Photoacoustic Image Reconstruction Research Articles

Sparse-View Photoacoustic Reconstruction Method for Diabetic Retinopathy Using Feature Fusion Network.

Diabetic retinopathy is one of the most prevalent microvascular complications of diabetes mellitus, and photoacoustic imaging is an effective method for imaging diabetic retinal vessels. Photoacoustic imaging is an emerging noninvasive imaging method based on the photoacoustic effect, which offers advantages of contrast, resolution, and depth imaging. Appropriate photoacoustic reconstruction methods are essential for obtaining high-quality photoacoustic images. In this study, a multi-input self-attention multiscale feature fusion network (SAMF-Net) is proposed for photoacoustic reconstruction. The algorithm accepts two inputs, namely the original photoacoustic signal and the traditional reconstructed image. Furthermore, a global feature extraction module based on the self-attention mechanism is employed to focus on the global information. The results demonstrate that the proposed method exhibits superior reconstruction capability under different sparse detection views. The method has instructive value for photoacoustic image reconstruction and has the potential for further application in the diagnosis of diabetic retinopathy.

Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning

Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially using limited-bandwidth ultrasonic linear detector arrays. Here, we propose a hybrid Bayesian convolutional neural network (Hybrid-BCNN) to jointly predict PA image and segmentation with error (uncertainty) predictions. Each output pixel represents a probability distribution where error can be quantified. The Hybrid-BCNN was trained with simulated PA data and applied to both simulations and experiments. Due to the sparsity of PA images, segmentation focuses Hybrid-BCNN on minimizing the loss function in regions with PA signals for better predictions. The results show that accurate PA segmentations and images are obtained, and error predictions are highly statistically correlated to actual errors. To leverage error predictions, confidence processing created PA images above a specific confidence level.

Time Series Forecasting for Sparse Ring-shaped Array Photoacoustic Imaging Reconstruction

Abstract Photoacoustic computed tomography (PACT), which provides high optical absorption contrast and deep acoustic penetration, plays an important role in non-invasive biomedical imaging area. As the decrease of array elements, the reconstructed image suffers from severe artifacts. Recent studies utilize deep learning methods to improve the imaging quality of PACT based on image network design, but few were reported with raw data. To address this issue, this paper proposes a Wave to Wave Convolution Gate Recurrent-Net (WWCG-Net) to reconstruct photoacoustic image based on time series acoustic signal prediction. Simulation and experiment results show the superiority of our method compared with linear interpolation (LI) and eXtreme Gradient Boosting (XGBoost) in term of suppress artifact and improve resolution.

Application of $L_{1}-L_{2}$ Regularization in Sparse-View Photoacoustic Imaging Reconstruction

Application of $L_{1}-L_{2}$ Regularization in Sparse-View Photoacoustic Imaging Reconstruction

Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue in vivo. Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, in vivo animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.

Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality. Various methods have been proposed to address this issue, but they usually involve complex hardware and/or time-consuming algorithms, hindering clinical translation. In this work, we introduce a deep learning framework for SoS estimation and subsequent aberration correction in a dual-modal PA/US imaging system exploiting a clinical US probe. As the acquired PA and US images were inherently co-registered, the estimated SoS distribution from US channel data using a deep neural network was incorporated for accurate PA image reconstruction. The framework comprised an initial pre-training stage based on digital phantoms, which was further enhanced through transfer learning using physical phantom data and associated SoS maps obtained from measurements. This framework achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation on digital and physical phantoms, respectively and structural similarity index measures of up to 0.88 for PA reconstructions compared to the conventional approach of 0.69. A maximum of 1.2 times improvement in the signal-to-noise ratio of PA images was further demonstrated with a human volunteer study. Our results show that the proposed framework could be valuable in various clinical and preclinical applications to enhance PA image reconstruction.

Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography

Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT reconstruction strategy that combines model-based iteration with score-based generative model was proposed. By incrementally adding noise to the training samples, prior knowledge can be learned from the complex probability distribution. The acquired prior is then utilized as constraint in model-based iteration. The information of missing views can be gradually compensated by cyclic iteration to achieve high-quality reconstruction. The performance of the proposed method was evaluated with the circular phantom and in vivo experimental data. Experimental results demonstrate the outstanding effectiveness of the proposed method in limited-view cases. Notably, the proposed method exhibits excellent performance in limited-view case of 70° compared with traditional method. It achieves a remarkable improvement of 203% in PSNR and 48% in SSIM for the circular phantom experimental data, and an enhancement of 81% in PSNR and 65% in SSIM for in vivo experimental data, respectively. The proposed method has capability of reconstructing PAT images in extremely limited-view cases, which will further expand the application in clinical scenarios.

Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view

Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains.

PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data.

