Acoustic wave detection techniques like ultrasound (US) and photoacoustic (PA) tomography are widely used in biomedical imaging but often require expensive transducer arrays and complex setups for 3D imaging. To overcome these challenges, recent research has explored using an ergodic relay (ER) with a single-element transducer. This approach improves imaging speed at low cost and reduces complexity. This study presents a dual-mode system that utilizes an ER for single-shot, 3D PA and US imaging with a single-element detector. Each ultrasonic or optical excitation pulse generates a 1D signal, which is then used to reconstruct a 3D image in US or PA mode. While the PA mode provides optical absorption-based contrast, the US mode offers complementary acoustic scattering-based contrast. We demonstrate the system's capabilities through in vivo imaging of blood vessels and skin structure in human hands. The system is non-invasive, label-free, and ultrafast, enabling 4D imaging with simplified hardware requirements.

Tomographic imaging modalities are described by large system matrices. To improve the temporal resolution of functional imaging in tomography, sparse spatial sampling is often employed, which degrades the system matrix and introduces artifacts in reconstructed images. Various existing techniques improve the image quality without correcting the system matrix and have limitations. Here, we compress the system matrix to improve computational efficiency (e.g., 42 times) using singular value decomposition and fast Fourier transform. Enabled by the efficiency, we propose fast sparsely sampling functional imaging by incorporating a densely sampled prior image into the system matrix, which maintains the critical linearity while mitigating artifacts. We demonstrate the methods in 3D photoacoustic computed tomography with significantly improved image quality and clarify their applicability to X-ray CT and radial-sampling MRI due to the similarities in system matrices.

Optical-resolution photoacoustic microscopy (OR-PAM) can visualize cellular-level wavelength-dependent optical absorption with high resolution and sensitivity. However, the imaging speed of OR-PAM has been limited by the laser repetition rate due to the point-by-point scanning of a focused laser beam. To overcome this limitation, we propose multifocal optical-resolution photoacoustic microscopy (MOR-PAM) with a single-element ultrasonic transducer, leveraging the diffractive optical element (DOE) and a custom-designed encoding acoustic mask. The DOE generates 8 focal spots of $3~\mu $ m diameter. The acoustic mask was designed to encode photoacoustic signals from different focal spots. MOR-PAM achieved an 8-fold increase in imaging speed compared to conventional OR-PAM with the same laser repetition rate. We demonstrated the MOR-PAM using a 266 nm laser at 10 KHz, providing solutions for rapid OR-PAM beyond the laser repetition rate in a cost-effective way. The proposed method can be applied to versatile OR-PAM configurations and enable new applications where high-speed imaging is critical.

We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at $256\times 256$, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-experts policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We release all the code, models, and results at: https://4kagent.github.io.

Transcranial photoacoustic computed tomography presents challenges in human brain imaging due to skull-induced acoustic aberration. Existing full-wave image reconstruction methods rely on a unified elastic wave equa- tion for skull shear and longitudinal wave propagation, therefore demanding substantial computational resources. We propose an efficient discrete imaging model based on finite element discretization. The elastic wave equation for solids is solely applied to the hard-tissue skull region, while the soft-tissue or coupling-medium region that dominates the simulation domain is modeled with the simpler acoustic wave equation for liquids. The solid-liquid interfaces are explicitly modeled with elastic-acoustic coupling. Furthermore, finite element discretization allows coarser, irregular meshes to conform to object geometry. These factors significantly reduce the linear system size by 20 times to facilitate accurate whole-brain simulations with improved speed. We derive a matched forward-adjoint operator pair based on the model to enable integration with various optimization algorithms. We validate the reconstruction framework through numerical simulations and phantom experiments.

Photoacoustic tomography holds tremendous potential for neuroimaging due to its functional magnetic resonance imaging (fMRI)-like functional contrast and greater specificity, richer contrast, portability, open platform, faster imaging, magnet-free and quieter operation, and lower cost. However, accounting for the skull-induced acoustic distortion remains a long-standing challenge due to the problem size. This is aggravated in functional imaging, where high accuracy is needed to detect minuscule functional changes. Here, we develop an acoustic solver based on the boundary-element method (BEM) to model the skull and de-aberrate the images. BEM uses boundary meshes and compression for superior computational efficiency compared to volumetric discretization-based methods. We demonstrate BEM's higher accuracy and favorable scalability relative to the widely used pseudo-spectral time-domain method (PSTD). In imaging through an ex-vivo adult human skull, BEM outperforms PSTD in several metrics. Our work establishes BEM as a valuable and naturally suited technique in photoacoustic tomography and lays the foundation for BEM-based de-aberration methods.

Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality that combines optical absorption contrast with ultrasound imaging depth. One challenge in PAT is image reconstruction with inadequate acoustic signals due to limited sensor coverage or due to the density of the transducer array. Such cases call for solving an ill-posed inverse reconstruction problem. In this work, we use score-based diffusion models to solve the inverse problem of reconstructing an image from limited PAT measurements. The proposed approach allows us to incorporate an expressive prior learned by a diffusion model on simulated vessel structures while still being robust to varying transducer sparsity conditions.

