Numerical light dosimetry in murine tissue: analysis of tumor curvature and angle of incidence effects upon fluence in the tissue

In order to better understand light dosimetry issues for photodynamic therapy (PDT), we have used various tumor and normal tissue geometries to develop a diffusion model of light transport in tissues. We hypothesize that tumor tissues with curved surfaces will have significantly different internal fluence distributions, as compared to tissues with flat surfaces. Using a mouse subcutaneous tumor and rear limb muscle model we compared the internal fluence values within the tissue. In addition, numerical simulations for these corresponding tissue geometries and laser light incidence angles were made. Assuming that the relative photon fluence in the tissue can be accurately modeled by the diffusion equation, we used a finite element approach to approximate the distribution inside the tissue. Meshes with different geometries (flat and curved with different curvatures) were used in this study to mimic the tumor and leg geometries of the murine tumors treated in the lab. Results suggest that tissues surface geometries and incidence angle of light can significantly alter the photon fluence inside the tissue. The photon fluence difference for an 8 mm diameter, curved surface mouse tumor vs. flat muscle tissue can be as high as 20%. In general, the greater the tissues curvature, the greater the potential loss in light fluence is. In summary, our data demonstrates the importance of tissue surface geometry and the incidence angle of light in determining optimal PDT light dosimetry, and indicates that comparisons between tissue geometries must be carried out with attention to differences in the internal optical distribution. The use of optical radiation in medical physics is important in several fields, such as tissue ablation and coagulation, photodynamic therapy (PDT), optical imaging and near infrared spectroscopy (NIRS). (1) In this study, we will restrict our discussion to PDT, which is being investigated for a possible treatment for tumors, and is currently used clinically for esophageal and lung tumors, as well as non -cancer applications such as age -related macular degeneration. PDT is a phototherapy, a term which includes all treatments that use light to induce reactions in the body which are of benefit to patient. For our purposes, PDT treatment is done with a drug called a photosensitizer, which is being studied in preclinical animal models of tumors. The photosensitizer is injected into mice bearing tumors, and studies are underway to optimize the delivery of the treatment to the tumor tissue. The photosensitizer alone is thought to be harmless at typical therapeutic doses, indicating it has no effect on either healthy or abnormal tissue. However, when laser is directed onto tissue containing the photosensitizer, the photosensitizer becomes activated and the tissue is rapidly destroyed, but only precisely where the light has been directed. Thus, by careful application of the light beam, the technique can be targeted selectively to the abnormal tissue. In PDT, several properties of the tissue, including oxygen concentration, photosensitizer concentration and light dose determine efficacy. In this study we concentrated on determining factors that influence the exact number of photons delivered into tumor and normal tissues. We hypothesize that tissue curvature has a significant effect on PDT treatment efficacy, such that it is difficult to directly compare the treatment efficacy in curved versus flat tissue geometries, because the amount of light penetrating the surface of the tissue is changed due to reflections and also due to remission out of the tissue. Since the lasing dose is an important parameter to evaluate the potential for PDT treatment, it's necessary for us to study light fluence at depth in a number of tissue geometries, including, tissues without tumor (flat muscle tissue geometry) , tissues with tumor (curved tumor geometry tissue) using the same laser output for all treatments. Here, we develop a numerical diffusion model to simulate several samples with different geometries. Assuming that the photon