# Supporting Material

Below is a set of links to recorded lectures or reading material which we hope will be useful for course participants. Please note that we will not cover this material ourselves (except very briefly).

## Essential

* [Fully3D 2021 - SIRF/CIL training school - 03 PET intro](https://youtu.be/-67l4wp1R_A)
**Kris Thielemans**. *SIRF-CIL Training School*
*Content*: Intro to PET: Application, Physics, Events, data storage, data model, SIRF software architecture, STIR software. [19 mins]
* [Fully3D 2021 - SIRF/CIL training school - 04 Iterative image reconstruction - basics](https://youtu.be/KSiYYdyn8Sc)
**Andrew Reader**. *The Why and How of iterative image reconstruction*
*Content*: Least squares, maximum likelihood, Poisson noise, regularisation for noise and for missing data. [43 mins]

For project specific preparation, please refer to the project pages:
* [Project 1 - Write your own PET or SPECT reconstruction algorithm](/j4iwLfBHRhKSl7fVpjqWYA)
* [Project 2 - Synergistic PET and SPECT reconstruction](/I42iDUWHRfi0fRnHVUudwA)
* [Project 3 - TotalBody PET reconstruction](/AGl8BlxSRVWUhFuNZBY_RA)
* [Project 4 - Image reconstruction using Deep Learning](/4bW24YXTSJ-zwJ_XdMn0wA)

## Optional
### Software and programming
- If you are new to Python and the Unix terminal, please check the excellent material on https://software-carpentry.org/lessons/. There are some additional links in the [Appendix of our 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 the CCP 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.
- [SIRF/CIL Docker on Mac video](https://mediacentral.ucl.ac.uk/Player/C1eAhHAi) and [PowerPoint](https://mediacentral.ucl.ac.uk/assoc_files/C1eAhHAi_0.pptx?token=16abhFB8)
- 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 and motion correction" paper:
Brown, Richard, Christoph Kolbitsch, Claire Delplancke, Evangelos Papoutsellis, Johannes Mayer, Evgueni Ovtchinnikov, Edoardo Pasca, et al. ‘Motion Estimation and Correction for Simultaneous PET/MR Using SIRF and CIL’. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (23 August 2021): 20200208. https://doi.org/10.1098/rsta.2020.0208.
- Core Imaging Library - Part I: a versatile Python framework for tomographic imaging
J. S. Jørgensen, E. Ametova, G. Burca, G. Fardell, E. Papoutsellis, E. Pasca, K. Thielemans, M. Turner, R. Warr, W. R. B. Lionheart and P. J. Withers
https://doi.org/10.1098/rsta.2020.0192
- Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography
Evangelos Papoutsellis, Evelina Ametova, Claire Delplancke, Gemma Fardell, Jakob S. Jørgensen, Edoardo Pasca, Martin Turner, Ryan Warr, William R. B. Lionheart and Philip J. Withers
https://doi.org/10.1098/rsta.2020.0193

### PET
- [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

### MR
- [Basics of MRI and MR image reconstruction](https://www.youtube.com/watch?v=xCv38thzljw)
**Gastao Cruz** (CCP PETMR lecture at PSMR 2018).
Overview of the basics of MRI, from nuclear spins and magnetic fields to the sampling requirements for k-space. Also advanced topics such as reconstruction from undersampled data (GRAPPA, SENSE) are discussed.
- [MR image reconstruction using SIRF](https://www.youtube.com/watch?v=vC66LgPfRNM)
**Christoph Kolbitsch**. MR image reconstruction (k-space sampling, coil sensitivity maps, GRAPPA) and how it can be done in SIRF.
- [Advanced MR image reconstruction ](https://www.youtube.com/watch?v=C5C4juQ5-7M)
**Florian Knoll**. Advanced methods for MR image reconstruction mainly using machine learning.

### Optimisation
- [Convex Optimization Short Course](https://web.stanford.edu/~boyd/papers/cvx_short_course.html)
**S. Boyd, S. Diamond, J. Park, A. Agrawal, and J. