@inproceedings{discovery1501088,
          volume = {9901},
         address = {Cham, Switzerland},
            note = {Copyright {\copyright} Springer International Publishing AG 2016.
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46723-8\_63},
           pages = {547--555},
       booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
          editor = {S Ourselin and L Joskowicz and M Sabuncu and G Unal and W Wells},
          series = {Lecture Notes in Computer Science},
           month = {October},
       publisher = {Springer},
            year = {2016},
           title = {Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning},
         journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
            issn = {1611-3349},
        abstract = {Accurate knowledge of organ location and tissue attenuation properties are the two essential components to perform radiotherapy treatment planning (RTP). Computed tomography (CT) has been the modality of choice for RTP as it easily provides electron density information. However, its low soft tissue contrast limits the accuracy of organ delineation. On the contrary, magnetic resonance (MR) provides images with excellent soft tissue contrast but its use for RTP is limited by the fact that it does not readily provide tissue attenuation information.

In this work we propose a multi-atlas information propagation scheme that jointly segments the organs at risk and generates pseudo CT data from MR images. We demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, bypassing the need for CT scan for accurate RTP.},
          author = {Burgos, N and Guerreiro, F and McClelland, J and Nill, S and Dearnaley, D and Desouza, N and Oelfke, U and Knopf, AC and Ourselin, S and Cardoso, MJ},
             url = {https://doi.org/10.1007/978-3-319-46723-8\%5f63}
}