In practical applications within the human body, it is often challenging to fully encompass the target tissue or organ, necessitating the use of limited-view arrays, which can lead to the loss of crucial information. Addressing the reconstruction of photoacoustic sensor signals in limited-view detection spaces has become a focal point of current research. In this study, we introduce a self-supervised network termed HIgh-quality Self-supervised neural representation (HIS), which tackles the inverse problem of photoacoustic imaging to reconstruct high-quality photoacoustic images from sensor data acquired under limited viewpoints. We regard the desired reconstructed photoacoustic image as an implicit continuous function in 2D image space, viewing the pixels of the image as sparse discrete samples. The HIS's objective is to learn the continuous function from limited observations by utilizing a fully connected neural network combined with Fourier feature position encoding. By simply minimizing the error between the network's predicted sensor data and the actual sensor data, HIS is trained to represent the observed continuous model. The results indicate that the proposed HIS model offers superior image reconstruction quality compared to three commonly used methods for photoacoustic image reconstruction.
GPAIR: Gaussian-Kernel-Based Ultrafast 3D Photoacoustic Iterative Reconstruction
Department of Biomedical Engineering, College of Future Technology, Peking University（生物医学工程系，未来技术学院，北京大学）；Department of Electrical Engineering, Pohang University of Science and Technology（电气工程系，POSTECH）；Department of Convergence IT Engineering, Pohang University of Science and Technology（融合IT工程系，POSTECH）；Department of Mechanical Engineering, Pohang University of Science and Technology（机械工程系，POSTECH）；Department of Medical Science and Engineering, Pohang University of Science and Technology（医学科学与工程系，POSTECH）；Medical Device Innovation Center, Pohang University of Science and Technology（医疗设备创新中心，POSTECH）；National Biomedical Imaging Center, Peking University（国家生物医学成像中心，北京大学）
Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications
The Laboratory of Digital Image and Intelligent Computation（数字图像与智能计算实验室）；Shanghai Maritime University（上海 Maritime 大学）；School of Electronic Engineering and Intelligent Manufacturing（电子工程与智能制造学院）；Anqing Normal University（安庆师范学院）；School of Engineering（工程学院）；Great Bay University（Great Bay 大学）；Dongguan Great Bay Institute for Advanced Study（东莞 Great Bay 高级研究机构）；School of Basic Medical Sciences（基础医学学院）；Shenzhen University Medical School（深圳大学医学院）；Department of Chinese and Bilingual Studies（中文与双语研究部）；The Hong Kong Polytechnic University（香港理工大学）
Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse
Department of Mathematics, University of Innsbruck（数学系，因斯布鲁克大学）；Department of Computer Science, University of Innsbruck（计算机科学系，因斯布鲁克大学）；Department of Mathematics, Yeungnam University（数学系，延世大学）
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Photoacoustic (PA) imaging is a biomedical imaging modality capable of acquiring high-contrast images of optical absorption at depths much greater than traditional optical imaging techniques. However, practical instrumentation and geometry limit the number of available acoustic sensors surrounding the imaging target, which results in the sparsity of sensor data. Conventional PA image reconstruction methods give severe artifacts when they are applied directly to the sparse PA data. In this paper, we firstly propose to employ a novel signal processing method to make sparse PA raw data more suita…
University College London（伦敦大学学院）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究中心）
We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for the iterative reconstruction, but these artefacts are highly structured and we can train a CNN that can use the approximate information to perform an iterative reconstruction. We show feasibility of the method for human in-vivo measurements in a limited-view geometry. The proposed method is able to produce superior results to total variation r…
The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtere…
4D SlingBAG: spatial-temporal coupled Gaussian ball for large-scale dynamic 3D photoacoustic iterative reconstruction
College of Future Technology（未来技术学院）；Peking University（北京大学）；School of Intelligence Science and Technology（智能科学与技术学院）；Nanjing University（南京大学）；Pohang University of Science and Technology (POSTECH)（浦项科技大学（POSTECH））
University College London（伦敦大学学院）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究院）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/英国工程与自然科学研究委员会介入与外科科学中心）
Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame. As the latter is computationally more tractable, investigation to which extent this equivalence holds conducted in this paper is of immediate practical significance. To this end we formulate and compare DR,…
Super-temporal-resolution Photoacoustic Imaging with Dynamic Reconstruction through Implicit Neural Representation in Sparse-view
School of Information Science and Technology, ShanghaiTech University（信息科学与技术学院，上海科技大学）；School of Biomedical Engineering, ShanghaiTech University（生物医学工程学院，上海科技大学）；School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University（生物医学工程学院与医学机器人研究所，上海交通大学）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（生物医学工程学院，生命科学与医学系，中国科学技术大学）；School of Engineering Science, University of Science and Technology of China（工程科学学院，中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究所，中国科学技术大学）
George Mason University（乔治梅森大学）；The MITRE Corporation（MITRE公司）
In photoacoustic tomography (PAT), the acoustic pressure waves produced by optical excitation are measured by an array of detectors and used to reconstruct an image. Sparse spatial sampling and limited-view detection are two common challenges faced in PAT. Reconstructing from incomplete data using standard methods results in severe streaking artifacts and blurring. We propose a modified convolutional neural network (CNN) architecture termed Dense Dilation UNet (DD-UNet) for correcting artifacts in 3D PAT. The DD-Net leverages the benefits of dense connectivity and dilated convolutions to impro…
