McNaughton, J.; Holdsworth, S.; Chong, B.; Fernandez, J.; Shim, V.; Wang, A. Synthetic MRI Generation from CT Scans for Stroke Patients. BioMedInformatics2023, 3, 791-816.
McNaughton, J.; Holdsworth, S.; Chong, B.; Fernandez, J.; Shim, V.; Wang, A. Synthetic MRI Generation from CT Scans for Stroke Patients. BioMedInformatics 2023, 3, 791-816.
McNaughton, J.; Holdsworth, S.; Chong, B.; Fernandez, J.; Shim, V.; Wang, A. Synthetic MRI Generation from CT Scans for Stroke Patients. BioMedInformatics2023, 3, 791-816.
McNaughton, J.; Holdsworth, S.; Chong, B.; Fernandez, J.; Shim, V.; Wang, A. Synthetic MRI Generation from CT Scans for Stroke Patients. BioMedInformatics 2023, 3, 791-816.
Abstract
CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.
Keywords
Deep Learning; Image Synthesis; Image Generation; Machine Learning; Medical Imaging; CT to MRI; Synthetic MRI; Stroke; Image-to-image Translation
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
