Session Item

The increasing use of MRI in radiotherapy workflow has led to the development of "MRI-only" treatment planning methods, especially based on synthetic CT generation (sCT). Deep learning algorithms are the most attractive methods today for generating sCTs. However, the use of these algorithms requires a large amount of data, which can be critical in the case of paediatric patients, where cohorts are often small even when gathered from several centres and imagers. Thus, this study aims to compare four training methods based on a 3D HighResNet deep neural network architecture to generate sCTs for paediatric patients with brain tumours treated with protontherapy. The impact of using a learning strategy based on scans converted to relative stopping power (RSP) to avoid imager dependency while increasing cohort size was also studied.

A cohort of 394 adult patients including CT/MRI brain pairs (199 T1, 195 T1Gd) and a cohort of 198 paediatric patients including CT/MRI brain pairs (64 T1, 134 T1Gd) were used to train, validate and test a 3D HighResNet neural network. Pre-processing was applied to the images (N4 bias field correction, CT to MRI rigid registration, Z-score normalisation, intensity-clip, volume resampling). Except for method (1_HU), scan Hounsfield Units (HU) from 3 different devices set at 2 different high voltages (120 kVp and 135 kVp) were converted to RSP using stoechiometric calibration curves. Four methods of training and validation of the network were then compared: (1) paediatric-only (1_RSP and 1_HU), (2) adult-only, (3) mixed adult and paediatric, and (4) transfer learning with pre-training on adult patients before optimizing weights on the paediatric patients. The paediatric test cohort remained unchanged among the different methods and included 40 children. The Mean Absolute Error (MAE) was used to evaluate sCTs and as loss function of the network. Early stopping on the validation set was used as stopping criterion. Wilcoxon tests were performed to assess the significance of the observed differences with a threshold value of 5%.

The average MAE within heads were respectively equal to 107 ± 20 HU, 143 ± 20 HU, 106 ± 19 HU and 102 ± 19 HU for (1_RSP), (2), (3) and (4). Training with RSP rather than HU in method (1) showed an improvement in average MAE within heads from 121 ± 22 HU to 107 ± 20 HU. Wilcoxon tests showed that these differences were significant (p<0.0001), except between methods (1_RSP) and (3) (p >0.25).

Our analysis confirms the difficulty of generating paediatric sCTs directly from an adult model. The transfer learning method combined with a transformation of the scans into RSPs proposed in this study is an effective strategy to overcome the lack of patients in paediatric cohorts, and is extendable to adult patients. Dosimetric differences resulting from the different strategies have to be quantified in a near future.