Abstract: A method and system for performing online adapted radiotherapy are provided using combined ultrasound and ionizing radiation induced acoustic imaging (iRAI) computed tomography imaging techniques that can be used for measurement of low to ultrahigh dose deliveries (>40 Gy/s). Multiplexed transducers detect US and iRAI signals allowing for anatomical/functional imaging and radiation mapping with absolute dosimetry measurements of a region of interest during a radiotherapy session. Corrections to radiation dosage intensities and locations is determined and provided as feedback to a radiation source to improve the accuracy of applied radiation dosages intra- or inter-radiotherapy treatment sessions preventing the irradiation of healthy tissues and ensuring the accurate delivery of radiation to a tumor or region of interest.

Abstract: A method and system may utilize a quantum information state analog to reinforcement learning techniques to determine whether to adapt a course of treatment for an oncology patient. A quantum-based reinforcement learning engine may represent a decision to adapt and a decision not to adapt the course of treatment for the oncology patient as quantum information states in a superposition. Each quantum information state has a corresponding amplitude indicative of the likelihood that the quantum information state has a higher expected clinical outcome for the oncology patient. Using a quantum search algorithm, the quantum-based reinforcement learning engine identifies amplitudes for each quantum information state in the superposition. The quantum-based reinforcement learning engine instructs a health care provider to adapt the course of treatment for the oncology patient when a likelihood corresponding to the decision to adapt state exceeds a likelihood threshold.