Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence

Mao, Qiuqin; Zhao, Weiwei; Qian, Xiaoqin; Tao, Chao; Liu, Xiaojun

doi:10.1016/j.pacs.2022.100427

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…We note that Equation (13) demonstrates that matrix Q is the result of a convolution of the kernel L along the row dimension of matrix A, since multiplication in the frequency domain represents a convolution in the time domain. The above notation allows us to write Equation (12) as an (infinite) eigenvalue problem given by…”

Section: Fourier Series Expansion Methods For Optimal Waveformmentioning

confidence: 99%

“…Photoacoustic imaging systems for biomedical applications combine the advantages of both optical and acoustical imaging methods and have thus been attracting researchers' attention in recent decades [1][2][3][4][5]. Many attempts have been made to improve the Signalto-Noise Ratio (SNR) of a photoacoustic imaging system [6][7][8][9][10][11][12][13][14]. However, in the context of the selection of the transmitted waveform, most recent studies are still based on either chirps or square pulses, and occasionally Gaussian pulses [15,16] or pulse trains similar to pulsed sinusoids with a high-carrier frequency [17].…”

Section: Introductionmentioning

confidence: 99%

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Waveform Selection Based on Discrete Prolate Spheroidal Sequences for Near-Optimal SNRs for Photoacoustic Applications

Sun,

Baddour

2023

Photonics

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Waveform engineering is an important topic in imaging and detection systems. Waveform design for the optimal Signal-to-Noise Ratio (SNR) under energy and duration constraints can be modelled as an eigenproblem of a Fredholm integral equation of the second kind. SNR gains can be achieved using this approach. However, calculating the waveform for optimal SNR requires precise knowledge of the functional form of the absorber, as well as solving a Fredholm integral eigenproblem which can be difficult. In this paper, we address both those difficulties by proposing a Fourier series expansion method to convert the integral eigenproblem to a small matrix eigenproblem which is both easy to compute and gives a heuristic view of the effects of different absorber kernels on the eigenproblem. Another important result of this paper is to provide an alternate waveform, the Discrete Prolate Spheroidal Sequences (DPSS), as the input waveform to obtain near optimal SNR that does not require the exact form of the absorber to be known apriori.

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Section: Fourier Series Expansion Methods For Optimal Waveformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Waveform Selection Based on Discrete Prolate Spheroidal Sequences for Near-Optimal SNRs for Photoacoustic Applications

Sun,

Baddour

2023

Photonics

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“…Although all the existing methods are fast (less than one second) to compute, for some applications the increase in computation efficiency still matters. For example, in photoacoustic imaging where PSWFs can be good candidates for input laser waveforms [52], a typical pulse repetition rate is 10 Hz [53,54]. This requires the calculation of the PSWF 10 times in one second.…”

Section: Comparison Between Sinc-series-reconstructed Slepian Basis A...mentioning

confidence: 99%

Evaluation of the Prolate Spheroidal Wavefunctions via a Discrete-Time Fourier Transform Based Approach

Baddour,

Sun

2023

Symmetry

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Computation of prolate spheroidal wavefunctions (PSWFs) is notoriously difficult and time consuming. This paper applies operator theory to the discrete Fourier transform (DFT) to address the problem of computing PSWFs. The problem is turned into an infinite dimensional matrix operator eigenvalue problem, which we recognize as being the definition of the DPSSs. Truncation of the infinite matrix leads to a finite dimensional matrix eigenvalue problem which in turn yields what is known as the Slepian basis. These discrete-valued Slepian basis vectors can then be used as (approximately) discrete time evaluations of the PSWFs. Taking an inverse Fourier transform further demonstrates that continuous PSWFs can be reconstructed from the Slepian basis. The feasibility of this approach is shown via theoretical derivations followed by simulations to consider practical aspects. Simulations demonstrate that the level of errors between the reconstructed Slepian basis approach and true PSWFs are low when the orders of the eigenvectors are low but can become large when the orders of the eigenvectors are high. Accuracy can be increased by increasing the number of points used to generate the Slepian basis. Users need to balance accuracy with computational cost. For large time-bandwidth product PSWFs, the number of Slepian basis points required increases for a reconstruction to reach the same error as for low time-bandwidth products. However, when the time-bandwidth products increase and reach maximum concentration, the required number of points to achieve a given error level achieves steady state values. Furthermore, this method of reconstructing the PSWF from the Slepian basis can be more accurate when compared to the Shannon sampling approach and traditional quadrature approach for large time-bandwidth products. Finally, since the Slepian basis represents the (approximate) sampled values of PSWFs, when the number of points is sufficiently large, the reconstruction process can be omitted entirely so that the Slepian vectors can be used directly, without a reconstruction step.

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“…It is important to consider how to achieve photoacoustic imaging of other components under these bright backgrounds produced by high light absorption components. 7,8) Some researchers have found that the photoacoustic signal amplitude increased by about 5% when the temperature of biological light absorbers increased by 1 °C, and this effect was nearly linear. 9,10) If there is a way to effectively raise the temperature of components other than those with high light absorption coefficients, it is anticipated that the temperature rise of various components will be used to weight their respective photoacoustic signals, thereby reducing the impact of strong photoacoustic signals on imaging.…”

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confidence: 99%

Temperature difference effect of biological tissues induced by low-intensity unfocused ultrasound

Gong

Tao

Deng

2023

Appl. Phys. Express

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Low-intensity unfocused ultrasound (LIUU) is proposed for producing a temperature difference (TD) effect in biological tissues. A finite-element simulation model has been established to validate the method's rationality and its effectiveness in practical applications is further discussed through phantom experiments. Experimental results indicate that LIUU under suitable conditions can result in significant discrepancy of temperature increases in biological tissues with complex compositions. For photoacoustic imaging, the method leverages differences in acoustic absorption coefficients of biological tissues and extracts the TD as an imaging contrast, offering the potential to enhance tissue discrimination in conventional photoacoustic imaging with low light absorption coefficients.

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Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence

Cited by 7 publications

References 36 publications

Waveform Selection Based on Discrete Prolate Spheroidal Sequences for Near-Optimal SNRs for Photoacoustic Applications

Waveform Selection Based on Discrete Prolate Spheroidal Sequences for Near-Optimal SNRs for Photoacoustic Applications

Evaluation of the Prolate Spheroidal Wavefunctions via a Discrete-Time Fourier Transform Based Approach

Temperature difference effect of biological tissues induced by low-intensity unfocused ultrasound

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