Time domain Diffuse Optical Tomography (TD-DOT) non-invasively probes the optical proprieties of biological tissue. These can be related to changes in tissue composition, thus making TD-DOT potentially valuable for cancer imaging. In particular, an application of interest is therapy monitoring for breast cancer. Thus, we developed a software tool for multi-wavelength TD-DOT in reflectance geometry. While the use of multiple wavelengths probes the main components of the breast, the chosen geometry offers the advantage of linking the photon flight time to the investigated depth. We validated the tool on silicone phantoms embedding an absorbing inclusion to simulate a malignant lesion in breast tissue. Also, we exploited the a priori information on position and geometry of the inclusion by using a morphological prior constraint. The results show a good localization of the depth of inclusion but a reduced quantification. When the morphological constraint is used, though, the localization improves dramatically, also reducing surface artifacts and improving quantification as well. Still, there is room for improvement in the quantification of the “lesion” properties.
Multi-wavelength time domain diffuse optical tomography for breast cancer: initial results on silicone phantoms
Edoardo Ferocino;Laura Di Sieno;Alberto Dalla Mora;Antonio Pifferi;Paola Taroni;Andrea Farina
2019-01-01
Abstract
Time domain Diffuse Optical Tomography (TD-DOT) non-invasively probes the optical proprieties of biological tissue. These can be related to changes in tissue composition, thus making TD-DOT potentially valuable for cancer imaging. In particular, an application of interest is therapy monitoring for breast cancer. Thus, we developed a software tool for multi-wavelength TD-DOT in reflectance geometry. While the use of multiple wavelengths probes the main components of the breast, the chosen geometry offers the advantage of linking the photon flight time to the investigated depth. We validated the tool on silicone phantoms embedding an absorbing inclusion to simulate a malignant lesion in breast tissue. Also, we exploited the a priori information on position and geometry of the inclusion by using a morphological prior constraint. The results show a good localization of the depth of inclusion but a reduced quantification. When the morphological constraint is used, though, the localization improves dramatically, also reducing surface artifacts and improving quantification as well. Still, there is room for improvement in the quantification of the “lesion” properties.File | Dimensione | Formato | |
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