GPU-based acceleration and mesh optimization of finite-element-method-based quantitative photoacoustic tomography: a step towards clinical applications

Monitoring neonatal brain hemorrhage progression by photoacoustic tomography

Neonatal brain hemorrhage (NBH) is the most common neurological disorder in neonates and its clinical interventions are very limited. Understanding the pathology of NBH by non-invasive in-vivo characterization of standardized animal models is essential for developing potential treatments. Currently, there is no suitable tool to provide non-invasive, non-ionizing dynamic imaging of neonatal mouse models with high resolution, high contrast, and deep imaging depth. In this study, we implemented a fast 3D photoacoustic tomography (PAT) system suitable for imaging neonatal mouse brains with good image quality and demonstrated its feasibility in non-invasive monitoring of the dynamic process of NBH in the whole neonatal mouse brain. The results present a high resolution and sensitivity for NBH detection. Both morphological and hemodynamic changes of the hematoma were accurately obtained. Our results demonstrated the potential of PAT as a powerful tool for the preclinical study of neonatal brain hemorrhage.

Enhancing Finite Element-Based Photoacoustic Tomography by Localized Reconstruction Method

Iterative reconstruction algorithm based on finite element (FE) modeling is a powerful approach in photoacoustic tomography (PAT). However, an iterative inverse algorithm using conventional FE meshing of the entire imaging zone is computationally demanding, which hinders this powerful tool in applications where quick image acquisition and/or a large image matrix is needed. To address this challenge, parallel computing techniques are proposed and implemented in the field. Here, we present an alternative approach for 2D PAT, which locoregionally reconstructs the region of interest (ROI) instead of the full imaging zone. Our simulated and phantom experimental results demonstrate that this ROI reconstruction algorithm can produce almost the same image quality as the conventional full zone-based reconstruction algorithm; however, the computation time can be significantly reduced without any additional hardware cost by more than two orders of magnitude (100-fold). This algorithm is further applied and validated in an in vivo study. The major vessel structures in a rat’s brain can be imaged clearly using our ROI-based approach, coupled with a mesh of 11,801 nodes. This novel algorithm can also be parallelized using MPI or GPU acceleration techniques to further enhance the reconstruction performance of FE-based PAT.

Model-Based Reconstruction of Large Three-Dimensional Optoacoustic Datasets

Iterative model-based algorithms are known to enable more accurate and quantitative optoacoustic (photoacoustic) tomographic reconstructions than standard back-projection methods. However, three-dimensional (3D) model-based inversion is often hampered by high computational complexity and memory overhead. Parallel implementations on a graphics processing unit (GPU) have been shown to efficiently reduce the memory requirements by on-the-fly calculation of the actions of the optoacoustic model matrix, but the high complexity still makes these approaches impractical for large 3D optoacoustic datasets. Herein, we show that the computational complexity of 3D model-based iterative inversion can be significantly reduced by splitting the model matrix into two parts: one maximally sparse matrix containing only one entry per voxel-transducer pair and a second matrix corresponding to cyclic convolution. We further suggest reconstructing the images by multiplying the transpose of the model matrix calculated in this manner with the acquired signals, which is equivalent to using a very large regularization parameter in the iterative inversion method. The performance of these two approaches is compared to that of standard back-projection and a recently introduced GPU-based model-based method using datasets from in vivo experiments. The reconstruction time was accelerated by approximately an order of magnitude with the new iterative method, while multiplication with the transpose of the matrix is shown to be as fast as standard back-projection.

In-vivo hemodynamic imaging of acute prenatal ethanol exposure in fetal brain by photoacoustic tomography

Prenatal ethanol exposure (PEE) can lead to structural and functional abnormalities in fetal brain. Although neural developmental deficits due to PEE have been recognized, the immediate effects of PEE on fetal brain vasculature and hemodynamics remain poorly understood. One of the major obstacles that preclude the rapid advancement of studies on fetal vascular dynamics is the limitation of the imaging techniques. Thus, a technique for noninvasive in-vivo imaging of fetal vasculature and hemodynamics is desirable. In this study, we explored the dynamic changes of the vessel dimeter, density and oxygen saturation in fetal brain after acute maternal ethanol exposure in the second-trimester equivalent murine model using a real-time photoacoustic tomography system we developed for imaging embryo of small animals. The results indicate a significant decrease in fetal brain vessel diameter, perfusion and oxygen saturation. This work demonstrated that PAT can provide high-resolution noninvasive imaging ability to monitor fetal vascular dynamics.

Fan-shaped scanning approach for miniaturized photoacoustic tomography

We describe a novel scanning approach for miniaturized photoacoustic tomography (PAT), based on fan-shaped scanning of a single transducer at one or two discrete positions. This approach is tested and evaluated using several phantom and animal experiments. The results obtained show that this new scanning approach provides high image quality in the configuration of miniaturized handheld or endoscopic PAT with improved effective field of view and penetration depth.

Graphics processing unit accelerating compressed sensing photoacoustic computed tomography with total variation

Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24-31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method.

Thermoacoustic tomography of germinal matrix hemorrhage in neonatal mouse cerebrum

BACKGROUND

Microwave-induced thermoacoustic tomography (TAT) has potential for detecting germinal matrix hemorrhage (GMH). However, it has not been demonstrated in vivo.

