A Dual-Energy Computed Tomography Guided Intelligent Radiation Therapy Platform.

Purpose: The integration of advanced imaging and artificial intelligence technologies in radiation therapy has revolutionized cancer treatment by enhancing precision and adaptability. This study introduces a novel dual-energy computed tomography (DECT) guided intelligent radiation therapy (DEIT) platform designed to streamline and optimize the radiation therapy process. The DEIT system combines DECT, a newly designed dual-layer multileaf collimator, deep learning algorithms for auto-segmentation, and automated planning and quality assurance capabilities.

Methods And Materials: The DEIT system integrates an 80-slice computed tomography (CT) scanner with an 87 cm bore size, a linear accelerator delivering 4 photon and 5 electron energies, and a flat panel imager optimized for megavoltage (MV) cone beam CT acquisition. A comprehensive evaluation of the system's accuracy was conducted using end-to-end tests. Virtual monoenergetic CT images and electron density images of the DECT were generated and compared on both phantom and patient. The system's auto-segmentation algorithms were tested on 5 cases for each of the 99 organs at risk, and the automated optimization and planning capabilities were evaluated on clinical cases.

Results: The DEIT system demonstrated systematic errors of less than 1 mm for target localization. DECT reconstruction showed electron density mapping deviations ranging from -0.052 to 0.001, with stable Hounsfield unit consistency across monoenergetic levels above 60 keV, except for high-Z materials at lower energies. Auto-segmentation achieved dice similarity coefficients above 0.9 for most organs with an inference time of less than 2 seconds. Dose-volume histogram comparisons showed improved dose conformity indices and reduced doses to critical structures in auto-plans compared to manual plans across various clinical cases. In addition, high gamma passing rates at 2%/2 mm in both 2-dimensional (above 97%) and 3-dimensional (above 99%) in vivo analyses further validate the accuracy and reliability of treatment plans.

Conclusions: The DEIT platform represents a viable solution for radiation treatment. The DEIT system uses artificial intelligence-driven automation, real-time adjustments, and CT imaging to enhance the radiation therapy process, improving efficiency and flexibility.