Deep learning based apparent diffusion coefficient map generation from multi‐parametric MR images for patients with diffuse gliomas
We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.
Authors
- Jacob Wynne
- Hui‐Kuo G. Shu (ORCID: https://orcid.org/0000-0002-4060-0874)
- Richard L. J. Qiu (ORCID: https://orcid.org/0000-0002-7877-1900)
- Tonghe Wang (ORCID: https://orcid.org/0000-0001-9021-1204)
- Hui Mao (ORCID: https://orcid.org/0000-0002-0147-6022)
- Zach Eidex (ORCID: https://orcid.org/0009-0003-3603-0711)
- David Viar‐Hernandez (ORCID: https://orcid.org/0000-0002-1938-2980)
- Xiaofeng Yang (ORCID: https://orcid.org/0000-0001-9023-5855)
- Mojtaba Safari
Institutions
- Georgia Institute of Technology (US)
- Memorial Sloan Kettering Cancer Center (US)
- Emory University (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1002/mp.17509
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Institutes of Health