Optoacoustic tomography provides a unique possibility for ultra-high-speed 3-D imaging by acquiring complete volumetric datasets from interrogation of tissue by a single nanosecond-duration laser pulse. Yet, similarly to ultrasound, optoacoustics is a time-resolved imaging method, thus, fast 3-D imaging implies real-time acquisition and processing of high speed data from hundreds of detectors simultaneously, which presents significant technological challenges. Herein we present a highly efficient graphical processing unit (GPU) framework for real-time reconstruction and visualization of 3-D tomographic optoacoustic data. By utilizing a newly developed 3-D optoacoustic scanner, which simultaneously acquires signals with a handheld 256-element spherical ultrasonic array system, we further demonstrate tracking of deep tissue human vasculature rendered at a rate of 10 volumetric frames per second. The flexibility provided by the handheld hardware design, combined with the real-time operation, makes the developed platform highly usable for both clinical imaging practice and small animal research applications.

State-of-the-art optoacoustic tomographic imaging systems have been shown to attain threedimensional (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.

We introduce a technique that uses multicomponent seismic measurements to reconstruct the seismic wavefield at any desired crossline position between towed streamers. This method, called multichannel interpolation by matching pursuit (MIMAP), operates on pressure and crossline particle-motion measurements. It is based on the matching-pursuit technique and iteratively reconstructs the signal as a combination of optimal basis functions. Being a data-dependent technique, MIMAP can interpolate severely aliased data without assumptions about seismic events such as linearity or the model related to the seismic wavefield. MIMAP has the capability to perform well in the presence of irregular sampling and is robust when only a small number of samples are available. Using synthetic data examples, we show that the new method has the potential to interpolate signals that are sampled at realistic crossline streamer spacing and in the presence of noise.

Three-component measurements of particle motion would bring significant benefits to towed-marine seismic data if processed in conjunction with the pressure data. We show that particle velocity measurements can increase the effective Nyquist wavenumber by a factor of two or three, depending on how they are used. A true multicomponent streamer would enable accurate data reconstruction in the crossline direction with cable separations for which pressure-only data would be irrecoverably aliased. We also show that conventional workflows aimed at reducing these aliasing effects, such as moveout correction applied before interpolation, are compatible with multicomponent measurements. Some benefits of velocity measurements for deghosting data are well known. We outline how the new measurements might be used to address some long-standing deghosting challenges of particular interest. Specifically, we propose methods for recovering de-ghosted data between streamers and for 3D deghosting of seismic data at the streamer locations.

Computation of the 3D upgoing/downgoing separated wavefield at any desired position within a marine streamer spread is enabled by multicomponent streamers that can measure the crossline and vertical components of water-particle motion in addition to the pressure. We introduce the concept of simultaneous interpolation and deghosting and describe a new technique, generalized matching pursuit (GMP), to achieve this. This method is based on the matching-pursuit technique and iteratively reconstructs the signal as a combination of optimal basis functions. In the GMP method, the basis functions describing the unknown 3D upgoing wavefield are filtered by appropriate forward ghost operators before being matched to the multicomponent measurements. As a data-dependent method, GMP can operate on data samples that are highly aliased in the crossline direction without relying on assumptions about seismic events such as linearity. The technique is naturally suitable for data with only a small number of samples that may be irregularly spaced. We demonstrate the efficacy and robustness of the GMP method on several synthetic data sets of increasing complexity and in the presence of noise.

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Back-projection algorithms are probably the fastest approach to reconstruct an image from a set of optoacoustic (photoacoustic) data set. However, standard implementations of back-projection formulae are still not adequate for real-time (greater than 5 frames per second) visualization of three-dimensional structures. This is due to the fact that the number of voxels one needs to reconstruct in three-dimensions is orders of magnitude larger than the number of pixels in two dimensions. Herein we describe a parallel implementation of optoacoustic signal processing and back-projection reconstruction in an attempt to achieve real-time visualization of structures with three-dimensional optoacoustic tomographic systems. For this purpose, the parallel computation power of a graphics processing unit (GPU) is utilized. The GPU is programmed with OpenCL, a programming language for heterogenous platforms. We showcase that with the implementation suggested in this work imaging at frame rates up to 50 high-resolution three-dimensional images per second is achievable.

Optoacoustic mesoscopy (OAM) retrieves anatomical and functional contrast in vivo at depths not resolvable with optical microscopy. Recent progress on reconstruction algorithms have further advanced its imaging performance to provide high lateral resolution ultimately limited by acoustic diffraction. In this work, a new broadband model‐based OAM (MB‐OAM) framework efficiently exploiting scanning symmetries for an enhanced performance is presented. By capitalizing on the large detection bandwidth of a spherical polyvinylidene difluoride film while accurately accounting for its spatial impulse response, the new approach significantly outperforms standard OAM implementations in terms of contrast and resolution, as validated by functional in vivo experiments in mice and human volunteers. Furthermore, L1‐norm regularization enables resolving structures separated by less than the theoretical diffraction‐limited resolution. This unique label‐free angiographic performance demonstrates the general applicability of MB‐OAM as a super‐resolution deep‐tissue imaging method capable of breaking through the limits imposed by acoustic diffraction.