Real-Time Model-Based Inversion in Cross-Sectional Optoacoustic Tomography

Signal domain adaptation network for limited-view optoacoustic tomography

Optoacoustic (OA) imaging is based on optical excitation of biological tissues with nanosecond-duration laser pulses and detection of ultrasound (US) waves generated by thermoelastic expansion following light absorption. The image quality and fidelity of OA images critically depend on the extent of tomographic coverage provided by the US detector arrays. However, full tomographic coverage is not always possible due to experimental constraints. One major challenge concerns an efficient integration between OA and pulse-echo US measurements using the same transducer array. A common approach toward the hybridization consists in using standard linear transducer arrays, which readily results in arc-type artifacts and distorted shapes in OA images due to the limited angular coverage. Deep learning methods have been proposed to mitigate limited-view artifacts in OA reconstructions by mapping artifactual to artifact-free (ground truth) images. However, acquisition of ground truth data with full angular coverage is not always possible, particularly when using handheld probes in a clinical setting. Deep learning methods operating in the image domain are then commonly based on networks trained on simulated data. This approach is yet incapable of transferring the learned features between two domains, which results in poor performance on experimental data. Here, we propose a signal domain adaptation network (SDAN) consisting of i) a domain adaptation network to reduce the domain gap between simulated and experimental signals and ii) a sides prediction network to complement the missing signals in limited-view OA datasets acquired from a human forearm by means of a handheld linear transducer array. The proposed method showed improved performance in reducing limited-view artifacts without the need for ground truth signals from full tomographic acquisitions.

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging

Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.

A frequency-domain model-based reconstruction method for transcranial photoacoustic imaging: A 2D numerical investigation

Phase aberration caused by the skull is a major barrier to achieving high quality photoacoustic images of human and non-human primates' brains. To address this issue, time-reversal methods have been used but they are computationally demanding and slow due to relying on solving the full-wave equation. The proposed approach is based on model-based image reconstruction in the frequency-domain to achieve near real-time image reconstruction. The relationship between an imaging region and transducer array elements can be mathematically described as a model matrix and the image reconstruction can be performed by pseudo-inverse of the model matrix. The model matrix is numerically calculated due to the lack of analytical solutions for transcranial ultrasound. However, this calculation only needs to be performed once for a given experimental setup and the same acoustic medium, and is an offline process not affecting the actual image reconstruction time. This non-iterative mode-based method demonstrates a substantial improvement in image reconstruction time, being approximately 18 times faster than the time-reversal method, all while maintaining comparable image quality.

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.

Multi-angle data acquisition to compensate transducer finite size in photoacoustic tomography

In photoacoustic tomography (PAT) systems, the tangential resolution decreases due to the finite size of the transducer as the off-center distance increases. To address this problem, we propose a multi-angle detection approach in which the transducer used for data acquisition rotates around its center (with specific angles) as well as around the scanning center. The angles are calculated based on the central frequency and diameter of the transducer and the radius of the region-of-interest (ROI). Simulations with point-like absorbers (for point-spread-function evaluation) and a vasculature phantom (for quality assessment), and experiments with ten 0.5 mm-diameter pencil leads and a leaf skeleton phantom are used for evaluation of the proposed approach. The results show that a location-independent tangential resolution is achieved with 150 spatial sampling and central rotations with angles of ±8°/±16°. With further developments, the proposed detection strategy can replace the conventional detection (rotating a transducer around ROI) in PAT.

