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Characterization of normal and deformed red blood cells using simulated differential photoacoustic cross-section spectral data
Journal of Physics Communications Pub Date : 2021-03-12 , DOI: 10.1088/2399-6528/abebd0
Anuj Kaushik , Ratan K Saha

Frequency dependent differential photoacoustic cross-section (DPACS) over a large frequency band (100–1000 MHz) has been computed and subsequently, morphological parameters of photoacoustic (PA) source have been quantified. Green’s function method has been employed for computing the DPACS for a series of ellipsoidal droplets (with varying aspect ratio), Chebyshev particles (with different waviness (n) and deformation (ϵ) parameters), healthy red blood cell (RBC) and cells suffering from hereditary disorders (spherocytosis, elliptocytosis and stomatocytosis). The tri-axial ellipsoid form factor (TAEFF), finite cylinder form factor (CFF) and toroid form factor (TFF) models have been used to fit the DPACS spectrum to obtain size and shape information of the PA source. The TAEFF model estimates the shape parameters of the ellipsoidal droplets accurately (error < 5%). It is found that volume estimation is better (error < 10%) for lower order (n = 2, ϵ = 0.25) and very higher order (n = 35, 45, ϵ = 0.05) Chebyshev particles compared to those of n = 4, 6 and ϵ = 0.25. The TAEFF model predicts shape parameters of stomatocyte with volume error ≈15% but it is ≤6% for other cells. The opposite trend is observed for the CFF model. The TFF model is able to estimate the shape parameters efficiently for normal erythrocyte and stomatocyte but gives relatively large errors (>15%) for other deformed RBCs. The inverse problem framework may motivate to develop a PA-based technology to assess single cell morphology.



中文翻译:

使用模拟差分光声横截面光谱数据表征正常和变形的红细胞

计算了大频带(100–1000 MHz)上与频率有关的差分光声横截面(DPACS),随后,对光声(PA)源的形态参数进行了量化。格林函数方法已用于计算一系列椭圆形液滴(纵横比不同),切比雪夫粒子(具有不同的波纹度(n)和变形(()的DPACS)参数),健康的红血球(RBC)和患有遗传性疾病(单核细胞增多症,椭圆细胞增多症和造血细胞增多症)的细胞。三轴椭球形状因数(TAEFF),有限圆柱形状因数(CFF)和环形形状因数(TFF)模型已用于拟合DPACS光谱以获得PA源的尺寸和形状信息。TAEFF模型可准确估计椭圆形液滴的形状参数(误差<5%)。结果发现,与n = 4的切比雪夫粒子相比,较低阶(n = 2,ϵ = 0.25)和非常高阶(n = 35,45,ϵ = 0.05)的体积估计更好(误差<10%),6和ϵ= 0.25。TAEFF模型可预测口气细胞的形状参数,其体积误差约为15%,而其他细胞则为≤6%。对于CFF模型,观察到相反的趋势。TFF模型能够有效估计正常红细胞和口腔细胞的形状参数,但对于其他变形的RBC则给出相对较大的误差(> 15%)。逆问题框架可能会激发开发基于PA的技术来评估单细胞形态。

更新日期:2021-03-12
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