Radiological Image Quality Assessment of Synthetic 3T MRI Image from 64mT MRI
Motivation: The necessity to enhance the quality of portable low-field MRI images, which are crucial for wider accessibility but lack high-resolution, drives this research. Goal(s): This study aims to determine whether Synthetic 3T technology can elevate low-field image quality to that of high-field 3T standards, making it diagnostically adequate. Approach: We employed a generative network to transform low-field images to higher quality, maintaining pixel-level accuracy and structural integrity. The study involved a paired dataset from 37-healthy subjects and an evaluation on 20-images by two neuroradiologists. Results: Synthetic 3T demonstrated improved clarity, structure, and contrast, aligning closer to the 3T-quality than the original low-field images. Impact: This investigation highlights the potential of Synthetic 3T to bridge the gap in portable MRI imaging, enabling broader clinical utility. Further research could pivot on its application in pathological cases, with an aim to enhance diagnostic capabilities in resource-limited settings.
Authors
- Katie L. McMahon (ORCID: https://orcid.org/0000-0002-6357-615X)
- Shenjun Zhong (ORCID: https://orcid.org/0000-0001-6542-649X)
- Gary F. Egan (ORCID: https://orcid.org/0000-0002-3186-4026)
- Meng Law (ORCID: https://orcid.org/0000-0001-8414-1991)
- Andrew A. Dwyer (ORCID: https://orcid.org/0000-0002-7023-6794)
- Paul M. Parizel (ORCID: https://orcid.org/0000-0002-0221-2854)
- Shawna Farquharson
- Gail Durbridge
- Kh Tohidul Islam (ORCID: https://orcid.org/0000-0003-2172-7041)
- Zhaolin Chen (ORCID: https://orcid.org/0000-0002-0173-6090)
- Markus Barth (ORCID: https://orcid.org/0000-0002-0520-1843)
- Parisa Zakavi
- Helen Kavnoudias (ORCID: https://orcid.org/0000-0001-7686-6802)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/3764
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00