Model-Based X-Ray-Induced Acoustic Computed Tomography

X-ray-induced acoustic computed tomography (XACT) provides X-ray absorption-based contrast with acoustic detection. For its clinical translation, XACT imaging often has a limited field of view. This can result in image artifacts and overall loss of quantification accuracy. In this article, we aim to demonstrate model-based XACT image reconstruction to address these problems. An efficient matrix-free implementation of the regularized LSQR (MF-LSQR)-based minimization scheme and a noniterative model back-projection (MBP) scheme for computing XACT reconstructions have been demonstrated in this article. The proposed algorithms have been numerically validated and then used to perform reconstructions from experimental measurements obtained from an XACT setup. While the commonly used back-projection (BP) algorithm produces limited-view and noisy artifacts in the region of interest (ROI), model-based LSQR minimization overcomes these issues. The model-based algorithms also reduce the ring artifacts caused due to the nonuniformity response of the multichannel data acquisition. Using the model-based reconstruction algorithms, we are able to obtain reasonable XACT reconstructions for acoustic measurements of up to 120° view. Although the MBP is more efficient than the model-based LSQR algorithm, it provides only the structural information of the ROI. Overall, it has been demonstrated that the model-based image reconstruction yields better image quality for XACT than the standard BP. Moreover, the combination of model-based image reconstruction with different regularization methods can solve the limited-view problem for XACT imaging (in many realistic cases where the full-view dataset is unavailable), and hence pave the way for future clinical translation.

On the robustness of model-based algorithms for photoacoustic tomography: Comparison between time and frequency domains

For photoacoustic image reconstruction, certain parameters such as sensor positions and speed of sound have a major impact on the reconstruction process and must be carefully determined before data acquisition. Uncertainties in these parameters can lead to errors produced by a modeling mismatch, hindering the reconstruction process and severely affecting the resulting image quality. Therefore, in this work, we study how modeling errors arising from uncertainty in sensor locations affect the images obtained by matrix model-based reconstruction algorithms based on time domain and frequency domain models of the photoacoustic problem. The effects on the reconstruction performance with respect to the uncertainty in the knowledge of the sensors location are compared and analyzed both in a qualitative and quantitative fashion for both time and frequency models. Ultimately, our study shows that the frequency domain approach is more sensitive to this kind of modeling errors. These conclusions are supported by numerical experiments and a theoretical sensitivity analysis of the mathematical operator for the direct problem.

A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data

Multispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. Furthermore, such tools are inflexible, often locking users into a restricted set of processing motifs, which may not be able to accommodate the demands of diverse experiments. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. We demonstrate various use cases, including dynamic imaging challenges and quantification of drug effect, and describe the ability of the toolbox to be parallelized on a high performance computing cluster.

Photoacoustic computed tomography of mechanical HIFU-induced vascular injury

Mechanical high-intensity focused ultrasound (HIFU) has been used for cancer treatment and drug delivery. Existing monitoring methods for mechanical HIFU therapies such as MRI and ultrasound imaging often suffer from high cost, poor spatial-temporal resolution, and/or low sensitivity to tissue's hemodynamic changes. Evaluating vascular injury during mechanical HIFU treatment, therefore, remains challenging. Photoacoustic computed tomography (PACT) is a promising tool to meet this need. Intrinsically sensitive to optical absorption, PACT provides high-resolution imaging of blood vessels using hemoglobin as the endogenous contrast. In this study, we have developed an integrated HIFU-PACT system for detecting vascular rupture in mechanical HIFU treatment. We have demonstrated singular value decomposition for enhancing hemorrhage detection. We have validated the HIFU-PACT performance on phantoms and in vivo animal tumor models. We expect that PACT-HIFU will find practical applications in oncology research using small animal models.

LightSpeed: A Compact, High-Speed Optical-Link-Based 3D Optoacoustic Imager

Wide-scale adoption of optoacoustic imaging in biology and medicine critically depends on availability of affordable scanners combining ease of operation with optimal imaging performance. Here we introduce LightSpeed: a low-cost real-time volumetric handheld optoacoustic imager based on a new compact software-defined ultrasound digital acquisition platform and a pulsed laser diode. It supports the simultaneous signal acquisition from up to 192 ultrasound channels and provides a hig-bandwidth direct optical link (2x 100G Ethernet) to the host-PC for ultra-high frame rate image acquisitions. We demonstrate use of the system for ultrafast (500Hz) 3D human angiography with a rapidly moving handheld probe. LightSpeed attained image quality comparable with a conventional optoacoustic imaging systems employing bulky acquisition electronics and a Q-switched pulsed laser. Our results thus pave the way towards a new generation of compact, affordable and high-performance optoacoustic scanners.

