Optimal self-calibration of tomographic reconstruction parameters in whole-body small animal optoacoustic imaging

Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography

Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.

Enabling the autofocus approach for parameter optimization in planar measurement geometry clinical optoacoustic imaging

In optoacoustic (photoacoustic) tomography, several parameters related to tissue and detector features are needed for image formation, but they may not be known a priori. An autofocus (AF) algorithm is generally used to estimate these parameters. However, the algorithm works iteratively and is therefore impractical for clinical imaging with planar geometry systems due to the long reconstruction times. We have developed a fast autofocus (FAF) algorithm for 3D optoacoustic systems with planar geometry. Such an algorithm exploits the symmetries of the planar geometry and a virtual source concept to reduce the dimensionality of the parameter estimation problem. The dimensionality reduction makes FAF much simpler computationally than the conventional AF algorithm. We show that the FAF algorithm required about 5 s to provide accurate estimates of the speed of sound in simulated data and experimental data obtained using an imaging system that is poised to enter the clinic. The applicability of FAF for estimating other image formation parameters is discussed. We expect the FAF algorithm to contribute decisively to the clinical use of optoacoustic tomography systems with planar geometry.

Video-rate full-ring ultrasound and photoacoustic computed tomography with real-time sound speed optimization

Full-ring dual-modal ultrasound and photoacoustic imaging provide complementary contrasts, high spatial resolution, full view angle and are more desirable in pre-clinical and clinical applications. However, two long-standing challenges exist in achieving high-quality video-rate dual-modal imaging. One is the increased data processing burden from the dense acquisition. Another one is the object-dependent speed of sound variation, which may cause blurry, splitting artifacts, and low imaging contrast. Here, we develop a video-rate full-ring ultrasound and photoacoustic computed tomography (VF-USPACT) with real-time optimization of the speed of sound. We improve the imaging speed by selective and parallel image reconstruction. We determine the optimal sound speed via co-registered ultrasound imaging. Equipped with a 256-channel ultrasound array, the dual-modal system can optimize the sound speed and reconstruct dual-modal images at 10 Hz in real-time. The optimized sound speed can effectively enhance the imaging quality under various sample sizes, types, or physiological states. In animal and human imaging, the system shows co-registered dual contrasts, high spatial resolution (140 µm), single-pulse photoacoustic imaging (< 50 µs), deep penetration (> 20 mm), full view, and adaptive sound speed correction. We believe VF-USPACT can advance many real-time biomedical imaging applications, such as vascular disease diagnosing, cancer screening, or neuroimaging.

Multi-segmented feature coupling for jointly reconstructing initial pressure and speed of sound in photoacoustic computed tomography

SIGNIFICANCE

Photoacoustic computed tomography (PACT) is a fast-growing imaging modality. In PACT, the image quality is degraded due to the unknown distribution of the speed of sound (SoS). Emerging initial pressure (IP) and SoS joint-reconstruction methods promise reduced artifacts in PACT. However, previous joint-reconstruction methods have some deficiencies. A more effective method has promising prospects in preclinical applications.

AIM

We propose a multi-segmented feature coupling (MSFC) method for SoS-IP joint reconstruction in PACT.

APPROACH

In the proposed method, the ultrasound detectors were divided into multiple sub-arrays with each sub-array and its opposite counterpart considered to be a pair. The delay and sum algorithm was then used to reconstruct two images based on a subarray pair and estimated a direction-specific SoS, based on image correlation and the orientation of the subarrays. Once the data generated by all pairs of subarrays were processed, an image that was optimized in terms of minimal feature splitting in all directions was generated. Further, based on the direction-specific SoS, a model-based method was used to directly reconstruct the SoS distribution.

RESULTS

Both phantom and animal experiments demonstrated feasibility and showed promising results compared with conventional methods, with less splitting and blurring and fewer distortions.

CONCLUSIONS

The developed MSFC method shows promising results for both IP and SoS reconstruction. The MSFC method will help to optimize the image quality of PACT in clinical applications.