OBJECTIVE

To demonstrate the feasibility of TAT for in vivo detecting GMH by using neonatal mouse.

METHODS

A cylindrical-scanning TAT system was developed with optimized microwave irradiation and ultrasound detection for neonatal mouse imaging. Neonatal mice were used to develop GMH model by injection of autologous blood into the periventricular region. After TAT experiments, the animals were sacrificed, frozen and excised to validate the TAT findings. The detailed comparative analyses of the TAT images and corresponding photographs of the excised brain tissues were conducted.

RESULTS

Satisfactory matches are identified between the TAT images and corresponding histological sections, in terms of the shape and size of the brain tissues. Some organs and tissues were also identified. Particularly, comparing to the corresponding histological sections, using TAT enables to more accurately detect the hematoma region at different depths in the neonatal mouse brain.

CONCLUSIONS

This study demonstrates for the first time that TAT can detect GMH in neonatal mouse cerebrum in vivo. This represents the first important step towards the in vivo diagnosis and grading of hemorrhage in the infant human brain.

Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy

Photoacoustic microscopy (PAM) is an emerging biomedical imaging technology capable of quantitative measurement of the microvascular blood flow by correlation analysis. However, the computational cost is high, limiting its applications. Here, we report a parallel computation design based on graphics processing unit (GPU) for high-speed quantification of blood flow in PAM. Two strategies were utilized to improve the computational efficiency. First, the correlation method in the algorithm was optimized to avoid redundant computation and a parallel computing structure was designed. Second, the parallel design was realized on GPU and optimized by maximizing the utilization of computing resource in GPU. The detailed timings and speedup for each calculation step were given and the MATLAB and C/C++ code versions based on CPU were presented as a comparison. Full performance test shows that a stable speedup of ~80-fold could be achieved with the same calculation accuracy and the computation time could be reduced from minutes to just several seconds with the imaging size ranging from 1 × 1 mm² to 2 × 2 mm². Our design accelerates PAM-based blood flow measurement and paves the way for real-time PAM imaging and processing by significantly improving the computational efficiency.

GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays

Double-stage delay-multiply-and-sum (DS-DMAS) is an algorithm proposed for photoacoustic image reconstruction. The DS-DMAS algorithm offers a higher contrast than conventional delay-and-sum and delay-multiply and-sum but at the expense of higher computational complexity. Here, we utilized a compute unified device architecture (CUDA) graphics processing unit (GPU) parallel computation approach to address the high complexity of the DS-DMAS for photoacoustic image reconstruction generated from a commercial light-emitting diode (LED)-based photoacoustic scanner. In comparison with a single-threaded central processing unit (CPU), the GPU approach increased speeds by nearly 140-fold for 1024 × 1024 pixel image; there was no decrease in accuracy. The proposed implementation makes it possible to reconstruct photoacoustic images with frame rates of 250, 125, and 83.3 when the images are 64 × 64, 128 × 128, and 256 × 256, respectively. Thus, DS-DMAS can be efficiently used in clinical devices when coupled with CUDA GPU parallel computation.

Miniature fluorescence molecular tomography (FMT) endoscope based on a MEMS scanning mirror and an optical fiberscope

We present a novel FMT endoscope by using a MEMS scanning mirror and an optical fiberscope. The diameter of this highly miniaturized FMT device is only 5 mm. To our knowledge, this is the smallest FMT device we found so far. Several phantom experiments based on indocyanine green (ICG) were conducted to demonstrate the imaging ability of this device. Two tumor-bearing mice were systematically injected with tumor-targeted NIR fluorescent probes (ATF-PEG-IO-830) and were then imaged to further demonstrate the ability of this FMT endoscope for imaging small animals.

Recent progress in photoacoustic molecular imaging

By acoustically detecting the optical absorption contrast, photoacoustic (PA) tomography (PAT) has broken the penetration limits of traditional high-resolution optical imaging. Through spectroscopic analysis of the target's optical absorption, PAT can identify a wealth of endogenous and exogenous molecules and thus is inherently capable of molecular imaging with high sensitivity. PAT's molecular sensitivity is uniquely accompanied by non-ionizing radiation, high spatial resolution, and deep penetration in biological tissues, which other optical imaging modalities cannot achieve yet. In this concise review, we summarize the most recent technological advancements in PA molecular imaging and highlight the novel molecular probes specifically made for PAT in deep tissues. We conclude with a brief discussion of the opportunities for future advancements.

Toward whole-body quantitative photoacoustic tomography of small-animals with multi-angle light-sheet illuminations

Several attempts to achieve the quantitative photoacoustic tomography (q-PAT) have been investigated using point sources or a single-angle wide-field illumination. However, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification in imaging applications on large-size domains, due to the limitation of ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and calibrated recovering-and-averaging iterations. The scheme can obtain more complete information on the intrinsic absorption from the multi-angle illumination mode, and collect SNR-boosted photoacoustic signals in the selected planes from the wide-field light-sheet excitation. Therefore, the sliced absorption maps over whole body of small-animals can be recovered in a measurement-flexible, noise-robust and computation-economic way. The proposed approach is validated by phantom, ex vivo and in vivo experiments, exhibiting promising performances in image fidelity and quantitative accuracy for practical applications.