Single laser-shot super-resolution photoacoustic tomography with fast sparsity-based reconstruction

Recently, ℓ 1 -norm based reconstruction approaches have been used with linear array systems to improve photoacoustic resolution and demonstrate undersampled imaging when there is sufficient sparsity in some domain. However, such approaches have yet to beat the half-wavelength resolution limit. In this paper, the ability to beat the half-wavelength diffraction limit is demonstrated using a 5 MHz ring array photoacoustic tomography system and ℓ 1 -norm based reconstruction approaches. We used the array system to image wire targets at ≈ 2 - 3 cm depth in both intralipid scattering solution and water. The minimum observable separation was estimated as 70 ± 10 μ m , improving on the half-wavelength resolution limit of 145 μ m . This improvement was demonstrated even when using a random projection transform to reduce data by 99 % , enabling substantially faster reconstruction times. This is the first photoacoustic tomography approach capable of beating the half-wavelength resolution limit with a single laser shot.

Optoacoustic imaging of the skin

Optoacoustic (OA, photoacoustic) imaging capitalizes on the synergistic combination of light excitation and ultrasound detection to empower biological and clinical investigations with rich optical contrast while effectively bridging the gap between micro and macroscopic imaging realms. State-of-the-art OA embodiments consistently provide images at micron-scale resolution through superficial tissue layers by means of focused illumination that can be smoothly exchanged for acoustic-resolution images at diffuse light depths of several millimetres to centimetres via ultrasound beamforming or tomographic reconstruction. Taken together, this unique multi-scale imaging capacity opens unprecedented capabilities for high-resolution in vivo interrogations of the skin at scalable depths. Moreover, diverse anatomical and functional information is retrieved via dynamic mapping of endogenous chromophores such as haemoglobin, melanin, lipids, collagen, water and others. This, along with the use of non-ionizing radiation, facilitates a clinical translation of the OA modalities. We review recent progress in OA imaging of the skin in preclinical and clinical studies exploiting the rich contrast provided by endogenous substances in tissues. The imaging capabilities of existing approaches are discussed in the context of initial translational studies on skin cancer, inflammatory skin diseases, wounds and other conditions.

Internal-Illumination Photoacoustic Tomography Enhanced by a Graded-Scattering Fiber Diffuser

The penetration depth of photoacoustic imaging in biological tissues has been fundamentally limited by the strong optical attenuation when light is delivered externally through the tissue surface. To address this issue, we previously reported internal-illumination photoacoustic imaging using a customized radial-emission optical fiber diffuser, which, however, has complex fabrication, high cost, and non-uniform light emission. To overcome these shortcomings, we have developed a new type of low-cost fiber diffusers based on a graded-scattering method in which the optical scattering of the fiber diffuser is gradually increased as the light travels. The graded scattering can compensate for the optical attenuation and provide relatively uniform light emission along the diffuser. We performed Monte Carlo numerical simulations to optimize several key design parameters, including the number of scattering segments, scattering anisotropy factor, divergence angle of the optical fiber, and reflective index of the surrounding medium. These optimized parameters collectively result in uniform light emission along the fiber diffuser and can be flexibly adjusted to accommodate different applications. We fabricated and characterized the prototype fiber diffuser made of agarose gel and intralipid. Equipped with the new fiber diffuser, we performed thorough proof-of-concept studies on ex vivo tissue phantoms and an in vivo swine model to demonstrate the deep-imaging capability (~10 cm achieved ex vivo) of photoacoustic tomography. We believe that the internal light delivery via the optimized fiber diffuser is an effective strategy to image deep targets (e.g., kidney) in large animals or humans.

Cross-sectional photoacoustic tomography image reconstruction with a multi-curve integration model

BACKGROUND AND OBJECTIVE

In acoustic inversion of photoacoustic tomography (PAT), an imaging model that precisely describes both the ultrasonic wave propagation and the detector properties is of crucial importance. Inspired by the multi-stripe integration model in clinical X-ray computed tomography systems, in this work, we introduce the Multi-Curve-Integration-based acoustic inversion for cross-sectional Photoacoustic Tomography (MCI-PAT).

