 dB of around 70 MHz and 130 MHz respectively, and peak pressure > 5 MPa. Further, the presence of gold surface decoration leads to a better dispersibility of the flake into the polymer matrix and to an increasing bandwidth at −6 dB up to 85 MHz. The decrease in source thickness leads to an increase in ultrasound frequency, which retains its value even when the laser fluence is increased up to 150 mJ cm –2. This enables the generation of high pressures while keeping the same bandwidth. We experimentally show that the photoacoustic waveform can be tailored by changing the temporal duration of the laser light, the geometry of the acoustic source, and the nature of the graphene absorber. Flexible decoration and fabrication methods of thin films, along with the generation of high-frequency and high-pressure ultrasound waves, offer new perspectives for the use of physical methods to permeabilize biological barriers and develop high-resolution photoacoustic imaging systems.

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.

Research on 3D Reconstruction of Paleontological Fossils Based on Photoacoustic Microscopy

Abstract The focus on three-dimensional morphological analysis of endogenous trace fossils has emerged as a prominent aspect in the study of palaeobiological fossils. Photoacoustic imaging technology, renowned for its deep penetration, high resolution and non-destructiveness, holds considerable promise in exploring fossil morphology. In this work, a solid-coupling-based photoacoustic microscopic imaging system was developed, leveraging this technology to investigate small-scale physical fossils. The proposed system enabled layered imaging of various fossil specimens, achieving a lateral resolution exceeds 35 μm and an axial resolution superior to 8.8815 μm. The three-dimensional reconstruction results were obtained through image processing algorithms. This system was proved to be efficient to imaging the small-scale physical fossils.

Illumination-adjustable photoacoustic and harmonic ultrasound for tracking magnetically driven microrobots.

The development of microrobots for biomedical applications has enabled tasks such as targeted drug delivery, minimally invasive surgeries, and precise diagnostics. However, effective in vivo navigation and control remain challenging due to their small size and complex body environment. Photoacoustic (PA) and ultrasound (US) imaging techniques, which offer high contrast, high resolution, and deep tissue penetration, are integrated to enhance microrobot visualization and tracking. Traditional imaging systems have a narrow effective illumination area, suffer from severe reflection artifacts, and are affected by strong electromagnetic fields. To address this, we present an illumination-adjustable PA and harmonic US imaging system with a customized pushrod mechanism for real-time focus adjustment. Experiments demonstrate high-resolution imaging and accurate microrobot positioning, showcasing the potential for biomedical applications, especially in minimally invasive procedures.

Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.

Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient-derived breast cancer xenograft models and 3D clinical angiograms, VAN-GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN-GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high-quality blood vessel segmentation (F1 score: VAN-GAN vs. U-Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure andfunction.

A practical solution to improve the field of view in circular scanning-based photoacoustic tomography.

One of the primary challenges in ring single-element photoacoustic tomography systems is the low image quality in areas away from the center of the ring. This is mainly due to the limited field of view (FOV) of each transducer, which in turn reduces the imaging FOV. To address this shortcoming, we have put forward a practical and straightforward solution to enhance the FOV of circular scanning-based photoacoustic tomography (CS-PAT). This is accomplished by placing transducers at different angles instead of using a single transducer placed at a normal angle to the imaging target. We also modified the ring scanner inner wall surface to significantly reduce photoacoustic reverberation. By imaging several phantoms, we show a significant improvement in the images generated by our system imaging from 4.1 to over 7 for the signal-to-noise ratio and structural similarity index increased from 41% to 70%.

Photoacoustic imaging in brain disorders: Current progress and clinical applications

AbstractOver the past few decades, the number of patients with neurological diseases has increased significantly, posing huge challenges and opportunities for the development of brain imaging technology. As a hybrid imaging method combining optical excitation and acoustic detection techniques, photoacoustic tomography (PAT), has experienced rapid development, due to high optical contrast and spatial resolution at depth inside tissues. With the development of lasers, ultrasonic detectors, and data computations, PAT has been widely applied for the diagnosis of oncology, dermatosis, etc. However, the energy of light and ultrasound would be greatly attenuated while penetrating the skull, due to the reflection, absorption, and scattering effects, resulting in limited application of PAT in brain imaging. In this review, we summarized the achievements of PAT and its application in the detection of brain diseases including glioma, stroke, traumatic brain injury, Alzheimer's disease, epilepsy, and Parkinson's disease. Moreover, various PAT systems and multi‐modality photoacoustic imaging are introduced for potential clinical applications. Finally, the challenges and current limitations of PAT for further brain imaging are also discussed.

