Introduction HighTech radiotherapy capable to provide complex dose delivery modalities became widespread, making essential to evaluate clinical machine performances. Patient-specific verifications consist of dose delivery in a phantom before patient treatment. This operation of Delivery Quality Assurance (DQA) is repetitive and involving both workforce and Linac bunker occupational time. To work around this problem we developed a neural network capable of predicting passing rates a priori for Helical Tomotherapy (HT) DQA. In this paper we evaluated net performances, focusing on learning quality in function of specific machine parameters. Materials & Methods For net training we used 734 DQA plans calculated with HT treatment planning station and measured in Octavius phantom with a PTW 2D-729 chamber array. We chose as input planned sinogram and plan parameter information extracted from the machine database files. As output of the training, we used the measured map of 729 dose values. Results We focused on conv-net as: LENET, VGG-Like and ResNet-like network. Then we performed a tuning of the hyperparameters with a k-fould x-validation as a result of limiting data sample. As Loss Function the Mean Squared Error (MSE) was chosen, to test the accuracy we have done average calculation on all map dose value, calculating the difference between every single pixel of the two maps: simulate and measured. MSE mean value on all dataset plan is 12 cGy^2. Discussion & Conclusions Failure in reproducing dose distribution for some DQA could be ascribed to low statistic in the training set or to an unusual behavior of the machine respect to the mapping function represented by our neural network. This issues will be investigated. In conclusion, results of this study demostrated that deep learning with convolutional neural networks can become a valid support for an efficient routinely patient- specific quality assurance in HT.
Study of machine learning application for Tomotherapy Delivery Quality Assurance: evaluation of plan machine performances / Carlotti, Daniele; Cristina Pressello, Maria; Rauco, Roberta; Giagu, Stefano; Faccini, Riccardo; Aragno, Danilo. - (2019). (Intervento presentato al convegno International Conference on Monte Carlo Techniques for Medical Applications tenutosi a Mntreal, CA).
Study of machine learning application for Tomotherapy Delivery Quality Assurance: evaluation of plan machine performances
Daniele Carlotti;Stefano Giagu;Riccardo Faccini;
2019
Abstract
Introduction HighTech radiotherapy capable to provide complex dose delivery modalities became widespread, making essential to evaluate clinical machine performances. Patient-specific verifications consist of dose delivery in a phantom before patient treatment. This operation of Delivery Quality Assurance (DQA) is repetitive and involving both workforce and Linac bunker occupational time. To work around this problem we developed a neural network capable of predicting passing rates a priori for Helical Tomotherapy (HT) DQA. In this paper we evaluated net performances, focusing on learning quality in function of specific machine parameters. Materials & Methods For net training we used 734 DQA plans calculated with HT treatment planning station and measured in Octavius phantom with a PTW 2D-729 chamber array. We chose as input planned sinogram and plan parameter information extracted from the machine database files. As output of the training, we used the measured map of 729 dose values. Results We focused on conv-net as: LENET, VGG-Like and ResNet-like network. Then we performed a tuning of the hyperparameters with a k-fould x-validation as a result of limiting data sample. As Loss Function the Mean Squared Error (MSE) was chosen, to test the accuracy we have done average calculation on all map dose value, calculating the difference between every single pixel of the two maps: simulate and measured. MSE mean value on all dataset plan is 12 cGy^2. Discussion & Conclusions Failure in reproducing dose distribution for some DQA could be ascribed to low statistic in the training set or to an unusual behavior of the machine respect to the mapping function represented by our neural network. This issues will be investigated. In conclusion, results of this study demostrated that deep learning with convolutional neural networks can become a valid support for an efficient routinely patient- specific quality assurance in HT.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