METHODS

We assumed that in cross-sectional PAT system, the three-dimensional (3-D) wave propagation problem could be reduced to a two-dimensional (2-D) problem in a limited, yet sufficient field of view. Under such condition, the MCI-PAT imaging model is generated by integrating several circular acoustic curves, the centers of which are points evenly distributed on the finite-size ultrasonic transducer surface. In this way, the spatial detector response is taken into account, while its computational burden does not largely increase because the integration process is performed only on a 2-D plane.

RESULTS

As proven by simulation, phantom and in vivo small animal experiments, the MCI-PAT method leads to promising improvement in PAT image quality. Comparing to traditional imaging models that considered only a single acoustic curve, our proposed method successfully improved the visibility of small structures and achieved evident enhancement on signal-to-noise ratio.

CONCLUSIONS

The performance of the MCI-PAT method demonstrates that for cross-sectional PAT, a 2-D simplification of the propagation of multiple photoacoustic waves is feasible. Due to its simplicity, our method can be used as an add-on to current system models considering only a single acoustic curve.

Compressed Optoacoustic Sensing of Volumetric Cardiac Motion

The recently developed optoacoustic tomography systems have attained volumetric frame rates exceeding 100 Hz, thus opening up new venues for studying previously invisible biological dynamics. Further gains in temporal resolution can potentially be achieved via partial data acquisition, though a priori knowledge on the acquired data is essential for rendering accurate reconstructions using compressed sensing approaches. In this work, we suggest a machine learning method based on principal component analysis for high-frame-rate volumetric cardiac imaging using only a few tomographic optoacoustic projections. The method is particularly effective for discerning periodic motion, as demonstrated herein by non-invasive imaging of a beating mouse heart. A training phase enables efficiently compressing the heart motion information, which is subsequently used as prior information for image reconstruction from sparse sampling at a higher frame rate. It is shown that image quality is preserved with a 64-fold reduction in the data flow. We demonstrate that, under certain conditions, the volumetric motion could effectively be captured by relying on time-resolved data from a single optoacoustic detector. Feasibility of capturing transient (non-periodic) events not registered in the training phase is further demonstrated by visualizing perfusion of a contrast agent in vivo. The suggested approach can be used to significantly boost the temporal resolution of optoacoustic imaging and facilitate development of more affordable and data efficient systems.

Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.

Model-Based Reconstruction of Large Three-Dimensional Optoacoustic Datasets

Iterative model-based algorithms are known to enable more accurate and quantitative optoacoustic (photoacoustic) tomographic reconstructions than standard back-projection methods. However, three-dimensional (3D) model-based inversion is often hampered by high computational complexity and memory overhead. Parallel implementations on a graphics processing unit (GPU) have been shown to efficiently reduce the memory requirements by on-the-fly calculation of the actions of the optoacoustic model matrix, but the high complexity still makes these approaches impractical for large 3D optoacoustic datasets. Herein, we show that the computational complexity of 3D model-based iterative inversion can be significantly reduced by splitting the model matrix into two parts: one maximally sparse matrix containing only one entry per voxel-transducer pair and a second matrix corresponding to cyclic convolution. We further suggest reconstructing the images by multiplying the transpose of the model matrix calculated in this manner with the acquired signals, which is equivalent to using a very large regularization parameter in the iterative inversion method. The performance of these two approaches is compared to that of standard back-projection and a recently introduced GPU-based model-based method using datasets from in vivo experiments. The reconstruction time was accelerated by approximately an order of magnitude with the new iterative method, while multiplication with the transpose of the matrix is shown to be as fast as standard back-projection.

Toroidally focused sensor array for real-time laser-ultrasonic imaging: The first experimental study

In this paper we report on the first toroidally focused 2D real-time laser-ultrasonic imaging system and a modified filtered back projection algorithm that can be used in the region near the waist of the astigmatic laser-ultrasonic probe beam. The system is capable of visualizing an acupuncture needle 0.2 mm in diameter located at ∼4 cm depth in water. The lateral spatial resolution is better than ∼0.32 mm and axial spatial resolution is ∼30 μm. The achieved frame rate is up to 30 Hz. The depth dependency of the sensitivity region width and lateral resolution are experimentally measured and discussed. The array is intended to be used as a part of combined real-time photoacoustic and laser-ultrasonic imaging system.