Quantitative evaluation of lower limb varicose veins using photoacoustic imaging

PurposeVaricose veins in the lower extremities are dilated subcutaneous varicose veins with a diameter of ≥ 3 mm, caused by increased venous pressure resulting from backflow of blood due to venous valve insufficiency (Gloviczki in Handbook of venous disorders: guidelines of the American venous forum, Hodder Arnold, London, 2009). When diagnosing varicose veins, the shape and thickness of the blood vessels should be accurately visualized in three dimensions. In this study, we investigated a new method for numerical evaluation of vascular morphology related to varicose veins in the lower extremities, using a photoacoustic imaging (PAI) system, which can acquire high-resolution and three-dimensional images noninvasively.MethodsNine patients with varicose veins participated in the study, and their images were captured using an optical camera and PAI system. We visualized the vascular structure, created a blood presence density (BPD) heat map, and examined the correlation between BPD and location of varicose veins.ResultsThe obtained photoacoustic (PA) images demonstrated the ability of this method to visualize vessels ranging from as small as 0.2 mm in diameter to large, dilated vessels in three dimensions. Furthermore, the study revealed a correlation between the high-density part of the BPD heat map generated from the PAI images and the presence of varicose veins.ConclusionPAI is a promising technique for noninvasive and accurate diagnosis of varicose veins in the lower extremities. By providing valuable information on the morphology and hemodynamics of the varicose veins, PAI may facilitate their early detection and treatment.

Multiwavelength laser diode based portable photoacoustic and ultrasound imaging system for point of care applications.

Vascular diseases are a leading cause of death and disability worldwide. Despite having precursor conditions like peripheral arterial disease (PAD), they are often only diagnosed after the onset of stroke or heart attack. Low-cost, portable, noninvasive, point-of-care (POC), label-free assessment of deep vascular function benefits PAD diagnosis, especially in resource poor settings of the world. Doppler ultrasound-based blood flow measurements can diagnose PAD, albeit with limited sensitivity and specificity. To overcome this, here, we propose the first-of-its-kind dual-modality photoacoustic-and-ultrasound (PAUS) imaging system that integrates a multiwavelength pulsed laser diode (PLD) with a compact ultrasound data acquisition unit. The mesoscopic imaging depth of the portable PLD-PAUS system was validated using tissue phantoms, and its multispectral photoacoustic imaging capabilities were validated using an atherosclerosis-mimicking phantom. Furthermore, we demonstrated high-contrast volumetric in vivo photoacoustic imaging of rodent abdominal vasculature and quantified vessel reactivity due to hypercapnia stimulation. The multiparametric functional and molecular imaging capabilities of the PLD-PAUS system holds promise for POC applications.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system.

Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs). Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner. We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery. We implement a CS PAPI system for a compression factor of 4:3, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results. CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

Image restoration for ring-array photoacoustic tomography system based on blind spatially rotational deconvolution

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

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.

Reconstruction methods for super-resolution imaging with PSF modulation

Recently, a super-resolution imaging method called the PSF (point spread function) modulation method was developed (Lu, IEEE TUFFC 2024). In this method, the amplitude, phase, or both of the PSF of a linear shift-invariant (LSI) imaging system is modulated so that the modulated PSF has a higher spatial frequency than that of the original PSF to reconstruct super-resolution images. The modulator can be produced and manipulated remotely by methods such as radiation force or it can be a physical particle such as micro- or nano-particle manipulated by an external force such as electrical and electromagnetic force. In principle, the super-resolution imaging method can be applied to any LSI imaging system, such as ultrasound, optical, photoacoustic, electromagnetic, underwater, nondestructive evaluation (NDE), and magnetic resonance imaging (MRI) system. These include pulse-echo ultrasound imaging, transmission imaging, wave source/field imaging, acoustical camera, and optical bright-field microscope. To optimize the quality of the images, methods for the reconstruction of pulse-echo and wave source/field super-resolution images are studied and the results will be presented. These methods include the uses of an analytic envelope of radio-frequency (RF) signals with and without windowing, “I” (in-phase) and “Q” (quadrature) signals, and the DC (direct current) component removal.

