RESUMEN
This study aims at assessing approaches for generating high-resolution magnetic resonance imaging- (MRI-) based synthetic computed tomography (sCT) images suitable for orthopedic care using a deep learning model trained on low-resolution computed tomography (CT) data. To that end, paired MRI and CT data of three anatomical regions were used: high-resolution knee and ankle data, and low-resolution hip data. Four experiments were conducted to investigate the impact of low-resolution training CT data on sCT generation and to find ways to train models on low-resolution data while providing high-resolution sCT images. Experiments included resampling of the training data or augmentation of the low-resolution data with high-resolution data. Training sCT generation models using low-resolution CT data resulted in blurry sCT images. By resampling the MRI/CT pairs before the training, models generated sharper images, presumably through an increase in the MRI/CT mutual information. Alternatively, augmenting the low-resolution with high-resolution data improved sCT in terms of mean absolute error proportionally to the amount of high-resolution data. Overall, the morphological accuracy was satisfactory as assessed by an average intermodal distance between joint centers ranging from 0.7 to 1.2 mm and by an average intermodal root-mean-squared distances between bone surfaces under 0.7 mm. Average dice scores ranged from 79.8% to 87.3% for bony structures. To conclude, this paper proposed approaches to generate high-resolution sCT suitable for orthopedic care using low-resolution data. This can generalize the use of sCT for imaging the musculoskeletal system, paving the way for an MR-only imaging with simplified logistics and no ionizing radiation.