Synthetic X-Q space learning for diffusion MRI parameter estimation: a pilot study in breast DKI
The basic characteristics of synXQSL were investigated by using various types of datasets. The results indicate that synXQSL with the appropriate choice of bases in training data synthesis has the potential to improve dMRI parameters in noisy datasets.
Authors
- Hideki Ota (ORCID: https://orcid.org/0000-0002-7239-3152)
- Shunji Mugikura (ORCID: https://orcid.org/0000-0002-9138-610X)
- Naoko Mori (ORCID: https://orcid.org/0000-0002-8700-9731)
- Yoshitaka Masutani (ORCID: https://orcid.org/0000-0001-8505-673X)
- Yuki Ichinoseki
- Kei Takase (ORCID: https://orcid.org/0000-0003-0931-9942)
- Erina Kato
- Kousei Konya
Institutions
- Tohoku University (JP)
- Akita University (JP)
- Tohoku University Hospital (JP)
- Tohoku Medical Megabank Organization (JP)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1007/s11548-025-03550-7
- Primary Topic
- Advanced Neuroimaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00