A multi-aperture encoding scheme for increased SNR in photoacoustic Imaging

Photoacoustic imaging creates light-induced ultrasonic signals to provide valuable information on internal body structures and tissue morphology non-invasively. A multi-aperture photoacoustic imaging (MP-PAI) system is an improvement over conventional photoacoustic imaging (PAI) systems in terms of resolution, contrast, and field of view. Previously, a prototype MP-PAI system was introduced based on multiple capacitive micromachined ultrasound transducers (CMUTs) with shared channels, such that each element in a CMUT shares its channel with its counterpart in other CMUTs. The system uses the biasing voltages of the CMUTs to switch between them and multiplex the received signals in time. Notwithstanding all the enhancements, the signal-to-noise ratio (SNR) remains limited in PAI. To address this issue, we are proposing a multi-aperture encoding scheme (MAES) to further increase the SNR in a multi-aperture PAI system. The proposed method involves receiving signals with multiple CMUTs simultaneously based on an encoding matrix, instead of switching between individual CMUTs. As a result, shared channels contain a superposition of signals, which are later recovered by applying a decoding matrix. Here, an analytical model for computing SNR with an arbitrary encoding sequence is presented, and the method is validated through numerical simulations and in an experimental study. Bipolar and unipolar encoding sequences were considered for the experiments. The numerical results show, in comparison to conventional MP-PAI, that MAES will obtain an SNR gain of 5.8 and 8.8 dB for S-sequence and truncated Hadamard encodings, respectively, when using 15 transducers. In experiments, three transducers are encoded by S-sequences and show 1.5 dB improvement in SNR over conventional MP-PAI method, which aligns well with the analytical model.

Beyond synthetic aperture focusing: deconvolution-based elevation resolution enhancement using simulated point spread function for linear array-based three-dimensional photoacoustic imaging.

This paper introduces a deconvolution-based method to enhance the elevation resolution of a linear array-based three-dimensional (3D) photoacoustic (PA) imaging system. PA imaging combines the high contrast of optical imaging with the deep, multi-centimeter spatial resolution of ultrasound (US) imaging, providing structural and functional information about biological tissues. Linear array-based 3D PA imaging is easily accessible and applicable for ex vivo studies, small animal research, and clinical applications in humans. However, its elevation resolution is limited by the acoustic lens geometry, which establishes a single elevation focus. Previous work used synthetic aperture focusing (SAF) to enhance elevation resolution, but the resolution achievable by SAF is constrained by the size of the elevation focus. Here, we introduce the application of Richardson-Lucy deconvolution, grounded in simulated point-spread-functions, to surpass the elevation resolution attainable with SAF alone. We validated this approach using both simulation and experimental data, demonstrating that the full-width-at-half-maximum of point targets on the elevation plane was reduced compared to using SAF only, suggesting resolution improvement. This method shows promise for improving 3D image quality of existing linear array-based PA imaging systems, offering potential benefits for disease diagnosis and monitoring.

Deep learning framework for three-dimensional surface reconstruction of object of interest in photoacoustic tomography.

Photoacoustic tomography (PAT) is a non-ionizing hybrid imaging technology of clinical importance that combines the high contrast of optical imaging with the high penetration of ultrasonic imaging. Two-dimensional (2D) tomographic images can only provide the cross-sectional structure of the imaging target rather than its overall spatial morphology. This work proposes a deep learning framework for reconstructing three-dimensional (3D) surface of an object of interest from a series of 2D images. It achieves end-to-end mapping from a series of 2D images to a 3D image, visually displaying the overall morphology of the object. The framework consists of four modules: segmentation module, point cloud generation module, point cloud completion module, and mesh conversion module, which respectively implement the tasks of segmenting a region of interest, generating a sparse point cloud, completing sparse point cloud and reconstructing 3D surface. The network model is trained on simulation data sets and verified on simulation, phantom, and in vivo data sets. The results showed superior 3D reconstruction performance both visually and on the basis of quantitative evaluation metrics compared to the state-of-the-art non-learning and learning approaches. This method potentially enables high-precision 3D surface reconstruction from the tomographic images output by the preclinical PAT system without changing the imaging system. It provides a general deep learning scheme for 3D reconstruction from tomographic scanning data.