Photoacoustic Image Reconstruction Research Articles

Exponential filtering of singular values improves photoacoustic image reconstruction

Model-based image reconstruction techniques yield better quantitative accuracy in photoacoustic image reconstruction. In this work, an exponential filtering of singular values was proposed for carrying out the image reconstruction in photoacoustic tomography. The results were compared with widely popular Tikhonov regularization, time reversal, and the state of the art least-squares QR-based reconstruction algorithms for three digital phantom cases with varying signal-to-noise ratios of data. It was shown that exponential filtering provides superior photoacoustic images of better quantitative accuracy. Moreover, the proposed filtering approach was observed to be less biased toward the regularization parameter and did not come with any additional computational burden as it was implemented within the Tikhonov filtering framework. It was also shown that the standard Tikhonov filtering becomes an approximation to the proposed exponential filtering.

Efficient block-sparse model-based algorithm for photoacoustic image reconstruction

The model-based algorithm for photoacoustic imaging (PAI) has been proved to be stable and accurate. However, its reconstruction is computationally burdensome which limits its application in the practical PAI. In this paper, we proposed a block-sparse discrete cosine transform (BS-DCT) model-based PAI reconstruction algorithm in order to improve the computational efficiency of the model-based PAI reconstruction. We adopted the discrete cosine transform (DCT) to eliminate the minor coefficients and reduce the data scale. A block-sparse based iterative method was proposed to accomplish the image reconstruction. Due to its block independent nature, we used the CPU-based parallel calculation implementation to accelerate the reconstruction. During the iterative reconstruction, the number of required iterations was reduced by adopting the fast-converging optimization Barzilai–Borwein method. The numerical simulations and in-vitro experiments were carried out. The results has shown that the reconstruction quality is equivalent to the state-of-the-art iterative algorithms. Our algorithm requires less number of iterations with a reduced data scale and significant acceleration through the parallel calculation implementation. In conclusion, the BS-DCT algorithm may be an effectively accelerated practical algorithm for the PAI reconstruction.

Frequency analysis for an extended photoacoustic transport model.

In photoacoustic imaging, the signal attenuation is a well-known source of artifacts over the image reconstruction. It is recognized that this is caused by optical absorption effects and by the ultrasound broadband scattering. However, the sound dispersion is generally neglected, although it appears notably in thick or heterogeneous tissues. In the present Letter, we give an experimental example in which both attenuation and sound dispersion are dealt with as relevant features to be taken into consideration. An analytic perspective of these perturbations leads us to a waveform transport-model extension that provides a linear description of the induced acoustic effects. We find a near match between the theoretical predictions and the experimental results in the frequency domain. These outcomes approximate projection data that represent forward solutions in photoacoustic image reconstruction.

Photoacoustic image quality enhancement by estimating mean sound speed based on optimum focusing

A constant sound speed (i.e., 1540 m/s) is generally used in various ultrasound (US) and photoacoustic (PA) image reconstruction algorithms based on the assumption of a homogeneous sound speed in human tissue. However, the variation of the sound speed in human tissue can be as great as 10%, which can lead to low contrast, distortion, and blurring in reconstruction images. We proposed an automatic method of selecting the mean sound speed based on optimum focusing of received PA signals to enhance the quality of reconstructed PA images. Optimum focusing is quantified by calculating the minimum sum of the deviation of beamformed PA signals from their mean value for various sound speeds. The proposed method was demonstrated by homogeneous and heterogeneous sound speed simulation models, and also evaluated by experiments with agar and porcine tissue-mimicking phantoms. The central vertical and lateral profiles of reconstructed absorbers verified the improvement of contrast, signal-to-noise ratio (SNR), and spatial resolution by using the estimated mean sound speed.

Multigrid-based reconstruction algorithm for quantitative photoacoustic tomography.

This paper proposes a multigrid inversion framework for quantitative photoacoustic tomography reconstruction. The forward model of optical fluence distribution and the inverse problem are solved at multiple resolutions. A fixed-point iteration scheme is formulated for each resolution and used as a cost function. The simulated and experimental results for quantitative photoacoustic tomography reconstruction show that the proposed multigrid inversion can dramatically reduce the required number of iterations for the optimization process without loss of reliability in the results.

An adaptive filtered back-projection for photoacoustic image reconstruction.

