A Hybrid Diffusion Model Enhances Multiparametric 3D Photoacoustic Computed Tomography

Photoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Notably, our framework can achieve an effective rate of times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

Capitalizing on endogenous hemoglobin contrast, photoacoustic-computed tomography (PACT), a deep-tissue high-resolution imaging modality, has drawn increasing interest in neuroimaging. However, most existing studies are limited to functional imaging on the cortical surface and the deep brain structural imaging capability of PACT has never been demonstrated. Here, we explicitly studied the limiting factors of deep brain PACT imaging. We found that the skull distorted the acoustic signal and blood suppressed the structural contrast from other chromophores. When the two effects are mitigated, PACT can potentially provide high-resolution label-free imaging of structures in the entire mouse brain. With [Formula: see text] in-plane resolution, we can clearly identify major structures of the brain, which complements magnetic resonance microscopy for imaging small-animal brain structures. Spectral PACT studies indicate that structural contrasts mainly originate from cytochrome distribution and that the presence of lipid sharpens the image contrast; brain histology results provide further validation. The feasibility of imaging the structure of the brain in vivo is also discussed. Our results demonstrate that PACT is a promising modality for both structural and functional brain imaging.

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the wavefield recorded on a measurement aperture located outside the support of the source. A major challenge in transcranial PACT brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. The transmission of ultrasonic waves through the skull induces strong changes to the photoacoustic wavefield through the processes of absorption, scattering, and longitudinal-to-shear wave mode conversion. To properly account for these effects, a wave equation-based inversion method should be employed that can model the heterogeneous elastic properties of the medium.

Due to their low cost, hand-held convenience, wide selection of bandwidths, and ultrasound imaging capability, linear ultrasonic transducer arrays have been widely studied for photoacoustic computed tomography (PACT). As linear-array PACT suffers from a limited view, full-view imaging requires either the transducer or the object to be rotated. So far, both the central frequencies and bandwidth of linear transducer arrays applied in full-view PACT are low, limiting the spatial resolutions of the reconstructed images. Here, we present a multiview high-frequency PACT imaging system implemented with a commercial 40-MHz central frequency linear transducer array. By rotating the object through multiple angles with respect to the linear transducer array, we acquired full-view photoacoustic pressure measurements. Further, to quantify the unipolar initial pressures and overcome the limitations of the single-view Hilbert transformation, we developed a multiview Hilbert transformation method. The in-plane spatial resolution of this full-view linear-array PACT was quantified to be isotropically 60 μm within a 10×10 mm² field of view. The system was demonstrated by imaging both a leaf skeleton and a zebrafish in vivo.

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. If the object possesses spatially variant acoustic properties that are unaccounted for by the reconstruction method, the estimated image can contain distortions. While reconstruction methods have recently been developed to compensate for this effect, they generally require the object’s acoustic properties to be known a priori. To circumvent the need for detailed information regarding an object’s acoustic properties, we previously proposed a half-time reconstruction method for PACT. A half-time reconstruction method estimates the PACT image from a data set that has been temporally truncated to exclude the data components that have been strongly aberrated. However, this method can be improved upon when the approximate sizes and locations of isolated heterogeneous structures, such as bones or gas pockets, are known. To address this, we investigate PACT reconstruction methods that are based on a variable data truncation (VDT) approach. The VDT approach represents a generalization of the half-time approach, in which the degree of temporal truncation for each measurement is determined by the distance between the corresponding ultrasonic transducer location and the nearest known bone or gas void location. Computer-simulated and experimental data are employed to demonstrate the effectiveness of the approach in mitigating artifacts due to acoustic heterogeneities.

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.

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the absorbed optical energy density within tissue. If the object possesses spatially variant acoustic properties that are unaccounted for by the reconstruction algorithm, the estimated image can contain distortions. While reconstruction algorithms have recently been developed for compensating for this effect, they generally require the objects acoustic properties to be known a priori. To circumvent the need for detailed information regarding an objects acoustic properties, we have previously proposed a half-time reconstruction method for PACT. A half-time reconstruction method estimates the PACT image from a data set that has been temporally truncated to exclude the data components that have been strongly aberrated. In this approach, the degree of temporal truncation is the same for all measurements. However, this strategy can be improved upon it when the approximate sizes and locations of strongly heterogeneous structures such as gas voids or bones are known. In this work, we investigate PACT reconstruction algorithms that are based on a variable temporal data truncation (VTDT) approach that represents a generalization of the half-time reconstruction approach. In the VTDT approach, the degree of temporal truncation for each measurement is determined by the distance between the corresponding transducer location and the nearest known bone or gas void location. Reconstructed images from a numerical phantom is employed to demonstrate the feasibility and effectiveness of the approach.

Photoacoustic computed tomography (PACT) is a non-invasive imaging technique offering high contrast, high resolution, and deep penetration in biological tissues. We report a PACT system equipped with a high frequency linear transducer array for mapping the microvascular network of a whole mouse brain with the skull intact and studying its hemodynamic activities. The linear array was scanned in the coronal plane to collect data from different angles, and full-view images were synthesized from the limited-view images in which vessels were only partially revealed. We investigated spontaneous neural activities in the deep brain by monitoring the concentration of hemoglobin in the blood vessels and observed strong interhemispherical correlations between several chosen functional regions, both in the cortical layer and in the deep regions. We also studied neural activities during an epileptic seizure and observed the epileptic wave spreading around the injection site and the wave propagating in the opposite hemisphere.

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.

