Influence of the anisotropic mechanical properties of the breast cancer on photoacoustic imaging

Metwally, Mohamed K.; El-Gohary, Sherif H.; Han, Sangheon; Byun, Kyung Min; Kim, Tae‐Seong

doi:10.1109/cibec.2014.7020958

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…where µ (~0.00025 [25]) is the friction coefficient; F compress is the compressive force from the channel sidewalls, acting on the deformed cell. We applied a constant Poisson' ratio for three breast cell lines since the Poisson's ratio varies little between breast tumor cells and benign cells [26]. F compress can be approximated by the Hertz contact theory [24]:…”

Section: Modeling Of a Deformed Cell Transiting Along A Straight Channelmentioning

confidence: 99%

A Narrow Straight Microchannel Array for Analysis of Transiting Speed of Floating Cancer Cells

et al. 2022

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Investigating floating cells along a narrow microchannel (e.g., a blood vessel) for their transiting speeds and the corresponding roles of cell physical properties can deepen our understanding of circulating tumor cells (CTCs) metastasis via blood vessels. Many existing studies focus on the cell transiting process in blood vessel-like microchannels; further analytical studies are desired to summarize behaviors of the floating cell movement under different conditions. In this work, we perform a theoretical analysis to establish a relation between the transiting speed and key cell physical properties. We also conduct computational fluid dynamics simulation and microfluidic experiments to verify the theoretical model. This work reveals key cell physical properties and the channel configurations determining the transiting speed. The reported model can be applied to other works with various dimensions of microchannels as a more general way to evaluate the cancer cell metastasis ability with microfluidics.

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Section: Modeling Of a Deformed Cell Transiting Along A Straight Channelmentioning

confidence: 99%

A Narrow Straight Microchannel Array for Analysis of Transiting Speed of Floating Cancer Cells

et al. 2022

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“…OAI requires highly sensitive ultrasound detectors for collecting weak OA signals generated from deeper biological tissues. Also, fluence compensation over different wavelengths to unmix different chromophores is challenging due to the nonlinear variation of fluence distribution 14 . Many OAI studies focus on obtaining the initial pressure rise distribution from the acquired tomographic acoustic measurements wherein the optical fluence is assumed to be constant 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

“…Hence accurate recovery of optical absorption coefficient remains challenging in OAI 19 , 20 . The nonlinear variation of the fluence 14 , 16 will affect the generation and propagation of the OA wave.…”

Section: Introductionmentioning

confidence: 99%

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

et al. 2022

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. Significance: Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect. Aim: Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium. Approach: Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets. Results: The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction. Conclusions: The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

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“…Definition for the simulation parameters through the numerical simulation example of the proposed mathematical model (values are almost compatible with laser wavelengths in the NIR: near infrared region based on:[77,80]). 𝑒 𝑎 , 𝑑 𝑎 , 𝑐, 𝛼, 𝛾, 𝛽, 𝑎, 𝑎𝑛𝑑 𝑓 must be defined as follow: 𝑒 𝑎…”

mentioning

confidence: 99%

A Mathematical Model for Simulating Photoacoustic Signal Generation Process in Biological Tissues

GadAllah

Mohamed

Hefnawy

et al. 2023

Preprint

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Background: Biomedical photoacoustic imaging (PAI) is a hybrid imaging modality based on the laser-generated ultrasound waves due to the photoacoustic (PA) effect physical phenomenon that has been reported firstly by A. G. Bell in 1880. Numerical modeling-based simulation for the PA signal generation process in biological tissues helps researchers for decreasing error trials in-vitro and hence decreasing error rates for in-vivo experiments. Numerical modeling methods help in obtaining a rapid modeling procedure comparable to pure mathematics. However, if a proper simplified mathematical model can be founded before applying numerical modeling techniques, it will be a great advantage for the overall numerical model. Most scientific theories, equations, and assumptions, been proposed to mathematically model the complete PA signal generation and propagation process in biological tissues, are so complicated. Hence, the researchers, especially the beginners, will find a hard difficulty to explore and obtain a proper simplified mathematical model describing the process. That’s why this paper is introduced. Methods: In this paper we have tried to simplify understanding for the biomedical PA wave’s generation and propagation process, deducing a simplified mathematical model for the whole process. The proposed deduced model is based on three steps: a- pulsed laser irradiance, b- diffusion of light through biological tissue, and c- acoustic pressure wave generation and propagation from the target tissue to the ultrasound transducer surface. COMSOL Multiphysics, which is founded due to the finite element method (FEM) numerical modeling principle, has been utilized to validate the proposed deduced mathematical model on a simulated biological tissue including a tumor inside. Results and Conclusion: The time-dependent study been applied by COMSOL has assured that the proposed deduced mathematical model may be considered as a simplified, easy, and fast startup base for scientific researchers to numerically model and simulate biomedical PA signals’ generation and propagation process utilizing any proper software like COMSOL.

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Influence of the anisotropic mechanical properties of the breast cancer on photoacoustic imaging

Cited by 11 publications

References 16 publications

A Narrow Straight Microchannel Array for Analysis of Transiting Speed of Floating Cancer Cells

A Narrow Straight Microchannel Array for Analysis of Transiting Speed of Floating Cancer Cells

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

A Mathematical Model for Simulating Photoacoustic Signal Generation Process in Biological Tissues

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