Sensitivity of quantitative photoacoustic tomography inversion schemes to experimental uncertainty

被引:1
|
作者
Fonseca, Martina [1 ]
Saratoon, Teedah [1 ]
Zeqiri, Bajram [2 ]
Beard, Paul [1 ]
Cox, Ben [1 ]
机构
[1] UCL, Dept Med Phys & Biomed Engn, Gower St, London WC1E 6BT, England
[2] Natl Phys Lab, Hampton Rd, Teddington TW11 0LW, Middx, England
关键词
quantitative photoacoustic imaging; virtual phantom studies; model-based inversion; unmixing strategies; sources of uncertainty; accuracy; OPTICAL-PROPERTIES; DISTRIBUTIONS; SCATTERING;
D O I
10.1117/12.2210916
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The ability to accurately quantify chromophore concentration from photoacoustic images would have a major impact on pre-clinical and clinical imaging. Recent years have seen significant advances in the theoretical understanding of quantitative photoacoustic imaging and in the development of model-based inversion strategies that overcome issues such as non-uniqueness and non-linearity. Nevertheless, their full in vivo implementation has not successfully been achieved, partially because experimental uncertainties complicate the transition. In this study, a sensitivity analysis is performed to assess the impact on accuracy of having uncertainty in critical experimental parameters such as scattering, beam diameter, beam position and calibration factor. This study was performed using two virtual phantoms, at one illumination and four optical wavelengths. The model-based inversion was applied in 3 variants - one just inverting for chromophores and two others further inverting for either a scaling factor or the scatterer concentration. The performance of these model-based inversions is also compared to linear unmixing strategies - with and without fluence correction. The results show that experimental uncertainties in a priori fixed parameters - especially calibration factor and scatterer concentration - significantly affect accuracy of model-based inversions and therefore measures to ameliorate this uncertainty should be considered. Including a scaling parameter in the inversion appears to improve quantification estimates. Furthermore, even with realistic levels of experimental uncertainty in model-based input parameters, they outperform linear unmixing approaches. If parameter uncertainty is large and has significant impact on accuracy, the parameter can be included as an unknown in model-based schemes.
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页数:14
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