Photoacoustic imaging combines advantages of both ultrasound imaging and optical imaging to provide high ultrasonic resolution and high optical contrast in noninvasive medical images. A photoacoustic computer tomography （PCT） system with a focused digital phased array transducer is proposed. In particular each element of the transducer array has a thin cylindrical ultrasonic lens to select 2D image plane and suppress the out-of-plane signals to realize photoacoustic computed tomography. A fast full-view photoacoustic imaging system for visualizing tissue structures using a linear transducer array with combined scan, whies; detects photoacoustic signals by combined electronic and mechanical scanning. An improved limited-field filtered back projection algorithm with directivity factors was applied to reconstruct the optical absorption distribution and further improve the lateral resolution. In the experiment, the spatial resolution of this imaging system is determined as 0.2mm, and the slice resolution of Z axis is 1.5mm. Mechanical step scanning was performed, leading to a measurement time of 6.4 seconds per position, and a total scan duration of 150 seconds to obtain the two-dimensional photoacoustic image. High spatial resolution of 2D photoacoustic vascular image and quasi-3D photoacoustic CT for breast cancer imaging are achicned. The experimental results demonstrate that this method and imaging system can realize the high resolution photoacoustic tomography wtich can be applied to noninvasive detection of early breast cancer.

Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. Utilizing ultrasound transducers with larger surface areas can improve detection sensitivity. However, when computationally efficient analytic reconstruction methods that neglect the spatial impulse responses (SIRs) of the transducer are employed, the spatial resolution of the reconstructed images will be compromised. Although optimization-based reconstruction methods can explicitly account for SIR effects, their computational cost is generally high, particularly in three-dimensional (3D) applications. To address the need for accurate but rapid 3D PACT image reconstruction, this study presents a framework for establishing a learned SIR compensation method that operates in the data domain. The learned compensation method maps SIR-corrupted PACT measurement data to compensated data that would have been recorded by idealized point-like transducers. Subsequently, the compensated data can be used with a computationally efficient reconstruction method that neglects SIR effects. Two variants of the learned compensation model are investigated that employ a U-Net model and a specifically designed, physics-inspired model, referred to as Deconv-Net. A fast and analytical training data generation procedure is also a component of the presented framework. The framework is rigorously validated in virtual imaging studies, demonstrating resolution improvement and robustness to noise variations, object complexity, and sound speed heterogeneity. When applied to in-vivo breast imaging data, the learned compensation models revealed fine structures that had been obscured by SIR-induced artifacts. To our knowledge, this is the first demonstration of learned SIR compensation in 3D PACT imaging.

Typically, multi-single-element photoacoustic computed tomography (PACT) systems utilize numerous ultrasound transducers arranged in cylindrical or hemispherical configurations for detection, combined with a single diffuse light source or multiple sparse light sources to illuminate the imaging target. While these systems produce high-quality 3D PA images, they require complex, multi-channel data acquisition (DAQ) systems to acquire data from all transducers. These DAQ systems are often bulky and expensive, significantly limiting the clinical translation of PACT systems for patient care. In this study, we evaluated the feasibility of using a compact and cost-effective Texas Instruments analog front-end DAQ module for multi-single-element PACT systems. By imaging a simple 3D phantom, we demonstrated the capability of this affordable DAQ board, with reconstructed images showing promise for practical and economical solutions in PACT systems. This advancement paves the way for broader applications of PACT in both research and clinical settings.

The growing prevalence of brain diseases and the limitations of existing imaging techniques highlight the urgent demand for advanced, biocompatible, and high-resolution brain imaging approaches. Photoacoustic imaging (PAI) uniquely integrates scalable spatial resolution, deep penetration, and non-ionizing excitation, which enables multi-scale visualization from subcellular structures to the whole brain. This review systematically summarizes the fundamental principles of PAI, commonly used contrast agents, and key system components, and provides a comparative analysis of the major imaging modalities-optical-resolution and acoustic-resolution photoacoustic microscopy (OR-/AR-PAM), photoacoustic computed tomography (PACT), and photoacoustic endoscopy (PAE). PACT offers superior penetration depth and field of view, whereas OR-/AR-PAM provides higher spatial resolution at molecular scales. By aligning disease-specific imaging requirements with the functional strengths of each modality, PAI has enabled integrated structural and functional imaging in glioblastoma, neurodegenerative disorders, traumatic brain injury, and cerebrovascular diseases, highlighting its integrated advantages for multiscale brain imaging. Finally, the review discusses key challenges for clinical translation of PAI, including motion artifacts, blood-brain barrier constraints, contrast agent approval, limitations of single-modality approaches, and technical limitations, and proposes corresponding strategies to address these obstacles. This review provides forward-looking guidance for the clinical translation of multiscale brain imaging technologies.

Photoacoustic computed tomography (PACT) is a multimodal imaging technique that combines the advantages of optical resolution and ultrasonic imaging depth, offering unique strengths in functional and molecular imaging. However, the reconstruction quality is often compromised by restricted field-of-view (FOV) acquisition, which is inherently constrained by both system physics limitations and tissue complexity. To overcome this challenge, we propose a paradigm-shifting framework based on a deep learning-augmented generative model. A Pix2Pix conditional GAN (cGAN) was trained to map truncated inputs to full-FOV outputs, explicitly targeting truncation artifacts through adversarial learning. The framework was validated on public datasets, with performance benchmarked against conventional Time Reversal and U-Net-based reconstruction methods.

Photoacoustic imaging is an emerging optical imaging modality which provides optical absorption contrasts and high resolution in the optical diffusive regime. In photoacoustic computed tomography (PACT), often times the detection of the photoacoustic signal only covers a partial solid angle less than 4π, due to experimental or economic constraints. Incomplete spatial coverage always jeopardizes image quality and resolution, and results in significant artifacts and missing of image features. This problem is referred to as “limited view” and has remained unsolved for decades. In this work, we present a new machine-learning-based method that is specifically designed to compensate for the missing information due to limited view. The robustness and effectiveness of our method were demonstrated using numerical, phantom, and in vivo experiments.