SU-F-J-01: Measurement of Blood Flow During PDT Using Diffuse Correlation Spectroscopy

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Abstract

Purpose:

To investigate the variation in blood flow during PDT and to determine the oxygen supplying rate, g, to refine the empirical macroscopic model.

Methods:

RIF tumors were propagated in female C3H mice by injecting cell suspension at a concentration of 1×106 cells/ml on the shoulder region. Photosensitizer(e.g., HPPH) was injected into the mice through tail vein at a concentration of 0.25 mg/kg when the tumors grew to ∼6 mm in diameter and depth. Temporal changes of tissue oxygen concentration were recorded prior, during and post PDT treatment using OxyLite single-channel tissue pO2 monitor(OXFORD OPTRONIX). [3O2] was approximated by multiplying the measured pO2 with 3O2 solubility in tissue(1.295 µM/mmHg). The experimental [3O2] profile were compared with those simulated with the macroscopic model with and without considering the blood flow changes. The dynamical change of blood flow during PDT was simulated by making parameter g to be time dependent, in which the g function was obtained from Photofrin-mediated PDT in RIF tumors. The relative blood flow changes in the tumors prior, during and post PDT treatment were measured using diffuse correlation spectroscopy (DCS) in a non-contact configuration.

Results:

Both macroscopic models, with and without blood flow changes, show reasonably good agreement with the experimental [3O2] measurements. However, a rapid increase in the measured [3O2] between 700–1200s during PDT treatment could not be simulated with our empirical models. As blood flow is the direct measure of tissue oxygen supply and different photosensitizers might impact the blood flow differently, detailed studies are essential to investigate the variation in blood flow changes and the impact on the [3O2] consumption using different photosensitizers.

Conclusion:

DCS can be used to monitor relative blood flow noninvasively. The continuous input of oxygen supply rate is essential to refine our empirical macroscopic model to better predict PDT treatment outcome.

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