University of Chinese Academy of Sciences（中国科学院大学）；ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）
Photoacoustic (PA) computed tomography (PACT) reconstructs the initial pressure distribution from raw PA signals. The standard reconstruction of medical 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. Most works remove the artifacts from image domain, and compensate the limited-view from dataset. 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 limit…
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Photoacoustic imaging (PAI) is an emerging non-invasive imaging modality combining the advantages of deep ultrasound penetration and high optical contrast. Image reconstruction is an essential topic in PAI, which is unfortunately an ill-posed problem due to the complex and unknown optical/acoustic parameters in tissue. Conventional algorithms used in PAI (e.g., delay-and-sum) provide a fast solution while many artifacts remain, especially for linear array probe with limited-view issue. Convolutional neural network (CNN) has shown state-of-the-art results in computer vision, and more and more w…
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University（海德堡大学）；Heidelberg University Hospital（海德堡大学医院）
Purpose: Photoacoustic tomography (PAT) is a novel imaging technique that can spatially resolve both morphological and functional tissue properties, such as the vessel topology and tissue oxygenation. While this capacity makes PAT a promising modality for the diagnosis, treatment and follow-up of various diseases, a current drawback is the limited field-of-view (FoV) provided by the conventionally applied 2D probes. Methods: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integ…
Digital twins enable full-reference quality assessment of photoacoustic image reconstructions
University of Cambridge（剑桥大学）；Cancer Research UK Cambridge Institute（英国癌症研究会剑桥研究所）；University of Arizona（亚利桑那大学）；University of Eastern Finland（东部芬兰大学）；Imperial College London（伦敦帝国理工学院）；University College London（伦敦大学学院）；University of Oulu（奥卢大学）
ShanghaiTech University（上海科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Photoacoustic imaging (PAI) is a non-invasive imaging modality that detects the ultrasound signal generated from tissue with light excitation. Photoacoustic computed tomography (PACT) uses unfocused large-area light to illuminate the target with ultrasound transducer array for PA signal detection. Limited-view issue could cause a low-quality image in PACT due to the limitation of geometric condition. The model-based method is used to resolve this problem, which contains different regularization. To adapt fast and high-quality reconstruction of limited-view PA data, in this paper, a model-based…
George Mason University（乔治梅森大学）；The MITRE Corporation（MITRE公司）
Photoacoustic tomography (PAT) is a nonionizing imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their view of the imaging target, which result in significant image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pi…
In photoacoustic tomography (PAT) with flat sensor, we routinely encounter two types of limited data. The first is due to using a finite sensor and is especially perceptible if the region of interest is large relative to the sensor or located farther away from the sensor. In this paper, we focus on the second type caused by a varying sensitivity of the sensor to the incoming wavefront direction which can be modelled as binary i.e. by a cone of sensitivity. Such visibility conditions result, in the Fourier domain, in a restriction of both the image and the data to a bow-tie, akin to the one cor…
University of Oulu（奥卢大学）；University College London（伦敦大学学院）
Biomedical photoacoustic tomography, which can provide high resolution 3D soft tissue images based on the optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is c…
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University（海德堡大学）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Cancer Research UK Cambridge Institute（英国癌症研究院剑桥研究所）；University of Cambridge（剑桥大学）
Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic {and ultrasound} imaging data are used as refe…
University College London（伦敦大学学院）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学中心）；KTH - Royal Institute of Technology（瑞典皇家理工学院）；Elekta（医科达公司）
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient inform…
ShanghaiTech University（上海科技大学）；University of Chinese Academy of Sciences（中国科学院大学）
Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data …
Benchmarking Deep Learning-Based Reconstruction Methods for Photoacoustic Computed Tomography with Clinically Relevant Synthetic Datasets
Department of Bioengineering, University of Illinois Urbana-Champaign（生物工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign（电气与计算机工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Biomedical Engineering, The University of Texas at Austin（生物医学工程系，德克萨斯大学奥斯汀分校）；Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（奥登计算工程与科学研究所，德克萨斯大学奥斯汀分校）
Coordinate-based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography
Northwestern University（西北大学）；California Institute of Technology（加州理工学院）；Tsinghua University（清华大学）；NSF-Simons AI Institute for the Sky (SkAI)（NSF-模拟斯通AI天空研究所（SkAI））
Duke University（杜克大学）；Tsinghua University（清华大学）；Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；University of Southern California（南加州大学）
Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser's repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (i.e., undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to improve sparsely sampled PAM images; however, these methods often require time-consuming pre-training and large training dataset with ground truth. Here, we propose the use of deep image prior (DIP) to improve the image quality of undersampled PA…
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Cancer Research UK Cambridge Institute, University of Cambridge（剑桥大学英国癌症研究中心剑桥研究所）
Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Pro…
