Objectives: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size. Hence, they do not allow clinical quantification of bone microstructure, which plays an important role in osteoarthritis, osteoporosis and fracture risk. For maxillofacial imaging, changes in small mineralized structures are important for dental, periodontal and ossicular chain diagnostics as well as treatment planning. Deep learning (DL)-based super-resolution (SR) models could allow for better evaluation of these microstructural details. In this study, we demonstrate a widely applicable method for increasing the spatial resolution of clinical CT images using DL, which only requires training on a limited set of data that are easy to acquire in a laboratory setting from e.g. cadaver knees. Our models are assessed rigorously for technical image quality, ability to predict bone microstructure, as well as clinical image quality of the knee, wrist, ankle and dentomaxillofacial region. Materials and methods: Knee tissue blocks from five cadavers and six total knee replacement patients as well as 14 extracted teeth from eight patients were scanned using micro-computed tomography. The images were used as training data for the developed DL-based SR technique, inspired by previous studies on single-image SR. The technique was benchmarked with an ex vivo test set, consisting of 52 small osteochondral samples imaged with clinical and laboratory CT scanners, to quantify bone morphometric parameters. A commercially available quality assurance phantom was imaged with a clinical CT device, and the technical image quality was quantified with a modulation transfer function. To visually assess the clinical image quality, CBCT studies from wrist, knee, ankle, and maxillofacial region were enhanced with SR and contrasted to interpolated images. A dental radiologist and dental surgeon reviewed maxillofacial CBCT studies of nine patients and corresponding SR predictions. Results: The SR models yielded a higher Pearson correlation to bone morphological parameters on the ex vivo test set compared to the use of a conventional image processing pipeline. The phantom analysis confirmed a higher spatial resolution on the images enhanced by the SR approach. A statistically significant increase of spatial resolution was seen in the third, fourth, and fifth line pair patterns. However, the predicted grayscale values of line pair patterns exceeded those of uniform areas. Musculoskeletal CBCT images showed more details on SR predictions compared to interpolation. Averaging predictions on orthogonal planes improved visual quality on perpendicular planes but could smear the details for morphometric analysis. SR in dental imaging allowed to visualize smaller mineralized structures in the maxillofacial region, however, some artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores in all categories for both CBCT and SR. Although not statistically significant, the dental radiologist slightly preferred the original CBCT images. The dental surgeon scored one of the SR models slightly higher compared to CBCT. The interrater variability κ was mostly low to fair. The source code (https://doi.org/10.5281/zenodo.8041943) and pretrained SR networks (https://doi.org/10.17632/4xvx4p9tzv.1) are publicly available. Conclusions: Utilizing experimental laboratory imaging modalities in model training could allow pushing the spatial resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT imaging. Implications of SR include higher patient throughput, more precise diagnostics, and disease interventions at an earlier state. However, the grayscale distribution of the images is modified, and the predictions are limited to depicting the mineralized structures rather than estimating density or tissue composition. Finally, while the musculoskeletal images showed promising results, a larger maxillofacial dataset would be recommended for training SR models in dental applications.
