Deep learning-based fluorescence image correction for high spatial resolution precise dosimetry

Objective. While radiation-excited fluorescence imaging has great potential to measure absolute 2D dose distributions with high spatial resolution, the fluorescence images are contaminated by noise or artifacts due to Cherenkov light, scattered light or background noise. This study developed a novel deep learning-based model to correct the fluorescence images for accurate dosimetric application. Approach. 181 single-aperture static photon beams were delivered to an acrylic tank containing quinine hemisulfate water solution. The emitted radiation-exited optical signals were detected by a complementary metal-oxide semiconductor camera to acquire fluorescence images with 0.3 x 0.3 mm2 pixel size. 2D labels of projected dose distributions were obtained by applying forward projection calculation of the 3D dose distributions calculated by a clinical treatment planning system. To calibrate the projected dose distributions for Cherenkov angular dependency, a novel empirical Cherenkov emission calibration method was performed. Total 400-epoch supervised learning was applied to a convolutional neural network (CNN) model to predict the projected dose distributions from fluorescence images, gantry, and collimator angles. Accuracy of the calculated projected dose distributions was evaluated with that of uncorrected or conventional methods by using a few quantitative evaluation metrics. Main results. The projected dose distributions corrected by the empirical Cherenkov emission calibration represented more accurate noise-free images than the uncalibrated distributions. The proposed CNN model provided accurate projected dose distributions. The mean absolute error of the projected dose distributions was improved from 2.02 to 0.766 mm & BULL;Gy by the CNN model correction. Moreover, the CNN correction provided higher gamma index passing rates for three different threshold criteria than the conventional methods. Significance. The deep learning-based method improves the accuracy of dose distribution measurements. This technique will also be applied to optical signal denoising or Cherenkov light discrimination in other imaging modalities. This method will provide an accurate dose verification tool with high spatial resolution.