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 computed tomography (PACT) based on a full-ring ultrasonic transducer array is widely used for small animal wholebody and human organ imaging, thanks to its high in-plane resolution and full-view fidelity. However, spatial aliasing in full-ring geometry PACT has not been studied in detail. If the spatial Nyquist criterion is not met, aliasing in spatial sampling causes artifacts in reconstructed images, even when the temporal Nyquist criterion has been satisfied. In this work, we clarified the source of spatial aliasing through spatiotemporal analysis. We demonstrated that the combination of spatial interpolation and temporal filtering can effectively mitigate artifacts caused by aliasing in either image reconstruction or spatial sampling, and we validated this method by both numerical simulations and in vivo experiments.

Accurate estimation of the initial pressure distribution in photoacoustic computed tomography (PACT) depends on knowledge of the sound speed distribution. However, the sound speed distribution is typically unknown. Further, the initial pressure and sound speed distributions cannot both, in general, be stably recovered from PACT measurements alone. In this work, a joint reconstruction (JR) method for the initial pressure distribution and a low-dimensional parameterized model of the sound speed distribution is proposed. By employing a priori information about the structure of the sound speed distribution, both the initial pressure and sound speed can be accurately recovered. The JR problem is solved by use of a proximal optimization method that allows constraints and nonsmooth regularization functions for the initial pressure distribution. The gradients of the cost function with respect to the initial pressure and sound speed distributions are calculated by use of an adjoint state method that has the same per-iteration computational cost as calculating the gradient with respect to the initial pressure distribution alone. This approach is evaluated through two-dimensional computer-simulation studies for a small animal imaging model and by application to experimental in vivo measurements of a mouse.

Photoacoustic computed tomography (PACT) is an emerging imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to an inverse source problem in which the initial pressure distribution is recovered from measurements of the radiated wavefield. A major challenge in transcranial PACT brain imaging is compensation for aberrations in the measured data due to the presence of the skull. Ultrasonic waves undergo absorption, scattering and longitudinal-to-shear wave mode conversion as they propagate through the skull. To properly account for these effects, a wave-equation-based inversion method should be employed that can model the heterogeneous elastic properties of the skull. In this work, a forward model based on a finite-difference time-domain discretization of the three-dimensional elastic wave equation is established and a procedure for computing the corresponding adjoint of the forward operator is presented. Massively parallel implementations of these operators employing multiple graphics processing units (GPUs) are also developed. The developed numerical framework is validated and investigated in computer19 simulation and experimental phantom studies whose designs are motivated by transcranial PACT applications.

Existing approaches to image reconstruction in photoacoustic computed tomography (PACT) with acoustically heterogeneous media are limited to weakly varying media, are computationally burdensome, and/or cannot effectively mitigate the effects of measurement data incompleteness and noise. In this work, we develop and investigate a discrete imaging model for PACT that is based on the exact photoacoustic (PA) wave equation and facilitates the circumvention of these limitations. A key contribution of the work is the establishment of a procedure to implement a matched forward and backprojection operator pair associated with the discrete imaging model, which permits application of a wide-range of modern image reconstruction algorithms that can mitigate the effects of data incompleteness and noise. The forward and backprojection operators are based on the k-space pseudospectral method for computing numerical solutions to the PA wave equation in the time domain. The developed reconstruction methodology is investigated by use of both computer-simulated and experimental PACT measurement data.

Thermoacoustic tomography (TAT) is a novel, non-invasive medical imaging technique but has encountered obstacles in imaging through the cranium. In this paper we present two methods for transcranial TAT: Kirchhoff migration (KM) and reverse-time migration (RTM). The two methods' imaging qualities are verified and compared based on both synthetic and experimental data. RTM proves to have better velocity variance and imaging quality, and little noise with spatial aliasing. RTM is a promising approach for achieving transcranial TAT in further studies.

This paper discusses the design of a novel photoacoustic microscopy imaging system with promise for studying the structure of tissue microvasculature for applications in visualizing angiogenesis. A new 16 channel analog and digital high-frequency array based photoacoustic microscopy system (PAM) was developed using an Nd:YLF pumped tunable dye laser, a 30 MHz piezo composite linear array transducer, and a custom multichannel receiver electronics system. Using offline delay and sum beamforming and beamsteering, phantom images were obtained from a 6 mum carbon fiber in water at a depth of 8 mm. The measured -6 dB lateral and axial spatial resolution of the system was 100+/-5 microm and 45+/-5 microm, respectively. The dynamic focusing capability of the system was demonstrated by imaging a composite carbon fiber matrix through a 12.5 mm imaging depth. Next, 2-D in vivo images were formed of vessels around 100 mum in diameter in the human hand. Three-dimensional in vivo images were also formed of micro-vessels 3 mm below the surface of the skin in two Sprague Dawley rats.

