Deep learning the sound of light to guide surgeries

Abstract

Photoacoustic imaging utilizes light and sound to make images by transmitting laser pulses that illuminate regions of interest, which subsequently absorb the light, causing thermal expansion and the generation of sound waves that are detected with conventional ultrasound transducers. The Photoacoustic and Ultrasonic Systems Engineering (PULSE) Lab is developing novel methods that use photoacoustic imaging to guide surgeries with the ultimate goal of eliminating surgical complications caused by injury to important structures like major blood vessels and nerves that are otherwise hidden from a surgeons immediate view. This paper summarizes our recent work to learn from the physics of sound propagation in tissue and develop acoustic beamforming algorithms that improve image quality, using state-of-the-art deep learning methods. These deep learning methods hold promise for robotic tracking tasks, visualization and visual servoing of surgical tool tips, and assessment of relative distances between the surgical tool and nearby critical structures (e.g., major blood vessels and nerves that if injured will cause severe complications, paralysis, or patient death). Impacted surgeries and procedures include neurosurgery, spinal fusion surgery, hysterectomies, and biopsies.