Photoacoustic Computed Tomography Research Articles

Oxygenation heterogeneity facilitates spatiotemporal flow pattern visualization inside human blood vessels using photoacoustic computed tomography

Hemodynamics can be explored through various biomedical imaging techniques. However, observing transient spatiotemporal variations in the saturation of oxygen (sO2) within human blood vessels proves challenging with conventional methods. In this study, we employed photoacoustic computed tomography (PACT) to reconstruct the evolving spatiotemporal patterns in a human vein. Through analysis of the multi-wavelength photoacoustic (PA) spectrum, we illustrated the dynamic distribution within blood vessels. Additionally, we computationally rendered the dynamic process of venous blood flowing into the major vein and entering a branching vessel. Notably, we successfully recovered, in real time, the parabolic wavefront profile of laminar flow inside a deep vein in vivo—a first-time achievement. While the study is preliminary, the demonstrated capability of dynamic sO2 imaging holds promise for new applications in biology and medicine.

Sub-Nyquist sampling-based high-frequency photoacoustic computed tomography.

High-frequency (greater than 30 MHz) photoacoustic computed tomography (PACT) provides the opportunity to reveal finer details of biological tissues with high spatial resolution. To record photoacoustic signals above 30 MHz, sampling rates higher than 60 MHz are required according to the Nyquist sampling criterion. However, the highest sampling rates supported by existing PACT systems are typically within the range of 40-60 MHz. Herein, we propose a novel PACT imaging method based on sub-Nyquist sampling. The results of numerical simulation, phantom experiment, and in vivo experiment demonstrate that the proposed imaging method can achieve high-frequency PACT imaging with a relatively low sampling rate. An axial resolution of 22 μm is achieved with a 30-MHz transducer and a 41.67-MHz sampling rate. To the best of our knowledge, this is the highest axial resolution ever achieved in PACT based on a sampling rate of not greater than 60 MHz. This work is expected to provide a practical way for high-frequency PACT imaging with limited sampling rates.

On the Importance of Low-Frequency Signals in Functional and Molecular Photoacoustic Computed Tomography.

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers.

Dynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers. The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame. A low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image. The conducted numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available. The proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.

Point spread function modeling for photoacoustic tomography - I: three-dimensional detection geometries.

Photoacoustic computed tomography (PACT) has been under intensive investigation as a promising noninvasive biomedical imaging modality. Various acoustic detector arrays have been developed to achieve enhanced imaging performance. In this paper, we study the effect of the detection geometry on image quality through point spread function (PSF) modeling based on back-projection image reconstruction. Three commonly-used three-dimensional detection geometries, namely, spherical, cylindrical, and planar detector arrays, are investigated. The effect of detector bandwidth and aperture on PSF in these detection geometries is also studied. This work provides a performance evaluation tool for acoustic detector arrays used in PACT and can be helpful in the design and selection of detector arrays in practical imaging applications.

Indocyanine Green-Loaded Liposomes-Assisted Photoacoustic Computed Tomography for Evaluating In Vivo Tumor Penetration of Liposomal Nanocarriers.

Liposomes possess the potential to enhance drug solubility, prolong the duration of circulation, and augment drug accumulation at the tumor site through passive and active targeting strategies. However, there is a lack of studies examining the in vivo tumor penetration capabilities of liposomes of varying sizes, which hampers the development of drug delivery systems utilizing liposomal nanocarriers. Here, we present an indocyanine green (ICG)-loaded liposomes-assisted photoacoustic computed tomography (PACT) for directly evaluating the tumor penetration ability of liposomal nanocarriers in vivo. Through the utilization of microfluidic mixing combined with extrusion techniques, we successfully prepare liposomes encapsulating ICG in both large (192.6 ± 8.0 nm) and small (61.9 ± 0.6 nm) sizes. Subsequently, we designed a dual-wavelength PACT system to directly monitor the in vivo tumor penetration of large- and small-size ICG-encapsulated liposomes. In vivo PACT experiments indicate that ICG-loaded liposomes of smaller size exhibit enhanced penetration capability within tumor tissues. Our work presents a valuable approach to directly assess the penetration ability of liposomal nanocarriers in vivo, thereby facilitating the advancement of drug delivery systems with enhanced tumor penetration and therapeutic efficacy.

Point spread function modeling for photoacoustic tomography - II: two-dimensional detection geometries.

