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• W4313448575 abstract "Photoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires regularization to obtain meaningful solutions. The purpose of the work is to propose an elastic network (EN) model to improve the quality of reconstructed photoacoustic images. To evaluate the performance of the proposed method, a series of numerical simulations and tissue-mimicking phantom experiments are performed. The experiment results indicate that, compared with the <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> -norm and <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math> -normbased regularization methods with different numerical phantoms, Gaussian noise of 10-50 dB, and different regularization parameters, the EN method with <math xmlns=http://www.w3.org/1998/Math/MathML id=M3> <mi>α</mi> <mo>=</mo> <mn>0.5</mn> </math> has better image quality, calculation speed, and antinoise ability." @default.
• W4313448575 created "2023-01-06" @default.
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• W4313448575 date "2022-12-20" @default.
• W4313448575 modified "2023-10-14" @default.
• W4313448575 title "Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach" @default.
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• W4313448575 doi "https://doi.org/10.1155/2022/7877049" @default.
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