The Impulse Response of Negatively Focused Spherical Ultrasound Detectors and Its Effect on Tomographic Optoacoustic Reconstruction

In optoacoustic tomography, negatively focused detectors have been shown to improve the tangential image resolution without sacrificing sensitivity. Since no exact inversion formulas exist for optoacoustic image reconstruction with negatively focused detectors, image reconstruction in such cases is based on using the virtual-detector approximation, in which it is assumed that the response of the negatively focused detector is identical, up to a constant time delay, to that of a point-like detector positioned in the detector's center of curvature. In this paper, we analyze the response of negatively focused spherical ultrasound detectors in three dimensions and demonstrate how their properties affect the optoacoustic reconstruction. Our analysis sheds new light on commonly reported experimental reconstruction artifacts in optoacoustic systems that employ negatively focused detectors. Based on our analysis, we introduce a simple correction to the virtual-detector approximation that significantly enhances image contrast and reduces artifacts.

Optoacoustic image formation approaches-a clinical perspective

Clinical translation of optoacoustic imaging is fostered by the rapid technical advances in imaging performance as well as the growing number of clinicians recognizing the immense diagnostic potential of this technology. Clinical optoacoustic systems are available in multiple configurations, including hand-held and endoscopic probes as well as raster-scan approaches. The hardware design must be adapted to the accessible portion of the imaged region and other application-specific requirements pertaining the achievable depth, field of view or spatio-temporal resolution. Equally important is the adequate choice of the signal and image processing approach, which is largely responsible for the resulting imaging performance. Thus, new image reconstruction algorithms are constantly evolving in parallel to the newly-developed set-ups. This review focuses on recent progress on optoacoustic image formation algorithms and processing methods in the clinical setting. Major reconstruction challenges include real-time image rendering in two and three dimensions, efficient hybridization with other imaging modalitites as well as accurate interpretation and quantification of bio-markers, herein discussed in the context of ongoing progress in clinical translation.

GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays

Double-stage delay-multiply-and-sum (DS-DMAS) is an algorithm proposed for photoacoustic image reconstruction. The DS-DMAS algorithm offers a higher contrast than conventional delay-and-sum and delay-multiply and-sum but at the expense of higher computational complexity. Here, we utilized a compute unified device architecture (CUDA) graphics processing unit (GPU) parallel computation approach to address the high complexity of the DS-DMAS for photoacoustic image reconstruction generated from a commercial light-emitting diode (LED)-based photoacoustic scanner. In comparison with a single-threaded central processing unit (CPU), the GPU approach increased speeds by nearly 140-fold for 1024 × 1024 pixel image; there was no decrease in accuracy. The proposed implementation makes it possible to reconstruct photoacoustic images with frame rates of 250, 125, and 83.3 when the images are 64 × 64, 128 × 128, and 256 × 256, respectively. Thus, DS-DMAS can be efficiently used in clinical devices when coupled with CUDA GPU parallel computation.

Finite-element reconstruction of 2D circular scanning photoacoustic tomography with detectors in far-field condition

The finite-element method (FEM) has been a powerful numerical tool for the reconstruction of 2D circular scanning-based photoacoustic tomography (PAT) for its unrivaled ability to accommodate complex boundary conditions, quantitatively reconstruct different physical parameters, and enable low sampling frequency and fewer detector numbers. To reduce the computation cost, a smaller image domain is commonly used instead of the region surrounded by the transducer scanning trace. Then, the pressure data used for the reconstruction that is defined on the boundary of the image domain is usually obtained by directly time delaying the actual measured data. In this case, distortions will be aroused for targets that are away from the rotation center. In this work, we put forward a new data preprocessing method to overcome this problem with a virtual detector concept, in which the measured data for the virtual point detectors on the boundary of the reconstruction domain are generated by a summation of the signals from nearby true detectors. The complete removal of the distortions using our proposed algorithm was proven with experimental reconstruction results.