The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors' new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal's contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing the correct signal strength of the absorbers. The reconstructed image of the second phantom further demonstrates the capability to form clear images of the spheres with sharp borders in the overlapping geometry. The smallest sphere is clearly visible and distinguishable, even though it is surrounded by two big spheres. In addition, image reconstructions were conducted with randomized noise added to the observed signals to mimic realistic experimental conditions. The authors have developed a new FBP algorithm that is capable for reconstructing high quality images with correct relative intensities and sharp borders for PAT. The results demonstrate that the weighting function serves as a precise ramp filter for processing the observed signals in the Fourier domain. In addition, this algorithm allows an adaptive determination of the cutoff frequency for the applied low pass filter.

Objective assessment and design improvement of a staring, sparse transducer array by the spatial crosstalk matrix for 3D photoacoustic tomography.

Accurate reconstruction of 3D photoacoustic (PA) images requires detection of photoacoustic signals from many angles. Several groups have adopted staring ultrasound arrays, but assessment of array performance has been limited. We previously reported on a method to calibrate a 3D PA tomography (PAT) staring array system and analyze system performance using singular value decomposition (SVD). The developed SVD metric, however, was impractical for large system matrices, which are typical of 3D PAT problems. The present study consisted of two main objectives. The first objective aimed to introduce the crosstalk matrix concept to the field of PAT for system design. Figures-of-merit utilized in this study were root mean square error, peak signal-to-noise ratio, mean absolute error, and a three dimensional structural similarity index, which were derived between the normalized spatial crosstalk matrix and the identity matrix. The applicability of this approach for 3D PAT was validated by observing the response of the figures-of-merit in relation to well-understood PAT sampling characteristics (i.e. spatial and temporal sampling rate). The second objective aimed to utilize the figures-of-merit to characterize and improve the performance of a near-spherical staring array design. Transducer arrangement, array radius, and array angular coverage were the design parameters examined. We observed that the performance of a 129-element staring transducer array for 3D PAT could be improved by selection of optimal values of the design parameters. The results suggested that this formulation could be used to objectively characterize 3D PAT system performance and would enable the development of efficient strategies for system design optimization.

Recent Progress in Multimodal Photoacoustic Tomography

AbstractSince its first demonstration of functional imaging in small animals about a decade ago, photoacoustic tomography (PAT) has quickly become one of the fastest growing biomedical imaging modalities. Combining optical excitation with acoustic detection, PAT can provide detailed images of tissues deep in the body. While PAT technology continues to improve significantly, substantial efforts have also been made to develop multimodal PAT systems. These systems not only provide complementary information for more comprehensive characterization of tissue, they also generate data that can be used to further improve PAT reconstruction. This review will present current progress in multimodal PAT imaging, focusing on the technical aspects of integration and its applications in biomedicine.

Image reconstruction of the absorption coefficients with l 1-norm minimization from photoacoustic measurements.

The photoacoustic (PA) imaging by considering light propagation into image reconstruction process can provide quantitative information of photon absorbers, such as hemoglobin and exogenous dyes, and to improve their imaging contrasts. A 2D image reconstruction of the distribution of the light absorption coefficient from the PA measurements with light source and ultrasound transducer placed at the identical position was tested. The PA pressures were formulated with the PA wave equation and the photon diffusion equation. The relation between the PA pressure and the absorption coefficient was linearized. The image reconstruction was carried out by minimizing the squared error between the measured and calculated PA signals. The l 1-norm of the reconstructed image was simultaneously minimized to improve the localization of the reconstructed target in the image. The image reconstruction with the l 1-norm minimization was compared to that with the Tikhonov regularization by numerical simulation and phantom experiment. In phantom experiment, an aqueous solution of the intralipid and the indocyanine green was used as the measured object. The PA probe had optical fiber for illumination and piezoelectric film for detection placed at the identical position. The l 1-norm minimization reconstructed more localized target than the Tikhonov regularization. The l 1-norm minimization is useful for the sparse PA image reconstruction.

A photoacoustic image reconstruction method using total variation and nonconvex optimization.

BackgroundIn photoacoustic imaging (PAI), the reduction of scanning time is a major concern for PAI in practice. A popular strategy is to reconstruct the image from the sparse-view sampling data. However, the insufficient data leads to reconstruction quality deteriorating. Therefore, it is very important to enhance the quality of the sparse-view reconstructed images.MethodIn this paper, we proposed a joint total variation and Lp-norm (TV-Lp) based image reconstruction algorithm for PAI. In this algorithm, the reconstructed image is updated by calculating its total variation value and Lp-norm value. Along with the iteration, an operator-splitting framework is utilized to reduce the computational cost and the Barzilai-Borwein step size selection method is adopted to obtain the faster convergence.Results and conclusionThrough the numerical simulation, the proposed algorithm is validated and compared with other widely used PAI reconstruction algorithms. It is revealed in the simulation result that the proposed algorithm may be more accurate than the other algorithms. Moreover, the computational cost, the convergence, the robustness to noises and the tunable parameters of the algorithm are all discussed respectively. We also implement the TV-Lp algorithm in the in-vitro experiments to verify its performance in practice. Through the numerical simulations and in-vitro experiments, it is demonstrated that the proposed algorithm enhances the quality of the reconstructed images with faster calculation speed and convergence.