Point spread function (PSF) modeling is important for the characterization of the imaging performance of a photoacoustic computed tomography (PACT) system. This work aims to study the degradation mechanism of PSF in PACT and investigate the impact of the shape of detection geometry on PSF. PSF modeling of three typical two-dimensional detection geometries, including circular, curved, and linear detector arrays, is presented. Based on the non-ideal detection geometries, the effect of detector bandwidth and detector aperture on PSF is also investigated. Moreover, PSFs of each geometry with typical detector bandwidths and typical detector aperture sizes are presented. Experiments are conducted to validate the results. The proposed PSF modeling approach and corresponding results can help predict and interpret the quality of photoacoustic images produced by a practical PACT system. It is beneficial for the design of detector arrays for enhanced imaging performance.

Adaptive polarization photoacoustic computed tomography for biological anisotropic tissue imaging

Most photoacoustic computed tomography (PACT) systems usually ignore the anisotropy of the tissue absorption coefficient, which will lead to the lack of information in reconstructed images. In this work, the effect is addressed of the possible optical absorption anisotropy of tissue on PACT images. The functional relationship is derived between the photoacoustic response and the polarization angle of the excitation light. An adaptive polarized light photoacoustic imaging (AP-PACT) approach is proposed and shown to make up for the lack of imaging information and achieve optimal image contrast when imaging samples with anisotropic optical absorption, by utilizing the standard deviation of photoacoustic response as the feedback signal in an adaptive data acquisition process. The method is implemented both on phantom and in vitro experiments, which show that AP-PACT can recover anisotropic absorption-related information from reconstructed images and thus significantly improve their quality.

A method for the geometric calibration of ultrasound transducer arrays with arbitrary geometries

Geometric calibration of ultrasound transducer arrays is critical to optimizing the performance of photoacoustic computed tomography (PACT) systems. We present a geometric calibration method that is applicable to a wide range of PACT systems. We obtain the speed of sound and point source locations using surrogate methods, which results in a linear problem in the transducer coordinates. We characterize the estimation error, which informs our choice of the point source arrangement. We demonstrate our method in a three-dimensional PACT system and show that our method improves the contrast-to-noise ratio, the size, and the spread of point source reconstructions by 80±19%, 19±3%, and 7±1%, respectively. We reconstruct the images of a healthy human breast before and after calibration and find that the calibrated image reveals vasculatures that were previously invisible. Our work introduces a method for geometric calibration in PACT and paves the way for improving PACT image quality.

Streak artifact suppressed back projection for sparse-view photoacoustic computed tomography.

The development of fast and accurate image reconstruction algorithms under constrained data acquisition conditions is important for photoacoustic computed tomography (PACT). Sparse-view measurements have been used to accelerate data acquisition and reduce system complexity; however, reconstructed images suffer from sparsity-induced streak artifacts. In this paper, a modified back-projection (BP) method termed anti-streak BP is proposed to suppress streak artifacts in sparse-view PACT reconstruction. During the reconstruction process, the anti-streak BP finds the back-projection terms contaminated by high-intensity sources with an outlier detection method. Then, the weights of the contaminated back-projection terms are adaptively adjusted to eliminate the effects of high-intensity sources. The proposed anti-streak BP method is compared with the conventional BP method on both simulation and in vivo data. The anti-streak BP method shows substantially fewer artifacts in the reconstructed images, and the streak index is 54% and 20% lower than that of the conventional BP method on simulation and in vivo data, when the transducer number N=128. The anti-streak BP method is a powerful improvement of the BP method with the ability of artifact suppression.

The influences of finite aperture size in photoacoustic computed tomography

In photoacoustic computed tomography (PACT), the “finite aperture effect” is often characterized as a tangential resolution that increases proportionally with the distance from the rotation center. However, this conclusion is based on the inaccurate point-detector assumption used in image reconstruction. In this study, we appropriately modeled the finite size of the acoustic detector in the back-projection (BP) based image reconstruction to improve the accuracy of the time delay calculation and systematically investigated its effects. Our results showed that the main effect of the finite aperture size is the creation of a limited high-quality imaging region (HQIR) around the scanning center, due to the directional sensitivity of the detector. We also demonstrated that the “finite aperture effect” can reduce the optimal number of detectors required for spatial anti-aliasing. These new findings provide novel perspectives for optimizing PACT systems and corresponding reconstruction methods.

Location-Dependent Spatiotemporal Antialiasing in Photoacoustic Computed Tomography.

