Photothermal therapy (PTT) is a minimally invasive tumor destruction method that avoids surgery, chemotherapy, and radiation by eradicating diseased tissue using near infrared laser light delivered through interstitial optical fibers. As such, it represents an ideal solid tumor treatment without the high personal and institutional costs of conventional therapy. Deep tissue PTT response monitoring and guidance is currently performed using MRI thermometry, aiming for complete tumor destruction while limiting unwanted collateral damage to critical tissues. However, MRI thermometry suffers from slow imaging rates, motion induced errors and limited resolution. Further, it is resource intensive, limiting its use to large health care centers. We previously reported on a new frequency optimized Photoacoustic (PA) thermal imaging capability with unprecedented bulk tissue temperature sensitivity and field of view, ideal for clinical PTT guidance. A major remaining challenge for quantitative PA thermal guidance is the dynamically changing tissue environment during PTT, including hemoglobin oxygenation, edema, and protein denaturation, all of which contribute to PA thermometry errors. In this work, we explore Diffuse Optical Tomography (DOT) measurements and PAI with Deep Learning and conventional image reconstruction frameworks that factor in tissue dynamics, absolute measures of tissue optical properties and an accurate estimate of the PAI laser fluence, all factors needed for quantitative PAI imaging. With the known high sensitivity of DOT measurements to bulk tissue changes, particularly thermally induced coagulation, it is expected that our dual modality PTT guidance platform will provide affordable, accurate thermal image guidance during PTT.
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