Three dimensional photoacoustic tomography in Bayesian framework

The image reconstruction problem (or inverse problem) in photoacoustic tomography is to resolve the initial pressure distribution from detected ultrasound waves generated within an object due to an illumination by a short light pulse. Recently, a Bayesian approach to photoacoustic image reconstruction with uncertainty quantification was proposed and studied with two dimensional numerical simulations. In this paper, the approach is extended to three spatial dimensions and, in addition to numerical simulations, experimental data are considered. The solution of the inverse problem is obtained by computing point estimates, i.e., maximum a posteriori estimate and posterior covariance. These are computed iteratively in a matrix-free form using a biconjugate gradient stabilized method utilizing the adjoint of the acoustic forward operator. The results show that the Bayesian approach can produce accurate estimates of the initial pressure distribution in realistic measurement geometries and that the reliability of these estimates can be assessed.

Tomographic breathing detection: a method to noninvasively assess in situ respiratory dynamics

Physiological monitoring is a critical aspect of in vivo experimentation, particularly imaging studies. Physiological monitoring facilitates gated acquisition of imaging data and more robust experimental interpretation but has historically required additional instrumentation that may be cumbersome. As frame rates have increased, imaging methods have been able to capture ever more rapid dynamics, passing the Nyquist sampling rate of most physiological processes and allowing the capture of motion, such as breathing. With this transition, image artifacts have also changed their nature; rather than intraframe motion causing blurring and deteriorating resolution, interframe motion does not affect individual frames and may be recovered as useful information from an image time series. We demonstrate a method that takes advantage of interframe movement for detection of gross physiological motion in real-time image sequences. We further demonstrate the ability of the method, dubbed tomographic breathing detection to quantify the dynamics of respiration, allowing the capture of respiratory information pertinent to anesthetic depth monitoring. Our example uses multispectral optoacoustic tomography, but it will be widely relevant to other technologies.

Dual-foci detection in photoacoustic computed tomography with coplanar light illumination and acoustic detection: a phantom study

A dual-foci transducer with coplanar light illumination and acoustic detection was applied for the first time. It overcame the small directivity angle, low-sensitivity, and large datasets in conventional circular scanning or array-based photoacoustic computed tomography (PACT). The custom-designed transducer is focused on both the scanning plane with virtual-point detection and the elevation direction for large field of view (FOV) cross-sectional imaging. Moreover, a coplanar light illumination and acoustic detection configuration can provide ring-shaped light irradiation with highly efficient acoustic detection, which in principle has a better adaptability when imaging samples of irregular surfaces. Phantom experiments showed that our PACT system can achieve high resolution (∼0.5  mm), enhanced signal-to-noise ratio (16-dB improvement), and a more complete structure in a greater FOV with an equal number of sampling points compared with the results from a flat aperture transducer. This study provides the proof of concept for the fabrication of a sparse array with the dual-foci property and large aperture size for high-quality, low-cost, and high-speed photoacoustic imaging.

Optoacoustic imaging at kilohertz volumetric frame rates

State-of-the-art optoacoustic tomographic imaging systems have been shown to attain three-dimensional (3D) frame rates of the order of 100 Hz. While such a high volumetric imaging speed is beyond reach for other bio-imaging modalities, it may still be insufficient to accurately monitor some faster events occurring on a millisecond scale. Increasing the 3D imaging rate is usually hampered by the limited throughput capacity of the data acquisition electronics and memory used to capture vast amounts of the generated optoacoustic (OA) data in real time. Herein, we developed a sparse signal acquisition scheme and a total-variation-based reconstruction approach in a combined space-time domain in order to achieve 3D OA imaging at kilohertz rates. By continuous monitoring of freely swimming zebrafish larvae in a 3D region, we demonstrate that the new approach enables significantly increasing the volumetric imaging rate by using a fraction of the tomographic projections without compromising the reconstructed image quality. The suggested method may benefit studies looking at ultrafast biological phenomena in 3D, such as large-scale neuronal activity, cardiac motion, or freely behaving organisms.