Photoacoustic computed tomography (PACT) images optical absorption contrast by detecting ultrasonic waves induced by optical energy deposition in materials such as biological tissues. An ultrasonic transducer array or its scanning equivalent is used to detect ultrasonic waves. The spatial distribution of the transducer elements must satisfy the spatial Nyquist criterion; otherwise, spatial aliasing occurs and causes artifacts in reconstructed images. The spatial Nyquist criterion poses different requirements on the transducer elements' distributions for different locations in the image domain, which has not been studied previously. In this research, we elaborate on the location dependency through spatiotemporal analysis and propose a location-dependent spatiotemporal antialiasing method. By applying this method to PACT in full-ring array geometry, we effectively mitigate aliasing artifacts with minimal effects on image resolution in both numerical simulations and in vivo experiments.

Photoacoustic imaging with limited sampling: a review of machine learning approaches.

Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.

A Signal Domain Object Segmentation Method for Ultrasound and Photoacoustic Computed Tomography.

Image segmentation is important in improving the diagnostic capability of ultrasound computed tomography (USCT) and photoacoustic computed tomography (PACT), as it can be included in the image reconstruction process to improve image quality and quantification abilities. Segmenting the imaged object out of the background using image domain methods is easily complicated by low contrast, noise, and artifacts in the reconstructed image. Here, we introduce a new signal domain object segmentation method for USCT and PACT which does not require image reconstruction beforehand and is automatic, robust, computationally efficient, accurate, and straightforward. We first establish the relationship between the time-of-flight of the received first arrival waves and the object's boundary which is described by ellipse equations. Then, we show that the ellipses are tangent to the boundary. By looking for tangent points on the common tangent of neighboring ellipses, the boundary can be approximated with high fidelity. Imaging experiments of human fingers and mice cross-sections showed that our method provided equivalent or better segmentations than the optimal ones by active contours. In summary, our method greatly reduces the overall complexity of object segmentation and shows great potential in eliminating user dependency without sacrificing segmentation accuracy. The method can be further seamlessly incorporated into algorithms for other processing purposes in USCT and PACT, such as high-quality image reconstruction.

Spatiotemporal image reconstruction for photoacoustic computed tomography of tumor vascular perfusion in murine models

Monitoring critical physiological processes in murine models like tumor vascular perfusion and its response to prospective anti-cancer treatments is a significant potential use case of dynamic photoacoustic computed tomography (PACT). Previously reported studies of dynamic PACT are based on a frame-by-frame image reconstruction (FBFIR) procedure in which full-view measurement data are assumed to be rapidly acquired. However, many commercial three-dimensional PACT imagers acquire measurements at each tomographic view rotating the object in discrete steps. The time to collect the full-view data is limited by the rotation speed of the object holder and the laser repetition rate. Therefore, FBFIR techniques are not applicable, and there is a critical need for accurate and efficient spatiotemporal image reconstruction (STIR) techniques that can account for spatiotemporal redundancies in the object’s features. To address this, we propose a low-rank matrix estimation-based STIR technique in which the sought-after dynamic image is approximated using a semiseparable approximation in space and time. To validate the proposed method, we also develop a virtual imaging framework for PACT employing dynamic numerical mouse phantoms with a physiologically realistic respiratory motion and perfusion model. The studies demonstrated that the proposed method accurately recovers the tumor vascular perfusion and object motion.

Non-invasive photoacoustic computed tomography of rat heart anatomy and function

Complementary to mainstream cardiac imaging modalities for preclinical research, photoacoustic computed tomography (PACT) can provide functional optical contrast with high imaging speed and resolution. However, PACT has not been demonstrated to reveal the dynamics of whole cardiac anatomy or vascular system without surgical procedure (thoracotomy) for tissue penetration. Here, we achieved non-invasive imaging of rat hearts using the recently developed three-dimensional PACT (3D-PACT) platform, demonstrating the regulated illumination and detection schemes to reduce the effects of optical attenuation and acoustic distortion through the chest wall; thereby, enabling unimpeded visualization of the cardiac anatomy and intracardiac hemodynamics following rapidly scanning the heart within 10 s. We further applied 3D-PACT to reveal distinct cardiac structural and functional changes among the healthy, hypertensive, and obese rats, with optical contrast to uncover differences in cardiac chamber size, wall thickness, and hemodynamics. Accordingly, 3D-PACT provides high imaging speed and nonionizing penetration to capture the whole heart for diagnosing the animal models, holding promises for clinical translation to cardiac imaging of human neonates.

