Using Training Samples as Transitive Information Bridges in Predicted 4D MRI
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44917-8_23
Reference15 articles.
1. Cai, J., Chang, Z., Wang, Z., Paul Segars, W., Yin, F.F.: Four-dimensional magnetic resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study. Med. Phys. 38(12), 6384–6394 (2011)
2. Gulamhussene, G., Joeres, F., Rak, M., Lübeck, C., Pech, M., Hansen, C.: 2D MRI liver slices with navigator frames. a test data set for image based 4D MRI reconstruction (2019). https://doi.org/10.24352/UB.OVGU-2019-093
3. Gulamhussene, G., Joeres, F., Rak, M., Pech, M., Hansen, C.: 4D MRI: robust sorting of free breathing MRI slices for use in interventional settings. PLoS ONE 15(6), e0235175 (2020)
4. Gulamhussene, G., et al.: Predicting 4D liver MRI for MR-guided interventions. Comput. Med. Imaging Graph., 102122 (2022). https://doi.org/10.1016/j.compmedimag.2022.102122, https://www.sciencedirect.com/science/article/pii/S0895611122000921
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