# Supporting Material Below is a set of links which we hope will be useful for course participants. Please note that we will not cover this material ourselves. ## Python and programming - If you are new to Python, Jupyter notebooks and/or the Unix terminal, please check the excellent material on https://software-carpentry.org/lessons/. There are some additional links in the [Appendix of the SIRF-Exercises starting guide for participants](https://github.com/SyneRBI/SIRF-Exercises/blob/master/DocForParticipants.md#appendix). - Ben Thomas gave a lecture on Object Oriented Programming for CCP SyneRBI in 2017. The associated [notebook](https://github.com/SyneRBI/SIRF-Exercises/blob/master/notebooks/Introductory/object_oriented_programming.ipynb) has links for the video recording etc. ### Software for the image reconstruction workshop We will use the Synergistic Image Reconstruction Framework (SIRF). This open source software uses Python (and C++) for image reconstruction in PET, MR and SPECT. It is designed to be simple for education, while being powerful enough for real-world application. SIRF relies on various other open source packages for much of its functionality. In particular, we will use the PET part of the SIRF Exercises during this workshop. SIRF and the exercises will be **pre-installed** for participants and accessible via the cloud. **Please check the following before the course** - [SIRF-Exercises starting guide for participants](https://github.com/SyneRBI/SIRF-Exercises/blob/master/DocForParticipants.md) - The [Technical support page](https://hackmd.io/G5ewa8WuTCGuNIexCydxqw?view). Feel free to ignore information on MR, SPECT and CT in these exercises. The following links could be useful as well: - The SIRF paper: Ovtchinnikov, Evgueni, Richard Brown, Christoph Kolbitsch, Edoardo Pasca, Casper da Costa-Luis, Ashley G. Gillman, Benjamin A. Thomas, et al. ‘SIRF: Synergistic Image Reconstruction Framework’. Computer Physics Communications 249 (1 April 2020): 107087. https://doi.org/10.1016/j.cpc.2019.107087. Link for the [accepted version](https://discovery.ucl.ac.uk/id/eprint/10087933/) - The [SIRF Wiki](https://github.com/SyneRBI/SIRF/wiki) gives links to more documentation and installation instructions for those who want to have their own copy of SIRF and the exercises (**not** needed for this course). ### PET lectures We will of course cover PET physics, image reconstruction and acquisition in this course. The following are some lectures available on the internet that cover PET image reconstruction in more detail. There is no requirement to check these lectures before the course, but they could be useful afterwards. - [PET acquisition, backprojection, sinograms](https://youtu.be/3BC0bnWobLs) Andrew Reader - [Time of Flight PET](https://kuleuven.mediaspace.kaltura.com/media/Lecture+on+time-of-flight+in+positron+emission+tomography+%28Prof.+Johan+Nuyts%29/1_zqpnc5gw) Johan Nuyts - [PET acquisition modelling](https://liveuclac-my.sharepoint.com/:v:/g/personal/rmhathi_ucl_ac_uk/ERyfdeFKaptFiVv2hbJt89ABXSU2847gnOTgIGYFVEMATA?e=RedTba), including normalisation, scatter etc Kris Thielemans - [Maximum Poisson likelihood PET reconstruction](https://youtu.be/pMx5brQe5yE) Andrew Reader - [Maximum likelihood - Expectation Maximisation](https://youtu.be/F7R1PC7Vmt0) Andrew Reader - [MAP image reconstruction](https://youtu.be/Uf-I4yaEIx8) Andrew Reader # Back to main [Main page](https://hackmd.io/_BDuHkbrSVmyZBDze9MUEg?view)