Automatic 3-D segmentation and volumetric light fluence correction for photoacoustic tomography based on optimal 3-D graph search

Preclinical imaging with photoacoustic tomography (PAT) has attracted wide attention in recent years since it is capable of providing molecular contrast with deep imaging depth. The automatic extraction and segmentation of the animal in PAT images is crucial for improving image analysis efficiency and enabling advanced image post-processing, such as light fluence (LF) correction for quantitative PAT imaging. Previous automatic segmentation methods are mostly two-dimensional approaches, which failed to conserve the 3-D surface continuity because the image slices were processed separately. This discontinuity problem further hampers LF correction, which, ideally, should be carried out in 3-D due to spatially diffused illumination. Here, to solve these problems, we propose a volumetric auto-segmentation method for small animal PAT imaging based on the 3-D optimal graph search (3-D GS) algorithm. The 3-D GS algorithm takes into account the relation among image slices by constructing a 3-D node-weighted directed graph, and thus ensures surface continuity. In view of the characteristics of PAT images, we improve the original 3-D GS algorithm on graph construction, solution guidance and cost assignment, such that the accuracy and smoothness of the segmented animal surface were guaranteed. We tested the performance of the proposed method by conducting in vivo nude mice imaging experiments with a commercial preclinical cross-sectional PAT system. The results showed that our method successfully retained the continuous global surface structure of the whole 3-D animal body, as well as smooth local subcutaneous tumor boundaries at different development stages. Moreover, based on the 3-D segmentation result, we were able to simulate volumetric LF distribution of the entire animal body and obtained LF corrected PAT images with enhanced structural visibility and uniform image intensity.

Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction

In the field of reproductive biology, there is a strong need for a suitable tool capable of non-destructive evaluation of oocyte viability and function. We studied the application of brilliant cresyl blue (BCB) as an intra-vital exogenous contrast agent using multispectral optoacoustic tomography (MSOT) for visualization of porcine ovarian follicles. The technique provided excellent molecular sensitivity, enabling the selection of competent oocytes without disrupting the follicles. We further conducted in vitro embryo culture, molecular analysis (real-time and reverse transcriptase polymerase chain reaction) and DNA fragmentation analysis to comprehensively establish the safety of BCB-enhanced MSOT imaging in monitoring oocyte viability. Overall, the experimental results suggest that the method offers a significant advance in the use of contrast agents and molecular imaging for reproductive studies. Our technique improves the accurate prediction of ovarian reserve significantly and, once standardized for in vivo imaging, could provide an effective tool for clinical infertility management.

Enhancement of photoacoustic tomography of acoustically inhomogeneous tissue by utilizing a memory effect

One of the major challenges for photoacoustic tomography is the variance of the speed of sound (SOS) in realistic tissue, which could lead to defocusing in image reconstruction and degrade the reconstructed image. In this study, we propose a method to optimize the SOS used for image reconstruction based on a memory effect of photoacoustic signal. We reveal that the photoacoustic signals received by two adjacent transducers have a high degree of similarity in waveform, while a time delay exists between them. The time delay is related to the SOS. Based on this physical phenomenon, an iterative operation is implemented to estimate the SOS used for image reconstruction. Both simulations and experiments confirm that the method significantly enhances the reconstructed image in inhomogeneous tissue. This study may have potential value in improving the performance of photoacoustic tomography in biomedical applications.

In vivo optoacoustic monitoring of percutaneous laser ablation of tumors in a murine breast cancer model

Laser ablation (LA) is a promising approach for minimally invasive cancer treatments. Its in vivo applicability is often impeded by the lack of efficient monitoring tools that can help to minimize collateral tissue damage and aid in determining the optimal treatment end-points. We have devised a new, to the best of our knowledge, hybrid LA approach combining simultaneous volumetric optoacoustic (OA) imaging to monitor the lesion progression accurately in real time and 3D. Time-lapse imaging of laser ablation of solid tumors was performed in a murine breast cancer model in vivo by irradiation of subcutaneous tumors with a 100 mJ short-pulsed (${\sim}{5}\;{\rm ns}$∼5ns) laser operating at 1064 nm and 100 Hz pulse repetition frequency. Local changes in the OA signal intensity ascribed to structural alterations in the tumor vasculature were clearly observed, while the OA volumetric projections recorded in vivo appeared to correlate with cross sections of the excised tumors.

