Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Hsu, Ko-Tsung; Guan, Steven; Chitnis, Parag V.

doi:10.1016/j.pacs.2023.100452

Cited by 7 publications

(7 citation statements)

References 53 publications

(59 reference statements)

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“…Various ways to mitigate these disadvantages, such as combining aspects of the above methods, are under investigation. 130 – 137 …”

Section: Photoacoustic Inverse Problemsmentioning

confidence: 99%

“…Various ways to mitigate these disadvantages, such as combining aspects of the above methods, are under investigation. [130][131][132][133][134][135][136][137] The short treatment of the acoustic inversion here should not be taken to suggest that it is unimportant with regard to the optical inversion. Accurate solutions to the acoustic inversion are critical to obtaining accurate solutions to the optical inversion, as they provide the data for it.…”

Section: Acoustic Inverse Problemmentioning

confidence: 99%

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Quantitative photoacoustic tomography: modeling and inverse problems

Tarvainen,

Cox

2023

J. Biomed. Opt.

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. Significance Quantitative photoacoustic tomography (QPAT) exploits the photoacoustic effect with the aim of estimating images of clinically relevant quantities related to the tissue’s optical absorption. The technique has two aspects: an acoustic part, where the initial acoustic pressure distribution is estimated from measured photoacoustic time-series, and an optical part, where the distributions of the optical parameters are estimated from the initial pressure. Aim Our study is focused on the optical part. In particular, computational modeling of light propagation (forward problem) and numerical solution methodologies of the image reconstruction (inverse problem) are discussed. Approach The commonly used mathematical models of how light and sound propagate in biological tissue are reviewed. A short overview of how the acoustic inverse problem is usually treated is given. The optical inverse problem and methods for its solution are reviewed. In addition, some limitations of real-life measurements and their effect on the inverse problems are discussed. Results An overview of QPAT with a focus on the optical part was given. Computational modeling and inverse problems of QPAT were addressed, and some key challenges were discussed. Furthermore, the developments for tackling these problems were reviewed. Although modeling of light transport is well-understood and there is a well-developed framework of inverse mathematics for approaching the inverse problem of QPAT, there are still challenges in taking these methodologies to practice. Conclusions Modeling and inverse problems of QPAT together were discussed. The scope was limited to the optical part, and the acoustic aspects were discussed only to the extent that they relate to the optical aspect.

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“…Various ways to mitigate these disadvantages, such as combining aspects of the above methods, are under investigation. 130 – 137 …”

Section: Photoacoustic Inverse Problemsmentioning

confidence: 99%

Section: Acoustic Inverse Problemmentioning

confidence: 99%

Quantitative photoacoustic tomography: modeling and inverse problems

Tarvainen,

Cox

2023

J. Biomed. Opt.

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“…10 Diffusion models were also used to improve quality of extremely sparse data outperforming the state-of-the-art methods by 260% in structural similarity and 30% in peak signalto-noise ratio. 11 Also, a diffusion model excursively train on natural images was proposed for speeding up the photoacoustic microscopy imaging. 12 In this preliminary work, we propose the use of conditional denoising diffusion probabilistic models (cDDPM) for improving limited-view PAT imaging.…”

Section: Introductionmentioning

confidence: 99%

Using denoising diffusion probabilistic models to enhance quality of limited-view photoacoustic tomography

De Santi,

Awasthi,

Manohar

2024

Photons Plus Ultrasound: Imaging and Sensing 2024

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In photoacoustic tomography (PAT), a limited angle of detector coverage around the object affects PAT image quality. Consequently, PAT images can become challenging to interpret accurately. Although deep learning methods, such as convolutional neural networks (CNNs), have shown promising results in recovering high-quality images from limited-view data, these methods suffer from loss of fine image details. Recently, denoising diffusion probabilistic models (DDPM) are gaining interest in image generation applications. Here we explore the potential of conditional denoising diffusion probabilistic models (cDDPM) to enhance quality of limited-view PAT images. The OADAT dataset consisting of 2D PAT images of healthy forearms acquired with a semicircle array of 256 ultrasound elements is used. PAT images are reconstructed using the full array (256 elements) and also the central 128, 64 and 32 elements for limited-view. The approach showed to be able to filter out limited-view streak artifacts, accurately recover shapes of vascular structures, and preserve fine-detailed texture. Conditional DDPMs show potential in improving quality of limited-view PAT reconstructions, however, they require higher computational cost compared to conventional CNNs. Future works will include the reduction of computational time and further evaluations on different datasets and array geometries.

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“…Photoacoustic tomography (PAT) imaging is an emerging non-invasive hybrid imaging technique that combines the advantages of optical and ultrasound imaging [1][2][3]. It can obtain high-resolution optical contrast images in biological tissues at depths of several centimeters [4]. In PAT, tissue absorbs light energy, undergoes adiabatic expansion, and produces initial pressure waves.…”

Section: Introductionmentioning

confidence: 99%

Artifact removal with physical model and deep learning for limited-data photoacoustic tomography

Zhong

Wang

Zhang

et al. 2023

Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023)

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Artifact in photoacoustic tomography is always an issue to be solved. Here, a deep learning based physical model method to remove artifact for limited-data photoacoustic tomography was proposed, termed as PD net. A virtual photoacoustic tomography platform was constructed based on k-Wave, and the dataset required for deep learning was obtained using this virtual platform. The U-Net was used to build a deep learning network to remove artifacts in sparse-view and limited-view photoacoustic tomography. Under sparsity condition, when the number of ultrasonic transducers is 64, the improvement rates of SSIM and PSNR of the network are 274% and 66.34%, respectively, compared with the input of the network, which verifies that this method can remove artifacts in sparse-view photoacoustic tomography. The proposed method can reduce artifacts and enhance anatomical contrast when the number of ultrasonic transducers used is limited, and effectively reduce manufacturing costs of photoacoustic tomography.

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Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Cited by 7 publications

References 53 publications

Quantitative photoacoustic tomography: modeling and inverse problems

Quantitative photoacoustic tomography: modeling and inverse problems

Using denoising diffusion probabilistic models to enhance quality of limited-view photoacoustic tomography

Artifact removal with physical model and deep learning for limited-data photoacoustic tomography

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