All-optical photoacoustic projection imaging

We introduce all-optical photoacoustic projection imaging. An array of fiber-optic interferometers is used to measure photoacoustic signals. The obtained images represent the projection of the three-dimensional spatial light absorbance within a sample onto a two-dimensional plane. We assess the performance of the system by phantom measurements and show that the fiber-optic detectors achieve a noise-equivalent pressure of 24 Pascal at a 10 MHz bandwidth. Furthermore, we demonstrate the ability to acquire high-resolution projection images of large volumes within a short period of time.

Efficient 3-D Model-Based Reconstruction Scheme for Arbitrary Optoacoustic Acquisition Geometries

Optimal optoacoustic tomographic sampling is often hindered by the frequency-dependent directivity of ultrasound sensors, which can only be accounted for with an accurate 3-D model. Herein, we introduce a 3-D model-based reconstruction method applicable to optoacoustic imaging systems employing detection elements with arbitrary size and shape. The computational complexity and memory requirements are mitigated by introducing an efficient graphic processing unit (GPU)-based implementation of the iterative inversion. On-the-fly calculation of the entries of the model-matrix via a small look-up table avoids otherwise unfeasible storage of matrices typically occupying more than 300GB of memory. Superior imaging performance of the suggested method with respect to standard optoacoustic image reconstruction methods is first validated quantitatively using tissue-mimicking phantoms. Significant improvements in the spatial resolution, contrast to noise ratio and overall 3-D image quality are also reported in real tissues by imaging the finger of a healthy volunteer with a hand-held volumetric optoacoustic imaging system.

Three-dimensional optoacoustic reconstruction using fast sparse representation

Optoacoustic tomography based on insufficient spatial sampling of ultrasound waves leads to loss of contrast and artifacts on the reconstructed images. Compared to reconstructions based on L2-norm regularization, sparsity-based reconstructions may improve contrast and reduce image artifacts but at a high computational cost, which has so far limited their use to 2D optoacoustic tomography. Here we propose a fast, sparsity-based reconstruction algorithm for 3D optoacoustic tomography, based on gradient descent with Barzilai-Borwein line search (L1-GDBB). Using simulations and experiments, we show that the L1-GDBB offers fourfold faster reconstruction than the previously reported L1-norm regularized reconstruction based on gradient descent with backtracking line search. Moreover, the new algorithm provides higher-quality images with fewer artifacts than the L2-norm regularized reconstruction and the back-projection reconstruction.

Regularized Iterative Weighted Filtered Back-Projection for Few-View Data Photoacoustic Imaging

Photoacoustic imaging is an emerging noninvasive imaging technique with great potential for a wide range of biomedical imaging applications. However, with few-view data the filtered back-projection method will create streak artifacts. In this study, the regularized iterative weighted filtered back-projection method was applied to our photoacoustic imaging of the optical absorption in phantom from few-view data. This method is based on iterative application of a nonexact 2DFBP. By adding a regularization operation in the iterative loop, the streak artifacts have been reduced to a great extent and the convergence properties of the iterative scheme have been improved. Results of numerical simulations demonstrated that the proposed method was superior to the iterative FBP method in terms of both accuracy and robustness to noise. The quantitative image evaluation studies have shown that the proposed method outperforms conventional iterative methods.

Optoacoustic Tomography Using Accelerated Sparse Recovery and Coherence Factor Weighting

Sparse recovery algorithms have shown great potential to accurately reconstruct images using limited-view optoacoustic (photoacoustic) tomography data sets, but these are computationally expensive. In this paper, we propose an improvement of the fast converging Split Augmented Lagrangian Shrinkage Algorithm method based on least square QR inversion for improving the reconstruction speed. We further show image fidelity improvement when using a coherence factor to weight the reconstruction result. Phantom and in vivo measurements show that the accelerated Split Augmented Lagrangian Shrinkage Algorithm method with coherence factor weighting offers images with reduced artifacts and provides faster convergence compared with existing sparse recovery algorithms.