Multi-Scale Volumetric Dynamic Optoacoustic and Laser Ultrasound (OPLUS) Imaging Enabled by Semi-Transparent Optical Guidance.

Major biological discoveries are made by interrogating living organisms with light. However, the limited penetration of un-scattered photons within biological tissues limits the depth range covered by optical methods. Deep-tissue imaging is achieved by combining light and ultrasound. Optoacoustic imaging exploits the optical generation of ultrasound to render high-resolution images at depths unattainable with optical microscopy. Recently, laser ultrasound has been suggested as a means of generating broadband acoustic waves for high-resolution pulse-echo ultrasound imaging. Herein, an approach is proposed to simultaneously interrogate biological tissues with light and ultrasound based on layer-by-layer coating of silica optical fibers with a controlled degree of transparency. The time separation between optoacoustic and ultrasound signals collected with a custom-made spherical array transducer is exploited for simultaneous 3D optoacoustic and laser ultrasound (OPLUS) imaging with a single laser pulse. OPLUS is shown to enable large-scale anatomical characterization of tissues along with functional multi-spectral imaging of chromophores and assessment of cardiac dynamics at ultrafast rates only limited by the pulse repetition frequency of the laser. The suggested approach provides a flexible and scalable means for developing a new generation of systems synergistically combining the powerful capabilities of optoacoustics and ultrasound imaging in biology and medicine.

Full-view LED-based optoacoustic tomography.

Optoacoustic tomography is commonly performed with bulky and expensive short-pulsed solid-state lasers providing high per-pulse energies in the millijoule range. Light emitting diodes (LEDs) represent a cost-effective and portable alternative for optoacoustic signal excitation that can additionally provide excellent pulse-to-pulse stability. Herein, we introduce a full-view LED-based optoacoustic tomography (FLOAT) system for deep tissue in vivo imaging. It is based on a custom-made electronic unit driving a stacked array of LEDs, which attains 100 ns pulse width and highly stable (0.62 % standard deviation) total per-pulse energy of 0.48 mJ. The illumination source is integrated into a circular array of cylindrically-focused ultrasound detection elements to result in a full-view tomographic configuration, which plays a critical role in circumventing limited-view effects, enhancing the effective field-of-view and image quality for cross-sectional (2D) imaging. We characterized the FLOAT performance in terms of pulse width, power stability, excitation light distribution, signal-to-noise and penetration depth. FLOAT of the human finger revealed a comparable imaging performance to that achieved with the standard pulsed Nd:YAG laser. It is anticipated that this compact, affordable and versatile illumination technology will facilitate optoacoustic imaging developments in resource-limited settings for biological and clinical applications.

Spiral volumetric optoacoustic tomography for imaging whole-body biodynamics in small animals.

Fast tracking of biological dynamics across multiple murine organs using the currently commercially available whole-body preclinical imaging systems is hindered by their limited contrast, sensitivity and spatial or temporal resolution. Spiral volumetric optoacoustic tomography (SVOT) provides optical contrast, with an unprecedented level of spatial and temporal resolution, by rapidly scanning a mouse using spherical arrays, thus overcoming the current limitations in whole-body imaging. The method enables the visualization of deep-seated structures in living mammalian tissues in the near-infrared spectral window, while further providing unrivalled image quality and rich spectroscopic optical contrast. Here, we describe the detailed procedures for SVOT imaging of mice and provide specific details on how to implement a SVOT system, including component selection, system arrangement and alignment, as well as the image processing methods. The step-by-step guide for the rapid panoramic (360°) head-to-tail whole-body imaging of a mouse includes the rapid visualization of contrast agent perfusion and biodistribution. The isotropic spatial resolution possible with SVOT can reach 90 µm in 3D, while alternative steps enable whole-body scans in less than 2 s, unattainable with other preclinical imaging modalities. The method further allows the real-time (100 frames per second) imaging of biodynamics at the whole-organ level. The multiscale imaging capacity provided by SVOT can be used for visualizing rapid biodynamics, monitoring responses to treatments and stimuli, tracking perfusion, and quantifying total body accumulation and clearance dynamics of molecular agents and drugs. Depending on the imaging procedure, the protocol requires 1-2 h to complete by users trained in animal handling and biomedical imaging.

