Total Variation Algorithms for PAT Image Reconstruction

John, Mary Josy; Barhumi, Imad

doi:10.23919/apsipaasc55919.2022.9980048

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…In response to the problem of traditional regularization methods being either too smooth or too sparse, Liu et al proposed the elastic net regularization method [128], which can improve the anti-noise ability. In specific cases, such as encountering limitations in the field of view, the split Bregman total variation algorithm can be used based on the distribution positions of all sensors [104]. This algorithm demonstrates that among all sensor arrangements, the convex sensor array performs the best, and the computation time required is also reduced.…”

Section: Physical Transmission Model-based Compressed Sensing Methodsmentioning

confidence: 98%

“…CS can utilize the sparsity or structural information of signals to partially sample and effectively reconstruct signals, thereby compensating for the information loss caused by the limited viewing angle and improving imaging quality. CS can address the decrease in image quality in PAI systems due to the limited viewing angle [104,105].…”

Section: Photoacoustic Imaging Based On Compressed Sensingmentioning

confidence: 99%

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Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Wang,

Chen,

Zhao

et al. 2024

Sensors

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Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

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Section: Physical Transmission Model-based Compressed Sensing Methodsmentioning

confidence: 98%

Section: Photoacoustic Imaging Based On Compressed Sensingmentioning

confidence: 99%

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Wang,

Chen,

Zhao

et al. 2024

Sensors

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“…• Total variation (TV) regularized LS: The ability of TV regularization to maintain image edges makes it a preferred choice for image restoration and reconstruction [43], [44]. It is formulated as…”

Section: A Compressive Sensing Algorithmsmentioning

confidence: 99%

Advancing Sensor-Data Based PAT Image Reconstruction Through Efficient and Intelligible Unrolled Networks

John,

Barhumi

2023

IEEE Access

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Despite recent advancements, the challenge of deep-dense tissue imaging with high resolution and contrast persists in breast cancer diagnosis; however, photoacoustic tomography (PAT) imaging addresses this issue by capturing both anatomical and functional information, including small masses as tiny as 3mm. Compressive sensing coined with PAT allows for a faster reconstruction, because it requires fewer sensors and samples. Although CS-PAT algorithms are efficient they rely on application specific parameter tuning and system matrix modelling. This paper introduces a time-efficient approach of algorithm unrolling, based on CS algorithms, to directly reconstruct high-resolution PAT images from sensor data, eliminating the need for explicit parameter assignment and system matrix modeling. The study proposes two unrolled deep learning networks based on split-Bregman total-variation and relaxed-basis-pursuit with ADMM implementation, followed by a denoising network for further enhancement. The model-free unrolled deep-learning approach successfully reconstructs high-resolution PAT images, even in the presence of noise, with low validation root mean square error. An enhancer network based on U-Net improves image quality to 0.91 and significantly reduces mean square error by 95%. Overall, the proposed algorithm unrolling method demonstrates promising potential for practical clinical applications, particularly in early disease detection, offering rapid image reconstruction without explicit system matrix modeling or parameter tuning. The inclusion of a U-Net denoising network enhances the approach's resilience and adaptability, suggesting possibilities for improved disease diagnosis and treatment outcomes, especially for early detection in dense tissues like breasts.

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Compressive Sensing Based Algorithms for Limited-View PAT Image Reconstruction

John,

Barhumi

2023

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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Total Variation Algorithms for PAT Image Reconstruction

Cited by 5 publications

References 27 publications

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Advancing Sensor-Data Based PAT Image Reconstruction Through Efficient and Intelligible Unrolled Networks

Compressive Sensing Based Algorithms for Limited-View PAT Image Reconstruction

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