Machine-learning enhanced photoacoustic computed tomography in a limited view configuration

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.

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) reveals biological structures, pharmacokinetics, and physiological functions. Although a premium PACT system with many ultrasound (US) transducers delivers high‐quality volumetric imaging, it suffers from high system costs and slow temporal resolution. Here, using a limited number of US elements, a hybrid diffusion model (HD‐PACT) is demonstrated that enhances dynamic multiparametric (structural, functional, and contrast‐enhanced) 3D PACT. Using just 256 out of the 1024 elements in a premier hemispherical US array for PACT, HD‐PACT improves structural images acquired in different planes, organisms, and wavelengths. In functional imaging, HD‐PACT enables 256‐element PACT to observe hypoxia, pharmacokinetics, and angiogenesis during tumor progression. Lastly, HD‐PACT is transferable to low‐end PACT (only 128 US elements), where it dynamically captures contrast‐free/enhanced organs, oxygen‐perturbed brains, and cardiac dynamics with high spatiotemporal resolution in live animals. It is believed that HD‐PACT will be valuable in oncology, cardiology, pharmacology, and endocrinology.

Photoacoustic computed tomography (PACT) is an advanced biomedical imaging modality that integrates optical and ultrasound imaging techniques. A central goal in PACT is to estimate the initial pressure rise from raw photoacoustic data (sinogram), a task complicated by the need for accurate inverse modeling, sensitivity to noise, and hardware limitations. Conventional reconstruction methods typically rely on multi-step processing and require careful parameter tuning to achieve high-quality results. While deep learning has proven effective in addressing challenges like noise suppression and computational efficiency, its use in PACT has largely been limited to image-domain or sensor data (sinogram)-domain enhancement, rather than direct image reconstruction from sensor data (sinograms) - a direction more extensively explored in computed tomography (CT) and magnetic resonance imaging (MRI). In this study, we propose a novel, data-driven methodology that formulates PACT image reconstruction as a supervised learning problem. Our approach employs a hybrid fully connected network followed by a Swin-UNETR to learn a direct mapping from raw sinograms (sensor data) to reconstruct images, effectively eliminating the need for conventional, handcrafted reconstruction steps. The proposed method achieved high image quality, with peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of 33.878 dB and 0.975, respectively.

As an emerging optical imaging modality, photoacoustic imaging provides optical absorption contrasts and ultrasonic high resolution. Artifacts appearing in photoacoustic computed tomography (PACT) always deteriorate image quality and resolution, and result in confusion of biological information. On the basis of different causing reasons, they are roughly classified as split artifacts and streak artifacts. Here we present an innovative Feature-Coupling (FC) method to weaken split artifacts with joint reconstruction of speed of sound and a new reconstruction algorithm, termed Contamination-Tracing Back-Projection (CTBP), is proposed for the mitigation of streak artifacts. The utility, effectiveness and robustness of our methods were demonstrated using numerical, phantom, and in vivo experiments.

Photoacoustic computed tomography (PACT), also known as optoacoustic tomography or thermoacoustic tomography, is an emerging biomedical imaging technique that combines optical absorption contrast with ultrasound detection principles. Recently, a novel analytic image reconstruction formula has been proposed that operates on a data function expressed in the temporal frequency and spatial domains. The validity the formula has been demonstrated for a two-dimensional (2D) circular measurement geometry. In this study, computer simulation studies are conducted to validate the reconstruction formula for a three-dimensional (3D) spherical measurement geometry. This formula provides new insights into how the spatial frequency components of the sought-after object function can be explicitly determined by the temporal frequency components of the data function measured with a 2D circular or 3D spherical measurement geometry in PACT. Comparing with existing Fourier transform-based reconstruction formulas, the reconstruction formula possesses a simple structure that requires no computation of series expansions or multi-dimensional interpolation in Fourier space.

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.

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 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.

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.

The sparse transforms currently used in the model-based reconstruction method for photoacoustic computed tomography (PACT) are predefined and they typically cannot capture the underlying features of the specific data sets adequately, thus limiting the high-quality recovery of photoacoustic images. In this work, we present an advanced reconstruction model using the K-VSD dictionary learning technique and present the in vivo results after adapting the model into the 3D PACT system. The in vivo experiments were performed on an IRB approved human hand and two rats. When compared to the traditional sparse transform, experimental results using our proposed method improved accuracy and contrast to noise ration of the reconstructed photoacoustic images, on average, by 3.7 and 1.8 times in the case of 50% sparse-sampling rate, respectively. We also compared the performance of our algorithm against other techniques, and imaging speed was 60% faster than other approaches. Our system would require sparse-transducer array and lower number of data acquisition hardware (DAQs) potentially reducing the cost of the system. Thus, our work provides a new way for reconstructing photoacoustic images, and it would enable the development of new high-speed low-cost 3D PACT for various biomedical applications.

Chapter 13 - Photoacoustic Molecular Imaging: Principles and Practice

Linear-array-based photoacoustic computed tomography (PACT) has been widely used in vascular imaging due to its low cost and high compatibility with current ultrasound systems. However, linear-array transducers have inherent limitations for three-dimensional imaging due to the poor elevation resolution. In this study, we introduced a deep learning-assisted data process algorithm to enhance the image quality in linear-array-based PACT. Compared to our earlier study where training was performed on 2D reconstructed data, here, we utilized 2D and 3D reconstructed data to train the two networks separately. We then fused the image data from both 2D and 3D training to get features from both algorithms. The numerical and in vivo validations indicate that our approach can improve elevation resolution, recover the true size of the object, and enhance deep vessels. Our deep learning-assisted approach can be applied to translational imaging applications that require detailed visualization of vascular features.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.