Abstract
Photoacoustic tomography (PAT) is a propitious imaging modality, which is helpful for biomedical study. However, fast PAT imaging and denoising is an exigent task in medical research. To address the problem, recently, methods based on compressed sensing (CS) have been proposed, which accede the low computational cost and high resolution for implementing PAT. Nevertheless, the imaging results of the sparsity-based methods strictly rely on sparsity and incoherence conditions. Furthermore, it is onerous to ensure that the experimentally acquired photoacoustic data meets CS’s prerequisite conditions. In this work, a deep learning–based PAT (Deep-PAT)method is instigated to overcome these limitations. By using a neural network, Deep-PAT is not only able to reconstruct PAT from a fewer number of measurements without considering the prerequisite conditions of CS, but also can eliminate undersampled artifacts effectively. The experimental results demonstrate that Deep-PAT is proficient at recovering high-quality photoacoustic images using just 5% of the original measurement data. Besides this, compared with the sparsity-based method, it can be seen through statistical analysis that the quality is significantly improved by 30% (approximately), having average SSIM = 0.974 and PSNR = 29.88 dB with standard deviation ±0.007 and ±0.089, respectively, by the proposed Deep-PAT method. Also, a comparsion of multiple neural networks provides insights into choosing the best one for further study and practical implementation.
Highlights
Photoacoustic tomography (PAT) is a coupled-physics imaging modality that allows noninvasive, quantitative, and 3-D imaging of biological and biochemical processes in living small animals
The above-formulated method is applied to three different neural networks
The output is applied to a simple convolutional neural networks (CNN) model having three fully connected convolutional layers
Summary
Photoacoustic tomography (PAT) is a coupled-physics imaging modality that allows noninvasive, quantitative, and 3-D imaging of biological and biochemical processes in living small animals. PAT for small animal imaging by the highly computational iterative CS methods. It must be noted that, to obtain the optimal imaging results, the sparsitybased methods are strictly relying on sparsity and incoherence conditions (Provost and Lesage, 2009). In other words, when encountering complex experimental conditions, the acquired photoacoustic data may not be precisely sparse in a fixed basis (transform). It is a challenging task to find the exact basis to make the photoacoustic data sparse. To some extent, it limits the application of the sparsity-based method for in vivo experiments
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
