Photo-magnetic imaging (PMI) is a novel diffuse optical imaging technique used to recover high resolution images of the optical absorption coefficient of bio-tissue. It uses near-infrared laser light to slightly warm up the tissue and measures the induced temperature using magnetic resonance thermometry (MRT). The measured temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. We present a convolutional neural network -based image reconstruction algorithm that improves the accuracy of the recovered absorption maps while reducing the recovery time. This approach directly delineates the boundaries of tumors on the MRT maps. These boundaries are then used to generate soft a priori information that will be employed to constrain the standard PMI image reconstruction algorithm. We evaluate the performance of the algorithm using a tissue-like phantom with an inclusion representing the presence of a potential tumor. The obtained results show that our new method can delineate the tumor region with an accuracy of ~96%.
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