In the classic formulation of photoacoustic tomography (PAT), two distinct descriptions of the imaging model have been employed for developing reconstruction algorithms. We demonstrate that the numerical and statistical properties of unweighted least-squares reconstruction algorithms associated with each imaging model are generally very different. Specifically, some PAT reconstruction algorithms, including many of the iterative algorithms previously explored, do not work directly with the raw measured pressure wavefields, but rather with an integrated data function that is obtained by temporally integrating the photoacoustic wavefield. The integration modifies the statistical distribution of the data, introducing statistical correlations among samples. This change is highly significant for iterative algorithms, many of which explicitly or implicitly seek to minimize a statistical cost function. In this work, we demonstrate that iterative reconstruction by least-squares minimization yields better resolution-noise tradeoffs when working with the raw pressure data than with the integrated data commonly employed. In addition, we demonstrate that the raw-data based approach is less sensitive to certain deterministic errors, such as dc offset errors.

Noninvasive molecular and functional imaging in vivo is promising for detecting and monitoring various physiological conditions in animals and ultimately humans. To this end, we present a novel noninvasive technology, spectroscopic photoacoustic tomography (SPAT), which offers both strong optical absorption contrast and high ultrasonic spatial resolution. Optical contrast allows spectroscopic separation of signal contributions from multiple optical absorbers (e.g., oxyhemoglobin, deoxyhemoglobin, and a molecular contrast agent), thus enabling simultaneous molecular and functional imaging. SPAT successfully imaged with high resolution the distribution of a molecular contrast agent targeting integrin overexpressed in human U87 glioblastomas in nude mouse brains. Simultaneously, SPAT also imaged the hemoglobin oxygen saturation and the total hemoglobin concentration of the vasculature, which revealed hypoxia in tumor neovasculature. Therefore, SPAT can potentially lead to better understanding of the interrelationships between hemodynamics and specific biomarkers associated with tumor progression.

Thermoacoustic tomography (TAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. In this paper, we propose and investigate reconstruction approaches for TAT that are based on the half-time reflectivity tomography paradigm. We reveal that half-time reconstruction approaches permit for the explicit control of statistically complementary information that can result in the optimal reduction of image variances. We also show that half-time reconstruction approaches can mitigate image artifacts due to heterogeneous acoustic properties of an object. Reconstructed images and numerical results produced from simulated and experimental TAT measurement data are employed to demonstrate these effects.

Thermoacoustic tomography (TAT) is an emerging imaging technique with potential for a wide range of biomedical imaging applications. In this correspondence, we propose an infinite family of weighted expectation maximization (EM) algorithms for reconstruction of images from temporally truncated TAT measurement data. The weighted EM algorithms are equivalent mathematically to the conventional EM algorithm, but are shown to propagate data inconsistencies in different ways. Using simulated and experimental TAT measurement data, we demonstrate that suitable choices of weighted EM algorithms can effectively mitigate image artifacts that are attributable to temporal truncation of the TAT data function.

Microwave-induced thermoacoustic tomography (TAT) in a cylindrical configuration is developed to image biological tissue. Thermoacoustic signals are acquired by scanning a flat ultrasonic transducer. Using a new expansion of a spherical wave in cylindrical coordinates, we apply the Fourier and Hankel transforms to TAT and obtain an exact frequency-domain reconstruction method. The effect of discrete spatial sampling on image quality is analyzed. An aliasing-proof reconstruction method is proposed. Numerical and experimental results are included.

Reconstruction-based microwave-induced thermoacoustic tomography in a spherical configuration is presented. Thermoacoustic waves from biological tissue samples excited by microwave pulses are measured by a wide-band unfocused ultrasonic transducer, which is set on a spherical surface enclosing the sample. Sufficient data are acquired from different directions to reconstruct the microwave absorption distribution. An exact reconstruction solution is derived and approximated to a modified backprojection algorithm. Experiments demonstrate that the reconstructed images agree well with the original samples. The spatial resolution of the system reaches 0.5 mm.

For pt. I see ibid., vol. 21, no. 7, p. 823-8 (2002). Microwave-induced thermoacoustic tomography (TAT) in a cylindrical configuration is developed to image biological tissue. Thermoacoustic signals are acquired by scanning a flat ultrasonic transducer. Using a new expansion of a spherical wave in cylindrical coordinates, we apply the Fourier and Hankel transforms to TAT and obtain an exact frequency-domain reconstruction method. The effect of discrete spatial sampling on image quality is analyzed. An aliasing-proof reconstruction method is proposed. Numerical and experimental results are included.

Scanning thermoacoustic tomography based on microwave-induced thermoacoustic waves studied. Two-dimensional images of /spl sim/50 mm thick biological tissue samples were obtained. The thermoacoustic signals were also simulated theoretically. The image resolution was significantly improved compared with purely microwave imaging.