Short-wavelength optoacoustic spectroscopy based on water muting

Infrared (IR) optoacoustic spectroscopy can separate a multitude of molecules based on their absorption spectra. However, the technique is limited when measuring target molecules in aqueous solution by strong water absorption at IR wavelengths, which reduces detection sensitivity. Based on the dependence of optoacoustic signal on the temperature of the probed medium, we introduce cooled IR optoacoustic spectroscopy (CIROAS) to mute water contributions in optoacoustic spectroscopy. We showcase that spectral measurements of proteins, lipids, and glucose in the short-wavelength IR region, performed at 4 °C, lead to marked sensitivity improvements over conventional optoacoustic or IR spectroscopy. We elaborate on the dependence of optoacoustic signals on water temperature and demonstrate polarity changes in the recorded signal at temperatures below 4 °C. We further elucidate the dependence of the optoacoustic signal and the muting temperature on sample concentration and demonstrate that changes in these dependences enable quantification of the solute concentration. We discuss how CIROAS may enhance abilities for molecular sensing in the IR.

Development of a Stationary 3D Photoacoustic Imaging System Using Sparse Single-Element Transducers: Phantom Study

Photoacoustic imaging (PAI) is an emerging label-free and non-invasive modality for imaging biological tissues. PAI has been implemented in different configurations, one of which is photoacoustic computed tomography (PACT) with a potential wide range of applications, including brain and breast imaging. Hemispherical Array PACT (HA-PACT) is a variation of PACT that has solved the limited detection-view problem. Here, we designed an HA-PACT system consisting of 50 single element transducers. For implementation, we initially performed a simulation study, with parameters close to those in practice, to determine the relationship between the number of transducers and the quality of the reconstructed image. We then used the greatest number of transducers possible on the hemisphere and imaged copper wire phantoms coated with a light absorbing material to evaluate the performance of the system. Several practical issues such as light illumination, arrangement of the transducers, and an image reconstruction algorithm have been comprehensively studied.

Feature coupling photoacoustic computed tomography for joint reconstruction of initial pressure and sound speed in vivo

Photoacoustic imaging relies on diffused photons for optical contrast and diffracted ultrasound for high resolution. As a tomographic imaging modality, often an inverse problem of acoustic diffraction needs to be solved to reconstruct a photoacoustic image. The inverse problem is complicated by the fact that the acoustic properties, including the speed of sound distribution, in the image field of view are unknown. During reconstruction, subtle changes of the speed of sound in the acoustic ray path may accumulate and give rise to noticeable blurring in the image. Thus, in addition to the ultrasound detection bandwidth, inaccurate acoustic modeling, especially the unawareness of the speed of sound, defines the image resolution and influences image quantification. Here, we proposed a method termed feature coupling to jointly reconstruct the speed of sound distribution and a photoacoustic image with improved sharpness, at no additional hardware cost. Simulations, phantom studies, and in vivo experiments demonstrated the effectiveness and reliability of our method.

Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image Reconstruction

OBJECTIVE

Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise, and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of this work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging.

METHODS

We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior-based fluence correction.

RESULTS

We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms, and in-vivo samples.

CONCLUSION

The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures.

SIGNIFICANCE

Our method can enable quantitative optoacoustic imaging, and has the potential to improve preclinical and translational imaging applications.

Visual Quality Enhancement in Optoacoustic Tomography Using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures as well as for detection and evaluation of tissue pathologies. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities in the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.

Optoacoustic Tomography Using Accelerated Sparse Recovery and Coherence Factor Weighting

Sparse recovery algorithms have shown great potential to accurately reconstruct images using limited-view optoacoustic (photoacoustic) tomography data sets, but these are computationally expensive. In this paper, we propose an improvement of the fast converging Split Augmented Lagrangian Shrinkage Algorithm method based on least square QR inversion for improving the reconstruction speed. We further show image fidelity improvement when using a coherence factor to weight the reconstruction result. Phantom and in vivo measurements show that the accelerated Split Augmented Lagrangian Shrinkage Algorithm method with coherence factor weighting offers images with reduced artifacts and provides faster convergence compared with existing sparse recovery algorithms.

