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.