Handheld photoacoustic tomography probe built using optical-fiber parallel acoustic delay lines.

The development of the first miniaturized parallel acoustic delay line (PADL) probe for handheld photoacoustic tomography (PAT) is reported. Using fused-silica optical fibers with low acoustic attenuation, we constructed two arrays of eight PADLs. Precision laser micromachining was conducted to produce robust and accurate mechanical support and alignment structures for the PADLs, with minimal acoustic distortion and interchannel coupling. The 16 optical-fiber PADLs, each with a different time delay, were arranged to form one input port and two output ports. A handheld PADL probe was constructed using two single-element transducers and two data acquisition channels (equal to a channel reduction ratio of 8∶1). Photoacoustic (PA) images of a black-ink target embedded in an optically scattering phantom were successfully acquired. After traveling through the PADLs, the eight channels of differently time-delayed PA signals reached each single-element ultrasonic transducer in a designated nonoverlapping time series, allowing clear signal separation for PA image reconstruction. Our results show that the PADL technique and the handheld probe can potentially enable real-time PAT, while significantly reducing the complexity and cost of the ultrasound receiver system.

Compressive sampling photoacoustic tomography based on edge expander codes and TV regularization

A new photoacoustic (PA) signal sampling and image reconstruction method, called compressive sampling PA tomography (CSPAT), is recently proposed to make low sampling rate and high-resolution PA tomography possible. A key problem within the CSPAT framework is the design of optic masks. We propose to use edge expander codes-based masks instead of the conventional random distribution masks, and efficient total variation (TV) regularization-based model to formulate the associated problem. The edge expander codes-based masks, corresponding to non-uniform sampling schemes, are validated by both theoretical analysis and results from computer simulations. The proposed method is expected to enhance the capability of CSPAT for reducing the number of measurements and fast data acquisition.

Ultrasound-guided photoacoustic image reconstruction: image completion and boundary suppression

Photoacoustic (PA) image reconstruction of data acquired with conventional linear arrays suffers from incompleteness due to limited bandwidth and limited view. This problem is compounded by the dominance of boundary signals suppressing other weaker PA sources. In this study, we propose a method that uses a naturally coregistered ultrasound image to enhance the PA image reconstruction, with the dual aim to reconstruct the sources that suffer from incompleteness and reveal weak sources near a strong boundary. In this method, an ultrasound image provides the input for a PA wave field simulation. The simulated PA field can be combined with the measured fields to complete the partial reconstruction or to suppress the dominant boundary signals. Experimental validation of the method was performed with two different phantoms and in vivo data from the lower arm of a healthy volunteer.

Real-time photoacoustic and ultrasound dual-modality imaging system facilitated with graphics processing unit and code parallel optimization

Photoacoustic tomography (PAT) offers structural and functional imaging of living biological tissue with highly sensitive optical absorption contrast and excellent spatial resolution comparable to medical ultrasound (US) imaging. We report the development of a fully integrated PAT and US dual-modality imaging system, which performs signal scanning, image reconstruction, and display for both photoacoustic (PA) and US imaging all in a truly real-time manner. The back-projection (BP) algorithm for PA image reconstruction is optimized to reduce the computational cost and facilitate parallel computation on a state of the art graphics processing unit (GPU) card. For the first time, PAT and US imaging of the same object can be conducted simultaneously and continuously, at a real-time frame rate, presently limited by the laser repetition rate of 10 Hz. Noninvasive PAT and US imaging of human peripheral joints in vivo were achieved, demonstrating the satisfactory image quality realized with this system. Another experiment, simultaneous PAT and US imaging of contrast agent flowing through an artificial vessel, was conducted to verify the performance of this system for imaging fast biological events. The GPU-based image reconstruction software code for this dual-modality system is open source and available for download from http://sourceforge.net/projects/patrealtime.

