It is very important for the safety of photothermal therapy to detect the temperature change of the interaction between laser and tissue during photothermal therapy. Using photoacoustic imaging can sensitively reflect the temperature distribution in the tissue. This paper proposes a photoacoustic temperature measurement method combined with quantitative absorption distribution. On the one hand, this method uses the temperature measurement method based on photoacoustic imaging to monitor the temperature change of the target area in real time; on the other hand, it quantifies the absorption distribution of the nanoprobe with the photoacoustic and photothermal effect in the target area. Thereby providing a feedback signal for temperature control during the treatment process, realizing precise control of the target area temperature and reconstruction of the absorption distribution of the target area nanoprobe. The study results verify the feasibility of this method. Compared with traditional quantitative methods, this article considers the dynamic changes of the target area temperature and provides treatment feedback. The feedback control guided by multiple parameters minimizes the damage to the surrounding healthy tissues, while improving the accuracy of reconstruction is helpful for the quantitative assessment of the disease.
Photoacoustic imaging (PAI) has unique structural and functional imaging capabilities and has attracted widespread attention in clinical diagnosis. However, in the case of fast or real-time imaging, the reconstruction of sparse-view sampling data of photoacoustic data is still a challenge. In this paper, we present our study on simultaneous algebraic photoacoustic reconstruction technique based on total variation. The proposed algorithm constructs an accurate projection matrix based on the detection sensitivity of the array element. Combining simultaneous algebraic reconstruction technique (SART) and total variation (TV) to optimize sparse-view sampling photoacoustic image reconstruction results. Numerical simulation experiment results show that the algorithm reconstructs high-quality photoacoustic images from sparse-view sampling data, effectively eliminates under-sampling artifacts, and preserves edge details. Compared with traditional algorithms, this algorithm may be a practical and effective algorithm for sparse-view PAI reconstruction.
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