Head-to-tail imaging of mice with spiral volumetric optoacoustic tomography.

Optoacoustic tomography has been established as a powerful modality for preclinical imaging. However, efficient whole-body imaging coverage has not been achieved owing to the arduous requirement for continuous acoustic coupling around the animal. In this work, we introduce panoramic (3600) head-to-tail 3D imaging of mice with spiral volumetric optoacoustic tomography (SVOT). The system combines multi-beam illumination and a dedicated head holder enabling uninterrupted acoustic coupling for whole-body scans. Image fidelity is optimized with self-gated respiratory motion rejection and dual speed-of-sound reconstruction algorithms to attain spatial resolution down to 90 µm. The developed system is thus highly suitable for visualizing rapid biodynamics across scales, such as hemodynamic changes in individual organs, responses to treatments and stimuli, perfusion, total body accumulation, or clearance of molecular agents and drugs with unmatched contrast, spatial and temporal resolution.

Real-time 3D optoacoustic tracking of cell-sized magnetic microrobots circulating in the mouse brain vasculature.

Mobile microrobots hold remarkable potential to revolutionize health care by enabling unprecedented active medical interventions and theranostics, such as active cargo delivery and microsurgical manipulations in hard-to-reach body sites. High-resolution imaging and control of cell-sized microrobots in the in vivo vascular system remains an unsolved challenge toward their clinical use. To overcome this limitation, we propose noninvasive real-time detection and tracking of circulating microrobots using optoacoustic imaging. We devised cell-sized nickel-based spherical Janus magnetic microrobots whose near-infrared optoacoustic signature is enhanced via gold conjugation. The 5-, 10-, and 20-µm-diameter microrobots are detected volumetrically both in bloodless ex vivo tissues and under real-life conditions with a strongly light-absorbing blood background. We further demonstrate real-time three-dimensional tracking and magnetic manipulation of the microrobots circulating in murine cerebral vasculature, thus paving the way toward effective and safe operation of cell-sized microrobots in challenging and clinically relevant intravascular environments.

Rapid Volumetric Optoacoustic Tracking of Nanoparticle Kinetics across Murine Organs.

Large-scale visualization of nanoparticle kinetics is essential for optimizing drug delivery and characterizing in vivo toxicity associated with engineered nanomaterials. Real-time tracking of nanoparticulate agents across multiple murine organs is hindered with the currently available whole-body preclinical imaging systems due to limitations in contrast, sensitivity, spatial, or temporal resolution. Herein, we demonstrate rapid volumetric tracking of gold nanoagent kinetics and biodistribution in mice at a suborgan level with single-sweep volumetric optoacoustic tomography (sSVOT). The imaging system accomplishes whole-body three-dimensional scans in less than 1.8 s, further attaining a high spatial resolution of 130 µm and sub-picomolar sensitivity. We visualized the clearance dynamics of purposely synthesized gold nanorods and nanorod clusters, featuring different sizes and surface chemistries as well as their corresponding accumulation within the liver and spleen. The newly discovered capacity to image rapid whole-body kinetics down to suborgan scales opens up new avenues for the development and characterization of diagnostic and therapeutic nanoagents.

Rapid Volumetric Optoacoustic Tracking of Individual Microparticles In Vivo Enabled by a NIR-Absorbing Gold-Carbon Shell.

Rapid volumetric in vivo visualization of circulating microparticles can facilitate new biomedical applications, such as blood flow characterization or targeted drug delivery. However, existing imaging modalities generally lack the sensitivity to detect the weak signals generated by individual micrometer-sized particles distributed across millimeter- to centimeter-scale depths in living mammalian tissues. Also, the temporal resolution is typically insufficient to track the particles in an entire three-dimensional region. Herein, we introduce a new type of monodisperse (4 µm) silica-core microparticle coated with a shell formed by a multilayered structure of carbon nanotubes (CNT) and gold nanoparticles (AuNP) to provide strong optoacoustic (OA) absorption-based contrast. We capitalize on the unique advantages of a state-of-the-art high-frame-rate OA tomography system to visualize and track the motion of these core-shell particles individually and volumetrically as they flow throughout the mouse brain vasculature. The feasibility of localizing individual solid particles smaller than red blood cells opens new opportunities for mapping the blood flow velocity, enhancing the resolution and visibility of OA images, and developing new biosensing assays.