Efficient discrete cosine transform model–based algorithm for photoacoustic image reconstruction

The model-based algorithm is an effective reconstruction method for photoacoustic imaging (PAI). Compared with the analytical reconstruction algorithms, the model-based algorithm is able to provide a more accurate and high-resolution reconstructed image. However, the relatively heavy computational complexity and huge memory storage requirement often impose restrictions on its applications. We incorporate the discrete cosine transform (DCT) in PAI reconstruction and establish a new photoacoustic model. With this new model, an efficient algorithm is proposed for PAI reconstruction. Relatively significant DCT coefficients of the measured signals are used to reconstruct the image. As a result, the calculation can be saved. The theoretical computation complexity of the proposed algorithm is figured out and it is proved that the proposed method is efficient in calculation. The proposed algorithm is also verified through the numerical simulations and in vitro experiments. Compared with former developed model-based methods, the proposed algorithm is able to provide an equivalent reconstruction with the cost of much less time. From the theoretical analysis and the experiment results, it would be concluded that the model-based PAI reconstruction can be accelerated by using the proposed algorithm, so that the practical applicability of PAI may be enhanced.

Limited-view photoacoustic imaging based on an iterative adaptive weighted filtered backprojection approach

An iterative adaptive weighted filtered backprojection (FBP) approach was applied to our photoacoustic imaging (PAI) of the optical absorption in biological tissues from limited-view data. By using an image-based adaptive weighted PAI reconstruction, we can modify the defect of the artifacts degrading the quality of the image. Results of numerical simulations demonstrated that the proposed algorithm was superior to FBP in terms of both accuracy and robustness to noise. Reconstructed images of biological tissues agreed well with the structures of the samples. The resolution of the PAI system with the proposed method was experimentally demonstrated to be better than 0.14 mm. By using the proposed method, the imaging quality of the PAI system can be improved.

Full-Wave Iterative Image Reconstruction in Photoacoustic Tomography With Acoustically Inhomogeneous Media

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.

Comparison of photoacoustic image reconstruction algorithms using the channelized Hotelling observer

We demonstrate the use of task-based image-quality metrics to compare various photoacoustic image-reconstruction algorithms, including a method based on the pseudoinverse of the system matrix, simple backprojection, filtered backprojection, and a method based on the Fourier transform. We use a three-dimensional forward model with a linear transducer array to simulate a photoacoustic imaging system. The reconstructed images correspond with two-dimensional slices of the object and are 128×128 pixels. In order to compare the algorithms, we use channelized Hotelling observers that predict the detection ability of human observers. We use two sets of channels: constant Q and difference of Gaussian spatial frequency channels. We look at three tasks, identification of a point source in a uniform background, identification of a 0.5-mm cube in a uniform background, and identification of a point source in a lumpy background. For the lumpy background task, which is the most realistic of the tasks, the method based on the pseudoinverse performs best according to both sets of channels.

Development of Photoacoustic System for Breast Cancer Detection

Recently, the photoacoustic imaging system has been widely and intensively developed, and has been shown the possibility of diagnosis for early stage cancer. In this study, we developed a photoacoustic tomography imaging system with a commercial ultra sound device and a linear array probe. A tube phantom and a chicken breast phantom was made for the possibility of a system as a breast cancer detection. A moving average filter and a band pass filter with 3∼6 MHz bandwidth were developed for background noise elimination before delay-and-sum beamforming algorithm was used for image reconstruction. As a result, we showed that some signal processing procedure before beamforming was effective for the photoacoustic image reconstruction. 󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏󰠏

Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction

In photoacoustic imaging (PAI), reconstruction from sparse-view sampling data is a remaining challenge in the cases of fast or real-time imaging. In this paper, we present our study on a total variation based gradient descent (TV-GD) algorithm for sparse-view PAI reconstruction. This algorithm involves the total variation (TV) method in compressed sensing (CS) theory. The objective function of the algorithm is modified by adding the TV value of the reconstructed image. With this modification, the reconstructed image could be closer to the real optical energy distribution map. Additionally in the proposed algorithm, the photoacoustic data is processed and the image is updated individually at each detection point. In this way, the calculation with large matrix can be avoided and a more frequent image update can be obtained. Through the numerical simulations, the proposed algorithm is verified and compared with other reconstruction algorithms which have been widely used in PAI. The peak signal-to-noise ratio (PSNR) of the image reconstructed by this algorithm is higher than those by the other algorithms. Additionally, the convergence of the algorithm, the robustness to noise and the tunable parameter are further discussed. The TV-based algorithm is also implemented in the in vitro experiment. The better performance of the proposed method is revealed in the experiments results. From the results, it is seen that the TV-GD algorithm may be a practical and efficient algorithm for sparse-view PAI reconstruction.