We introduce a novel deep-learning-based photoacoustic tomography method called Photoacoustic Tomography Neural Radiance Field (PA-NeRF) for reconstructing 3D volumetric PAT images from limited 2D Bscan data. In conventional 3D volumetric imaging, a 3D reconstruction requires transducer element data obtained from all directions. Our model employs a NeRF-based PAT 3D reconstruction method, which learns the relationship between transducer element positions and the corresponding 3D imaging. Compared with convolution-based deep-learning models, such as Unet and TransUnet, PA-NeRF does not learn the interpolation process but rather gains insight from 3D photoacoustic imaging principles. Additionally, we introduce a forward loss that improves the reconstruction quality. Both simulation and phantom studies validate the performance of PA-NeRF. Further, we apply the PA-NeRF model to clinical examples to demonstrate its feasibility. To the best of our knowledge, PA-NeRF is the first method in photoacoustic tomography to successfully reconstruct a 3D volume from sparse Bscan data.

A photoacoustic tomography image reconstruction method based on forward imaging model

A photoacoustic tomography image reconstruction method based on forward imaging model

Photoacoustic imaging plus X: a review.

Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability. The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications. An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively. The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

Photoacoustic image reconstruction with anobjective function using TGV and ESTGV asaregularization functional.

Photoacoustic tomographic imaging is a non-invasive medical diagnostic technology for visualizing biological tissue. However, the inverse problem and noise in photoacoustic signals often cause blurred images. Existing regularization methods struggle with staircasing artifacts and edge preservation. To overcome this, an objective function incorporating total generalized variation (TGV) is proposed. However, it failed with high-density Gaussian noise. To address this, an extended version called edge-guided second-order TGV (ESTGV) is introduced. For sparsification, wavelet transform and discrete cosine transform are introduced, while the fast-composite-splitting algorithm is employed for the inverse problem solution. Experimental validation demonstrates the potential of these approaches.

Triple-path feature transform network for ring-array photoacoustic tomography image reconstruction

Photoacoustic imaging (PAI) is a noninvasive emerging imaging method based on the photoacoustic effect, which provides necessary assistance for medical diagnosis. It has the characteristics of large imaging depth and high contrast. However, limited by the equipment cost and reconstruction time requirements, the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed. In this paper, a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data. Specifically, the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data, and takes the photoacoustic physical model as a prior information to guide the reconstruction process. In addition, to enhance the ability of extracting signal features, the residual block and squeeze and excitation block are introduced into the TFT-Net. For further efficient reconstruction, the final output of photoacoustic signals uses ‘filter-then-upsample’ operation with a pixel-shuffle multiplexer and a max out module. Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly, reduce background noise, and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

Combined ultrasound and photoacoustic C-mode imaging system for skin lesion assessment

Accurate assessment of the size and depth of infiltration is critical for effectively treating and removing skin cancer, especially melanoma. However, existing methods such as skin biopsy and histologic examination are invasive, time-consuming, and may not provide accurate depth results. We present a novel system for simultaneous and co-localized ultrasound and photoacoustic imaging, with the application for non-invasive skin lesion size and depth measurement. The developed system integrates an acoustical mirror that is placed on an ultrasound transducer, which can be translated within a flexible water tank. This allows for 3D (C-mode) imaging, which is useful for mapping the skin structure and determine the invasion size and depth of lesions including skin cancer. For efficient reconstruction of photoacoustic images, we applied the open-source MUST library. The acquisition time per 2D image is <1 s and the pulse energies are below the legal Maximum Permissible Exposure (MPE) on human skin. We present the depth and resolution capabilities of the setup on several self-designed agar phantoms and demonstrate in vivo imaging on human skin. The setup also features an unobstructed optical window from the top, allowing for simple integration with other optical modalities. The perspective towards clinical application is demonstrated.

S-Wave Accelerates Optimization-based Photoacoustic Image Reconstruction in vivo

Photoacoustic imaging has undergone rapid development in recent years. To simulate photoacoustic imaging on a computer, the most popular MATLAB toolbox currently used for the forward projection process is k-Wave. However, k-Wave suffers from significant computation time. Here we propose a straightforward simulation approach based on superposed Wave (s-Wave) to accelerate photoacoustic simulation. In this study, we consider the initial pressure distribution as a collection of individual pixels. By obtaining standard sensor data from a single pixel beforehand, we can easily manipulate the phase and amplitude of the sensor data for specific pixels using loop and multiplication operators. The effectiveness of this approach is validated through an optimization-based reconstruction algorithm. The results reveal significantly reduced computation time compared with k-Wave. Particularly in a sparse 3-D configuration, s-Wave exhibits a speed improvement >2000 times compared with k-Wave. In terms of optimization-based image reconstruction, in vivo imaging results reveal that using the s-Wave method yields images highly similar to those obtained using k-Wave, while reducing the reconstruction time by approximately 50 times. Proposed here is an accelerated optimization-based algorithm for photoacoustic image reconstruction, using the fast s-Wave forward projection simulation. Our method achieves substantial time savings, particularly in sparse system configurations. Future work will focus on further optimizing the algorithm and expanding its applicability to a broader range of photoacoustic imaging scenarios.