Binary photoacoustic tomography for improved vasculature imaging.

SIGNIFICANCE: The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs. AIM: Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature. We propose a binary PAT approach wherein direct reconstruction of vascular network from the acquired photoacoustic sinogram data is plausible. APPROACH: Binary tomography approach relies on solving a dual-optimization problem to reconstruct images with every pixel resulting in a binary outcome (i.e., either background or the absorber). Further, the binary tomography approach was compared against backprojection, Tikhonov regularization, and sparse recovery-based schemes. RESULTS: Numerical simulations, physical phantom experiment, and in-vivo rat brain vasculature data were used to compare the performance of different algorithms. The results indicate that the binary tomography approach improved the vasculature recovery by 10% using in-silico data with respect to the Dice similarity coefficient against the other reconstruction methods. CONCLUSION: The proposed algorithm demonstrates superior vasculature recovery with limited data both visually and based on quantitative image metrics.

Non-local means improves total-variation constrained photoacoustic image reconstruction.

Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the model-based schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images.

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography.

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat.

Pulsed Laser Diode-Based Desktop Photoacoustic Tomography for Monitoring Wash-In and Wash-Out of Dye in Rat Cortical Vasculature.

Photoacoustic (PA) tomography (PAT) imaging is an emerging biomedical imaging modality useful in various preclinical and clinical applications. Custom-made circular ring array-based transducers and conventional bulky Nd:YAG/OPO lasers inhibit translation of the PAT system to clinics. Ultra-compact pulsed laser diodes (PLDs) are currently being used as an alternative source of near-infrared excitation for PA imaging. High-speed dynamic in vivo imaging has been demonstrated using a compact PLD-based desktop PAT system (PLD-PAT). A visualized experimental protocol using the desktop PLD-PAT system is provided in this work for dynamic in vivo brain imaging. The protocol describes the desktop PLD-PAT system configuration, preparation of animal for brain vascular imaging, and procedure for dynamic visualization of indocyanine green (ICG) dye uptake and clearance process in rat cortical vasculature.

PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics.

The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental set-ups as well as imaging objects.

High-speed, low-cost, pulsed-laser-diode-based second-generation desktop photoacoustic tomography system.

Bulky, expensive Nd:YAG lasers are used in conventional photoacoustic tomography (PAT) systems, making them difficult to translate into clinics. Moreover, real-time imaging is not feasible when a single-element ultrasound transducer is used with these low-pulse-repetition-rate lasers (10-100 Hz). Low-cost pulsed laser diodes (PLDs) can be used instead for photoacoustic imaging due to their high-pulse-repetition rates and compact size. Together with acoustic-reflector-based multiple single-element ultrasound transducers, a portable desktop PAT system was developed. This second-generation PLD-based PAT achieved 0.5 s cross-sectional imaging time with high spatial resolution of ∼165 µm and an imaging depth of 3 cm. The performance of this system was characterized using phantom and in vivo studies. Dynamic in vivo imaging was also demonstrated by monitoring the fast uptake and clearance of indocyanine green in small animal (rat) brain vasculature.

Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction.

Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, l1 -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This paper introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, l1 -norm, and total-variation regularization schemes. Moreover, an automated method for choosing the fractional power was also proposed. It was shown theoretically and with numerical experiments that the fractional power is inversely related to the data noise level for fractional Tikhonov scheme. The fractional framework outperforms the standard regularization schemes, Tikhonov, l1 -norm, and total-variation by 54% in numerical simulations, experimental phantoms, and in vivo rat data in terms of observed contrast/signal-to-noise-ratio of the reconstructed images.

Spatially variant regularization based on model resolution and fidelity embedding characteristics improves photoacoustic tomography.

