| Title: |
Magnetic resonance imaging-based bone imaging of the lower limb:Strategies for generating high-resolution synthetic computed tomography |
| Authors: |
Florkow, Mateusz C; Nguyen, Chien H; Sakkers, Ralph J B; Weinans, Harrie; Jansen, Mylene P; Custers, Roel J H; van Stralen,Marijn; Seevinck, Peter R; DHS 3D Lab; MS Orthopaedie Algemeen; Regenerative Medicine and Stem Cells; ORT Research; Lab Reumatologie/Klinische Immunologie; Infection & Immunity; Beeldverwerking ISI; Cancer |
| Publication Year: |
2024 |
| Subject Terms: |
bone imaging; deep learning; lower limb; magnetic resonance imaging; synthetic computed tomography; Orthopedics and Sports Medicine; Journal Article |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text/plain |
| Language: |
English |
| ISSN: |
0736-0266 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/450412 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/450412 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.1B51BA3 |
| Database: |
BASE |