LED-based Schlieren system for full-field photoacoustic wave acquisition and image reconstruction

In this work, full-field detection of laser-induced ultrasound waves was performed with an off-axis LED-based Schlieren system. Sensing strobe light, pulsed laser dual light-sheet excitation, and CMOS sensor device were all synchronized to capture the pressure wave as it propagated through an elastic liquid surrounding the test sample. In addition, a reconstruction algorithm based on the Radon transform was applied to the digitally recorded field in order to obtain an image of the photoacoustic source. The proposed system is capable of retrieving the profile of cylindrical and hexagonal targets.

Implementation and Comparison of Three Image Reconstruction Algorithms in FPGA Toward Palm-Size Photoacoustic Tomography

Photoacoustic (PA) imaging has been widely applied in both preclinical and clinical applications. With a significantly increasing number of data acquisition channels, fast high-quality image reconstruction with low-cost for PA imaging is an open challenge. In this paper, we propose a palm-size PAT system, named palmPAT, to achieve miniaturized low-cost PA imaging modality with high performance. To prove the feasibility, Field Programmable Gate Array (FPGA) implementation of PA image reconstruction is performed to achieve high-quality imaging performance with miniaturized size and cost. This FPGA implementation can be easily adapted to widely used algorithms, such as delay-and-sum (DAS) and its variants (e.g. DMAS, DAS-CF). We have performed in-vivo human finger experiments to demonstrate the feasibility and potential. To our best knowledge, this is the first study concentrated on the implementation and comparison of three frequently-used PA image reconstruction algorithms based on FPGA platform.

An Investigation of Signal Preprocessing for Photoacoustic Tomography.

Photoacoustic tomography (PAT) is increasingly being used for high-resolution biological imaging at depth. Signal-to-noise ratios and resolution are the main factors that determine image quality. Various reconstruction algorithms have been proposed and applied to reduce noise and enhance resolution, but the efficacy of signal preprocessing methods which also affect image quality, are seldom discussed. We, therefore, compared common preprocessing techniques, namely bandpass filters, wavelet denoising, empirical mode decomposition, and singular value decomposition. Each was compared with and without accounting for sensor directivity. The denoising performance was evaluated with the contrast-to-noise ratio (CNR), and the resolution was calculated as the full width at half maximum (FWHM) in both the lateral and axial directions. In the phantom experiment, counting in directivity was found to significantly reduce noise, outperforming other methods. Irrespective of directivity, the best performing methods for denoising were bandpass, unfiltered, SVD, wavelet, and EMD, in that order. Only bandpass filtering consistently yielded improvements. Significant improvements in the lateral resolution were observed using directivity in two out of three acquisitions. This study investigated the advantages and disadvantages of different preprocessing methods and may help to determine better practices in PAT reconstruction.

A jointed feature fusion framework for photoacoustic image reconstruction

The standard reconstruction of Photoacoustic (PA) computed tomography (PACT) image could cause the artifacts due to interferences or ill-posed setup. Recently, deep learning has been used to reconstruct the PA image with ill-posed conditions. In this paper, we propose a jointed feature fusion framework (JEFF-Net) based on deep learning to reconstruct the PA image using limited-view data. The cross-domain features from limited-view position-wise data and the reconstructed image are fused by a backtracked supervision. A quarter position-wise data (32 channels) is fed into model, which outputs another 3-quarters-view data (96 channels). Moreover, two novel losses are designed to restrain the artifacts by sufficiently manipulating superposed data. The experimental results have demonstrated the superior performance and quantitative evaluations show that our proposed method outperformed the ground-truth in some metrics by 135% (SSIM for simulation) and 40% (gCNR for in-vivo) improvement.

Toward Ultrasensitive, Broadband, Reflection‐Mode In Vivo Photoacoustic Microscopy Using a Bare Glass

AbstractAccurate volumetric photoacoustic image reconstruction requires ultrasonic detection with sufficient sensitivity to pressure transients over a broad spectral bandwidth. By using the ultrafast temporal dynamics and high‐sensitivity responses to refractive index changes of optical surface waves, a structureless ultrasonic sensor is proposed for photoacoustic impulse measurement that is manufactured simply by covering a ubiquitous bare glass with several drops of water. Ultrasensitive photoacoustic detection with a broadband frequency response is achieved by converting the photoacoustically modulated phase shift in the optical reflection into a time‐varying polarization. This sensor achieves noise‐equivalent pressure sensitivity at ≈93 Pa while maintaining an acoustic bandwidth of ≈174 MHz. Incorporation of the transparent sensor into an optical‐resolution photoacoustic microscope enables reflection‐mode 3D imaging with isometric spatial resolution on the micrometer scale. Label‐free volumetric visualizations of adult zebrafish are obtained in vivo. The favorable performance of the proposed sensor offers considerable potential for advancement of biomedical photoacoustic investigations.