부산대학교 도서관

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. [ABSTRACT FROM AUTHOR]