Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

Emilie Alvarez Andres, Lucas Fidon, Maria Vakalopoulou, Marvin Lerousseau, Alexandre Carré, Roger Sun, Guillaume Klausner, Samy Ammari, Nathan Benzazon, Sylvain Reuzé, Théo Estienne, Stéphane Niyoteka, Enzo Battistella, Angéla Rouyar, Georges Noël, Anne Beaudre, Frédéric Dhermain, Eric Deutsch, Nikos Paragios, Charlotte Robert

Abstract: This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network. Four hundred two brain tumor cases were retrieved, yielding associations between 182 computed tomography (CT) and T1-weighted MRI (T1) scans, 180 CT and contrast-enhanced T1-weighted MRI (T1-Gd) scans, and 40 CT, T1, and T1-Gd scans. A 3D CNN was used to map T1 or T1-Gd onto CT scans and evaluate the importance of different components. First, the training set size’s influence on testing set accuracy was assessed. Moreover, we evaluated the MRI sequence impact, using T1-only and T1-Gd–only cohorts. We then investigated 4 MRI standardization approaches (histogram-based, zero-mean/unit-variance, white stripe, and no standardization) based on training, validation, and testing cohorts composed of 242, 81, and 79 patients cases, respectively, as well as a bias field correction influence. Finally, 2 networks, namely HighResNet and 3D UNet, were compared to evaluate the architecture’s impact on the pCT quality. The mean absolute error, gamma indices, and dose-volume histograms were used as evaluation metrics.