Photoacoustic tomography tends to be an ill-conditioned problem with noisy limited data requiring imposition of regularization constraints, such as standard Tikhonov (ST) or total variation (TV), to reconstruct meaningful initial pressure rise distribution from the tomographic acoustic measurements acquired at the boundary of the tissue. However, these regularization schemes do not account for nonuniform sensitivity arising due to limited detector placement at the boundary of tissue as well as other system parameters. For the first time, two regularization schemes were developed within the Tikhonov framework to address these issues in photoacoustic imaging. The model resolution, based on spatially varying regularization, and fidelity-embedded regularization, based on orthogonality between the columns of system matrix, were introduced. These were systematically evaluated with the help of numerical and in-vivo mice data. It was shown that the performance of the proposed spatially varying regularization schemes were superior (with at least 2 dB or 1.58 times improvement in the signal-to-noise ratio) compared to ST-/TV-based regularization schemes.

Image-guided filtering for improving photoacoustic tomographic image reconstruction.

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography.

PURPOSE: Development of simple and computationally efficient extrapolated Tikhonov filtering reconstruction methods for photoacoustic tomography. METHODS: The model-based reconstruction algorithms in photoacoustic tomography typically utilize Tikhonov regularization scheme for the reconstruction of initial pressure distribution from the measured boundary acoustic data. The automated choice of regularization parameter in these cases is computationally expensive. Moreover, the Tikhonov scheme promotes the smooth features in the reconstructed image due to the smooth regularizer, thus leading to loss of sharp features. The proposed extrapolation method estimates the solution at zero regularization assuming the solution being a function of regularization parameter and thus posing it as a zero value problem. Thus, the numerically computed zero regularization solution is expected to have better features compared to standard Tikhonov solution, with an added advantage of removing the necessity of automated choice of regularization. The reconstructed results using this method were shown in three variants (Lanczos, traditional, and exponential) of Tikhonov filtering and were compared with the standard error estimate technique. RESULTS: Four numerical (including realistic breast phantom) and two experimental phantom data were utilized to show the effectiveness of the proposed method. It was shown that the proposed method performance was superior than the standard error estimate technique, being at least four times faster in terms of computation, and provides an improvement as high as 2.6 times in terms of standard figures of merit. CONCLUSION: The developed extrapolated Tikhonov filtering methods overcome the difficulty of obtaining a suitable regularization parameter and shown to be reconstructing high-quality photoacoustic images with additional advantage of being computationally efficient, making it more appealing in real-time applications.

Calibrating reconstruction radius in a multi single-element ultrasound-transducer-based photoacoustic computed tomography system.

In a circular scanning photoacoustic computed tomography (PAT/PACT) system, a single-element ultrasound transducer (SUT) (rotates in full 360° around the sample) or a full-ring array transducer is used to acquire the photoacoustic (PA) data from the target object. SUT takes several minutes to acquire the PA data, whereas the full-ring array transducer takes only few seconds. Hence, for real-time imaging, full-ring circular array transducers are preferred. However, these are custom built, very expensive, and not available readily on the market, whereas SUTs are cheap and easily available. Thus, PACT systems can be made cost effective by using SUTs. To improve the data acquisition speed, multiple SUTs can be employed at the same time. This will reduce the acquisition time by N-fold if N numbers of SUTs are used, each rotating 360/N degrees. Experimentally, all SUTs cannot be placed exactly at the same distance from the scanning center. Hence, the acquired PA data from each transducer need to be reconstructed with their corresponding radii in a delay-and-sum reconstruction algorithm. This requires the exact location of each SUT from the scanning center. Here, we propose a calibration method to find out the distance from the scanning center at which each SUT acquires the PA data. Three numerical phantoms were used to show the efficacy of the proposed method, and later it was validated with experimental data (point source phantom).

Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography.

As limited data photoacoustic tomographic image reconstruction problem is known to be ill-posed, the iterative reconstruction methods were proven to be effective in terms of providing good quality initial pressure distribution. Often, these iterative methods require a large number of iterations to converge to a solution, in turn making the image reconstruction procedure computationally inefficient. In this work, two variants of vector polynomial extrapolation techniques were deployed to accelerate two standard iterative photoacoustic image reconstruction algorithms, including regularized steepest descent and total variation regularization methods. It is shown using numerical and experimental phantom cases that these extrapolation methods that are proposed in this work can provide significant acceleration (as high as 4.7 times) along with added advantage of improving reconstructed image quality.

Deep neural network-based bandwidth enhancement of photoacoustic data.

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden.