Limited-View Photoacoustic Imaging Reconstruction Via High-quality Self-supervised Neural Representation

• URL: http://arxiv.org/abs/2407.03663v1
• Date: Thu, 4 Jul 2024 06:07:54 GMT
• Title: Limited-View Photoacoustic Imaging Reconstruction Via High-quality Self-supervised Neural Representation
• Authors: Youshen xiao, Yuting Shen, Bowei Yao, Xiran Cai, Yuyao Zhang, Fei Gao,
• Abstract summary: We introduce a self-supervised network termed HIgh-quality Self-supervised neural representation (HIS) HIS tackles the inverse problem of photoacoustic imaging to reconstruct high-quality photoacoustic images from sensor data acquired under limited viewpoints. Results indicate that the proposed HIS model offers superior image reconstruction quality compared to three commonly used methods for photoacoustic image reconstruction.
• Score: 4.274771298029378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• Making the Invisible Visible: Toward High-Quality Terahertz Tomographic Imaging via Physics-Guided Restoration [24.045067900801072]
  Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. We propose a novel multi-view Subspace-guided Restoration Network (SARNet) that fuses multi-viewAttention and multi-spectral features of THz images for effective image restoration and 3D tomographic reconstruction.
  arXiv  Detail & Related papers  (2023-04-28T15:05:46Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• DH-GAN: A Physics-driven Untrained Generative Adversarial Network for 3D Microscopic Imaging using Digital Holography [3.4635026053111484]
  Digital holography is a 3D imaging technique by emitting a laser beam with a plane wavefront to an object and measuring the intensity of the diffracted waveform, called holograms. Recently, deep learning (DL) methods have been used for more accurate holographic processing. We propose a new DL architecture based on generative adversarial networks that uses a discriminative network for realizing a semantic measure for reconstruction quality.
  arXiv  Detail & Related papers  (2022-05-25T17:13:45Z)
• Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images [0.9208007322096533]
  The aim of this study is to train a single neural network to reconstruct high-quality CT images from noisy or incomplete data. The network includes a self-attention block to model long-range dependencies in the data. Our approach is shown to have comparable overall performance to CIRCLE GAN, while outperforming the other two approaches.
  arXiv  Detail & Related papers  (2021-12-23T19:20:38Z)
• Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network [0.0]
  Deep multiscale convolutional encoder-decoder neural network is proposed. The proposed model reaches on average 38.38 of PSNR and 0.98 of SSIM on a test set of 57458 images.
  arXiv  Detail & Related papers  (2021-05-01T14:54:57Z)
• AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data [1.7237160821929758]
  Photoacoustic imaging is capable of acquiring high contrast images of optical absorption at depths much greater than traditional optical imaging techniques. In this paper, we employ a novel signal processing method to make sparse PA raw data more suitable for the neural network. We then propose Attention Steered Network (AS-Net) for PA reconstruction with multi-feature fusion.
  arXiv  Detail & Related papers  (2021-01-22T03:49:30Z)
• Focal Frequency Loss for Image Reconstruction and Synthesis [125.7135706352493]
  We show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize.
  arXiv  Detail & Related papers  (2020-12-23T17:32:04Z)
• NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal [96.12120000492962]
  The quality of retinal images is often clinically unsatisfactory due to eye lesions and imperfect imaging process. One of the most challenging quality degradation issues in retinal images is non-uniform illumination. We propose a non-uniform illumination removal network for retinal image, called NuI-Go.
  arXiv  Detail & Related papers  (2020-08-07T04:31:33Z)
• Identity Enhanced Residual Image Denoising [61.75610647978973]
  We learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising. The proposed network produces remarkably higher numerical accuracy and better visual image quality than the classical state-of-the-art and CNN algorithms.
  arXiv  Detail & Related papers  (2020-04-26T04:52:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.