Enhancing online dose distribution reconstruction in radiotherapy through sparse sampled rotating fiber arrays and deep learning

A optical fiber array dosimeter based on radiation-induced luminescence can be used in two-dimensional dose distribution measurements. However, using rotating fiber arrays introduces challenges in terms of online monitoring and variations in detector performance due to the long data acquisition time. This study aims to propose and demonstrate a novel method for online fast reconstruction of two-dimensional dose distributions in radiation therapy using projection data obtained from sparsely sampled rotating fiber arrays. The projection data were processed in both the projection and image domains to achieve restoration and noise reduction by deep learning techniques. The performance of the proposed method was evaluated using simulation data obtained by Monte Carlo toolkit Geant4 and experimental data obtained from a medical accelerator. Results showed that the method can successfully reconstruct dose distribution through sparse sampling and reduce the measurement time to one-sixth of the original. The peak signal-to-noise ratio, root mean square error, and structural similarity index were 23.72, 2.68, and 0.95, respectively. Additionally, the 3%/3 mm gamma pass rate reached 98.4% under specific irradiation conditions with a dose of at least 10% of the maximum dose. The integration of sparse sampling techniques, rotating fiber arrays, and deep learning techniques offers new possibilities for the fast and efficient reconstruction of two-dimensional dose distributions, ensuring high-quality radiotherapy outcomes. •Combined sparse sampling and deep learning for fast dose distribution measurement using fiber array was proposed.•The PSNR, RMSE, and SSIM of proposed method show superior performance than other methods.•The 3%/3 mm gamma pass rate reaches 98.4% under specific irradiation condition.•Measurement time is reduced while maintaining a remarkable spatial resolution.