Spiral volumetric optoacoustic and ultrasound (SVOPUS) tomography of mice.

Optoacoustic (OA) tomography is a powerful noninvasive preclinical imaging tool enabling high resolution whole-body visualization of biodistribution and dynamics of molecular agents. The technique yet lacks endogenous soft-tissue contrast, which often hampers anatomical navigation. Herein, we devise spiral volumetric optoacoustic and ultrasound (SVOPUS) tomography for concurrent OA and pulse-echo ultrasound (US) imaging of whole mice.

Multi-Scale Volumetric Dynamic Optoacoustic and Laser Ultrasound (OPLUS) Imaging Enabled by Semi-Transparent Optical Guidance.

Major biological discoveries are made by interrogating living organisms with light. However, the limited penetration of un-scattered photons within biological tissues limits the depth range covered by optical methods. Deep-tissue imaging is achieved by combining light and ultrasound.

Full-view LED-based optoacoustic tomography.

Optoacoustic tomography is commonly performed with bulky and expensive short-pulsed solid-state lasers providing high per-pulse energies in the millijoule range. Light emitting diodes (LEDs) represent a cost-effective and portable alternative for optoacoustic signal excitation that can additionally provide excellent pulse-to-pulse stability. Herein, we introduce a full-view LED-based optoacoustic tomography (FLOAT) system for deep tissue in vivo imaging.

Spiral volumetric optoacoustic tomography for imaging whole-body biodynamics in small animals.

Fast tracking of biological dynamics across multiple murine organs using the currently commercially available whole-body preclinical imaging systems is hindered by their limited contrast, sensitivity and spatial or temporal resolution. Spiral volumetric optoacoustic tomography (SVOT) provides optical contrast, with an unprecedented level of spatial and temporal resolution, by rapidly scanning a mouse using spherical arrays, thus overcoming the current limitations in whole-body imaging. The method enables the visualization of deep-seated structures in living mammalian tissues in the near-infrared spectral window, while further providing unrivalled image quality and rich spectroscopic optical contrast.

Head-to-tail imaging of mice with spiral volumetric optoacoustic tomography.

Optoacoustic tomography has been established as a powerful modality for preclinical imaging. However, efficient whole-body imaging coverage has not been achieved owing to the arduous requirement for continuous acoustic coupling around the animal. In this work, we introduce panoramic (360) head-to-tail 3D imaging of mice with spiral volumetric optoacoustic tomography (SVOT).

Real-time 3D optoacoustic tracking of cell-sized magnetic microrobots circulating in the mouse brain vasculature.

Mobile microrobots hold remarkable potential to revolutionize health care by enabling unprecedented active medical interventions and theranostics, such as active cargo delivery and microsurgical manipulations in hard-to-reach body sites. High-resolution imaging and control of cell-sized microrobots in the in vivo vascular system remains an unsolved challenge toward their clinical use. To overcome this limitation, we propose noninvasive real-time detection and tracking of circulating microrobots using optoacoustic imaging.

Rapid Volumetric Optoacoustic Tracking of Nanoparticle Kinetics across Murine Organs.

Large-scale visualization of nanoparticle kinetics is essential for optimizing drug delivery and characterizing toxicity associated with engineered nanomaterials. Real-time tracking of nanoparticulate agents across multiple murine organs is hindered with the currently available whole-body preclinical imaging systems due to limitations in contrast, sensitivity, spatial, or temporal resolution. Herein, we demonstrate rapid volumetric tracking of gold nanoagent kinetics and biodistribution in mice at a suborgan level with single-sweep volumetric optoacoustic tomography (sSVOT).

Rapid Volumetric Optoacoustic Tracking of Individual Microparticles Enabled by a NIR-Absorbing Gold-Carbon Shell.

Rapid volumetric visualization of circulating microparticles can facilitate new biomedical applications, such as blood flow characterization or targeted drug delivery. However, existing imaging modalities generally lack the sensitivity to detect the weak signals generated by individual micrometer-sized particles distributed across millimeter- to centimeter-scale depths in living mammalian tissues. Also, the temporal resolution is typically insufficient to track the particles in an entire three-dimensional region.

