Quantitative photoacoustic tomography with light fluence compensation based on radiance Monte Carlo model. (21st March 2023)

• Record Type: Journal Article
• Authors: Zheng, Sun; Yingsa, Hou; Meichen, Sun; Qi, Meng
• Is Part Of: Physics in medicine & biology. Volume 68:Number 6(2023)
• Publication Date: 2023-03-21
• Subjects: photoacoustic tomography -- inverse problem -- image reconstruction -- quantitative imaging -- optical absorption coefficient -- Monte Carlo model of radiance -- alternating optimization; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/acbe90
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective . Photoacoustic tomography (PAT) is a rapidly evolving imaging modality that provides images with high contrast and spatial resolution showing the optical properties of biological tissues. The photoacoustic pressure is proportional to the product of the optical absorption coefficient and the local light fluence. The essential challenge in reconstructing quantitative images representing spatially varying absorption coefficients is the unknown light fluence. In addition, optical attenuation induces spatial variations in the light fluence, and the heterogeneity of the fluence determines the limits of reconstruction quality and depth. Approach. In this work, a reconstruction enhancement scheme is proposed to compensate for the variation in the light fluence in the absorption coefficient recovery. The inverse problem of the radiance Monte Carlo model describing light transport through the tissue is solved by using an alternating optimization strategy. In the iteration, the absorption coefficients and photon weights are alternately updated. Main results. The method provides highly accurate quantitative images of absorption coefficients in simulations, phantoms, and in vivo studies. The results show that the method has great potential for improving the accuracy of absorption coefficient recovery compared to conventional reconstruction methods that ignore light fluence variations. Comparison with state-of-the-art fluence compensation methods shows significant improvements in root mean square error, normalized mean square absolute distance, and structural similarity metrics. Significance. This method achieves high precision quantitative imaging by compensating for nonuniform light fluence without increasing the complexity and operation of the imaging system.