Three-dimensional non-invasive brain imaging of ischemic stroke by integrated photoacoustic, ultrasound and angiographic tomography (PAUSAT)

We present an ischemic stroke study using our newly-developed PAUSAT system that integrates photoacoustic computed tomography (PACT), high-frequency ultrasound imaging, and acoustic angiographic tomography. PAUSAT is capable of three-dimensional (3D) imaging of the brain morphology, blood perfusion, and blood oxygenation. Using PAUSAT, we studied the hemodynamic changes in the whole mouse brain induced by two common ischemic stroke models: the permanent middle cerebral artery occlusion (pMCAO) model and the photothrombotic (PT) model. We imaged the same mouse brains before and after stroke, and quantitatively compared the two stroke models. We observed clear hemodynamic changes after ischemic stroke, including reduced blood perfusion and oxygenation. Such changes were spatially heterogenous. We also quantified the tissue infarct volume in both stroke models. The PAUSAT measurements were validated by laser speckle imaging and histology. Our results have collectively demonstrated that PAUSAT can be a valuable tool for non-invasive longitudinal studies of neurological diseases at the whole-brain scale.

A jointed feature fusion framework for photoacoustic image reconstruction

The standard reconstruction of Photoacoustic (PA) computed tomography (PACT) image could cause the artifacts due to interferences or ill-posed setup. Recently, deep learning has been used to reconstruct the PA image with ill-posed conditions. In this paper, we propose a jointed feature fusion framework (JEFF-Net) based on deep learning to reconstruct the PA image using limited-view data. The cross-domain features from limited-view position-wise data and the reconstructed image are fused by a backtracked supervision. A quarter position-wise data (32 channels) is fed into model, which outputs another 3-quarters-view data (96 channels). Moreover, two novel losses are designed to restrain the artifacts by sufficiently manipulating superposed data. The experimental results have demonstrated the superior performance and quantitative evaluations show that our proposed method outperformed the ground-truth in some metrics by 135% (SSIM for simulation) and 40% (gCNR for in-vivo) improvement.

Small Animal In Situ Drug Delivery Effects via Transdermal Microneedles Array versus Intravenous Injection: A Pilot Observation Based on Photoacoustic Tomography.

Intravenous injection is a rapid, low-cost, and direct method that is commonly used to deliver multifarious biotherapeutics and vaccines. However, intravenous injection often causes trauma or tissue injury that requires professional operation. Transdermal drug delivery overcomes the aforementioned defects, and the microneedles (MNs) array is one of the most promising transdermal drug delivery platforms. Timely, precise, and non-invasive monitoring and evaluation of the effects of MNs in transdermal administration is significant to the research of drug efficiency response to specific diseases. In this sense, photoacoustic computed tomography (PACT), which provides wavelength-selective and deep-penetrating optical contrast, could be a promising imaging tool for in situ evaluation of the treatment effects. In this work, we propose the use of PACT to non-invasively assess the effects of real-time drug delivery in glioma tumors through transdermal administration with degradable indocyanine green-loaded hyaluronic acid MNs (ICG-HA-MNs). The outcome is systematically and quantitatively compared with that via intravenous injection. It is found that the photoacoustic signals of ICG in the tumor site express a faster elevation and shorter duration time in the intravenous injection group; by contrast, the photoacoustic signals demonstrate a lower intensity but prolonged duration time in the MNs group. The observed phenomenon indicates faster response but shorter drug duration for intravenous injection, which is in contrast with the lower loading but prolonged performance for transdermal drug delivery with MNs. These results exhibit good consistency with the earlier, common-sense findings reported from other aspects, confirming that PACT can serve as a potential imaging tool to precisely, non-invasively, and quickly evaluate in situ drug delivery effects and provide constructive guidance for the design and fabrication of microneedles.

Deep Learning Enhances Multiparametric Dynamic Volumetric Photoacoustic Computed Tomography In Vivo (DL-PACT).

Photoacoustic computed tomography (PACT) has become a premier preclinical and clinical imaging modality. Although PACT's image quality can be dramatically improved with a large number of ultrasound (US) transducer elements and associated multiplexed data acquisition systems, the associated high system cost and/or slow temporal resolution are significant problems. Here, a deep learning-based approach is demonstrated that qualitatively and quantitively diminishes the limited-view artifacts that reduce image quality and improves the slow temporal resolution. This deep learning-enhanced multiparametric dynamic volumetric PACT approach, called DL-PACT, requires only a clustered subset of many US transducer elements on the conventional multiparametric PACT. Using DL-PACT, high-quality static structural and dynamic contrast-enhanced whole-body images as well as dynamic functional brain images of live animals and humans are successfully acquired, all in a relatively fast and cost-effective manner. It is believed that the strategy can significantly advance the use of PACT technology for preclinical and clinical applications such as neurology, cardiology, pharmacology, endocrinology, and oncology.