Binary photoacoustic tomography for improved vasculature imaging.

Significance: The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs.

Aim: Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature.

Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data.

The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data.

Non-local means improves total-variation constrained photoacoustic image reconstruction.

Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging.

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography.

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images.

Pulsed Laser Diode-Based Desktop Photoacoustic Tomography for Monitoring Wash-In and Wash-Out of Dye in Rat Cortical Vasculature.

Photoacoustic (PA) tomography (PAT) imaging is an emerging biomedical imaging modality useful in various preclinical and clinical applications. Custom-made circular ring array-based transducers and conventional bulky Nd:YAG/OPO lasers inhibit translation of the PAT system to clinics. Ultra-compact pulsed laser diodes (PLDs) are currently being used as an alternative source of near-infrared excitation for PA imaging.

PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics.

The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach.

High-speed, low-cost, pulsed-laser-diode-based second-generation desktop photoacoustic tomography system.

Bulky, expensive Nd:YAG lasers are used in conventional photoacoustic tomography (PAT) systems, making them difficult to translate into clinics. Moreover, real-time imaging is not feasible when a single-element ultrasound transducer is used with these low-pulse-repetition-rate lasers (10-100 Hz). Low-cost pulsed laser diodes (PLDs) can be used instead for photoacoustic imaging due to their high-pulse-repetition rates and compact size.

Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction.

Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, l -norm, and total-variation.

Spatially variant regularization based on model resolution and fidelity embedding characteristics improves photoacoustic tomography.

Photoacoustic tomography tends to be an ill-conditioned problem with noisy limited data requiring imposition of regularization constraints, such as standard Tikhonov (ST) or total variation (TV), to reconstruct meaningful initial pressure rise distribution from the tomographic acoustic measurements acquired at the boundary of the tissue. However, these regularization schemes do not account for nonuniform sensitivity arising due to limited detector placement at the boundary of tissue as well as other system parameters. For the first time, two regularization schemes were developed within the Tikhonov framework to address these issues in photoacoustic imaging.

Image-guided filtering for improving photoacoustic tomographic image reconstruction.

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography.

Purpose: Development of simple and computationally efficient extrapolated Tikhonov filtering reconstruction methods for photoacoustic tomography.

Methods: The model-based reconstruction algorithms in photoacoustic tomography typically utilize Tikhonov regularization scheme for the reconstruction of initial pressure distribution from the measured boundary acoustic data. The automated choice of regularization parameter in these cases is computationally expensive.

Calibrating reconstruction radius in a multi single-element ultrasound-transducer-based photoacoustic computed tomography system.

In a circular scanning photoacoustic computed tomography (PAT/PACT) system, a single-element ultrasound transducer (SUT) (rotates in full 360° around the sample) or a full-ring array transducer is used to acquire the photoacoustic (PA) data from the target object. SUT takes several minutes to acquire the PA data, whereas the full-ring array transducer takes only few seconds. Hence, for real-time imaging, full-ring circular array transducers are preferred.

Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography.

As limited data photoacoustic tomographic image reconstruction problem is known to be ill-posed, the iterative reconstruction methods were proven to be effective in terms of providing good quality initial pressure distribution. Often, these iterative methods require a large number of iterations to converge to a solution, in turn making the image reconstruction procedure computationally inefficient. In this work, two variants of vector polynomial extrapolation techniques were deployed to accelerate two standard iterative photoacoustic image reconstruction algorithms, including regularized steepest descent and total variation regularization methods.

Deep neural network-based bandwidth enhancement of photoacoustic data.

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network.

A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging.

In vivo small-animal imaging has an important role to play in preclinical studies. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality that shows great potential for both preclinical and clinical applications. Conventional optical parametric oscillator-based PAT (OPO-PAT) systems are bulky and expensive and cannot provide high-speed imaging.