Backflow modeling in nanofluid infusion and analysis of its effects on heat induced damage during magnetic hyperthermia

Tang, Yundong; Zou, Jian; Flesch, Rodolfo C.C.; Jin, Tao

doi:10.1016/j.apm.2022.10.020

Cited by 4 publications

(3 citation statements)

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“…[28] In addition, the parameters in the Arrhenius model are 1.19 × 10 35 s −1 for frequency factor and 232 kJ•mol −1 for the activation energy, both given for hepatoma cells as analysis object. [10]…”

Section: Materials Parametersmentioning

confidence: 99%

“…[9] Tang et al established a poroelastic model considering geometric nonlinear behavior, and simulated an irregular nanofluid distribution inside biological tissues after the nanofluid injection. [10] Nevertheless, the distributions predicted by the mathematical model in Ref. [10] always had some difference with respect to the real case used as baseline for comparison due to the complexity of the injection behavior.…”

Section: Introductionmentioning

confidence: 99%

“…[10] Nevertheless, the distributions predicted by the mathematical model in Ref. [10] always had some difference with respect to the real case used as baseline for comparison due to the complexity of the injection behavior.…”

Section: Introductionmentioning

confidence: 99%

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Extraction method of nanoparticles concentration distribution from magnetic particle image and its application in thermal damage of magnetic hyperthermia

et al. 2023

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Magnetic particle imaging (MPI) technology can generate a real-time magnetic nanoparticle (MNP) distribution image for biological tissues, and its use can overcome the limitations imposed in magnetic hyperthermia treatments by the unpredictable MNP distribution after the intratumoral injection of nanofluid. However, the MNP concentration distribution is generally difficult to be extracted from MPI images. This study proposes an approach to extract the corresponding concentration value of each pixel from an MPI image by a least squares method (LSM), which is then translated as MNP concentration distribution by an interpolation function. The resulting MPI-based concentration distribution is used to evaluate the treatment effect and the results are compared with the ones of two baseline cases under the same dose: uniform distribution and MPI-based distribution considering diffusion. Additionally, the treatment effect for all these cases is affected by the blood perfusion rate, which is also investigated deeply in this study. The results demonstrate that the proposed method can be used to effectively reconstruct the concentration distribution from MPI images, and that the weighted LSM considering a quartic polynomial for interpolation provides the best results with respect to other cases considered. Furthermore, the results show that the uniformity of MNP distribution has a positive correlation with both therapeutic temperature distribution and thermal damage degree for the same dose and a critical power dissipation value in the MNPs. The MNPs uniformity inside biological tissue can be improved by the diffusion behavior after the nanofluid injection, which can ultimately reflect as an improvement of treatment effect. In addition, the blood perfusion rate considering local temperature can have a positive effect on the treatment compared to the case which considers a constant value during magnetic hyperthermia.

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Section: Materials Parametersmentioning

confidence: 99%