realtimeDeeds

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example code for TBME Publication "Model-based Sparse-to-dense Image Registration for Realtime Respiratory Motion Estimation in Image-guided Interventions"

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# realtimeDeeds
**Code**
Example code for TBME Publication *"Model-based Sparse-to-dense Image Registration for Realtime Respiratory Motion Estimation in Image-guided Interventions"*.

**Datasets**
Datasets used in the experments: 
- *2D MRI dataset [1]* used in the code can be found under: https://zenodo.org/record/55345#.WvLINd8xBhF 
 (additional files for example code can be downloaded <a href="https://www.dropbox.com/sh/pibkzr8poz8lyau/AADRrlzczs6S2xg66_5fqQmBa?dl=0">here</a>)
- *3D MRI dataset [2]* and *3D US dataset [3]* can be found under:
 [2] http://www.vision.ee.ethz.ch/en/datasets/ (4D MRI lung data) 
 [3] http://clust.ethz.ch/data.html

*[1]: CF Baumgartner, C Kolbitsch, JR McClelland, D Rueckert, AP King, Autoadaptive motion modelling for MR-based respiratory motion estimation, Medical Image Analysis (2016), http://dx.doi.org/10.1016/j.media.2016.06.005* 
*[2]: Boye, D. et al. - Population based modeling of respiratory lung motion and prediction from partial information - Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690U (March 13, 2013); doi:10.1117/12.2007076.*

Landmarks
Manually selected landmarks for 2D/3D MRI and 3D US dataset are available as .txt (for 2D MRI and 3D US) and .m (for 3D MRI) files. 
- 2D MRI: each txt-file contains landmark coordinates for one frame.
- 3D MRI: by executing MATLAB script file, 3 matrices (refLM, LM01px/LM04px, frames) are generated. 
- 3D US: each txt-file contains landmark coordinates for all frames with frame numbers in the first column.

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