Improving Optoacoustic Image Quality via Geometric Pixel Super-Resolution Approach

High fidelity optoacoustic (photoacoustic) tomography requires dense spatial sampling of optoacoustic signals using point acoustic detectors. However, in practice, spatial resolution of the images is often limited by limited sampling either due to coarse multi-element arrays or time in raster scan measurements. Herein, we investigate a method that integrates information from multiple optoacoustic images acquired at sub-diffraction steps into one high resolution image by means of an iterative registration algorithm. Experimental validations performed in target phantoms and ex vivo tissue samples confirm that the suggested approach renders significant improvements in terms of optoacoustic image resolution and quality without introducing significant alterations into the signal acquisition hardware or inversion algorithms.

Multiscale edge detection and parametric shape modeling for boundary delineation in optoacoustic images

In this article, we present a novel scheme for segmenting the image boundary (with the background) in optoacoustic small animal in vivo imaging systems. The method utilizes a multiscale edge detection algorithm to generate a binary edge map. A scale dependent morphological operation is employed to clean spurious edges. Thereafter, an ellipse is fitted to the edge map through constrained parametric transformations and iterative goodness of fit calculations. The method delimits the tissue edges through the curve fitting model, which has shown high levels of accuracy. Thus, this method enables segmentation of optoacoutic images with minimal human intervention, by eliminating need of scale selection for multiscale processing and seed point determination for contour mapping.

Optoacoustic image segmentation based on signal domain analysis

Efficient segmentation of optoacoustic images has importance in enhancing the diagnostic and quantification capacity of this modality. It may also aid in improving the tomographic reconstruction accuracy by accounting for heterogeneous optical and acoustic tissue properties. In particular, when imaging through complex biological tissues, the real acoustic properties often deviate considerably from the idealized assumptions of homogenous conditions, which may lead to significant image artifacts if not properly accounted for. Although several methods have been proposed aiming at estimating and accounting for the complex acoustic properties in the image domain, accurate delineation of structures is often hindered by low contrast of the images and other artifacts produced due to incomplete tomographic coverage and heuristic assignment of the tissue properties during the reconstruction process. In this letter, we propose instead a signal domain analysis approach that retrieves acoustic properties of the object to be reconstructed from characteristic features of the detected optoacoustic signals prior to image reconstruction. Performance of the proposed method is first tested in simulation and experiment using two-dimensional tissue-mimicking phantoms. Significant improvements in the segmentation abilities and overall reconstructed image quality are further showcased in experimental cross-sectional data acquired from a human finger.

Whole-body live mouse imaging by hybrid reflection-mode ultrasound and optoacoustic tomography

We present a hybrid preclinical imaging scanner that optimally supports image acquisition in both reflection-mode ultrasonography and optoacoustic (OA) tomography modes. The system comprises a quasi-full-ring tomographic geometry capable of the simultaneous dual-mode imaging through entire cross sections of mice with in-plane spatial resolution in the range of 150 and 350 μm in the respective OA and ultrasound (US) imaging modes with an imaging speed of up to 10 two-dimensional frames per second. Three-dimensional whole-body data is subsequently rendered by rapid scanning of the imaged plane. The system further incorporates rapid laser wavelength tuning for real-time acquisition of multispectral OA data, which enables studies of longitudinal dynamics as well as fast kinetics and biodistribution of contrast agents. In vivo imaging performance is demonstrated by label-free hybrid anatomical scans through living mice, as well as real-time visualization of optical contrast agent perfusion. By setting new standards for whole-body tomographic imaging performance in both the OA and pulse-echo US modes, the developed hybrid imaging approach is expected to benefit numerous applications where the availability of high-quality structural information provided by the tomographic reflection-mode US can ease interpretation of the functional and molecular imaging results attained by the OA modality.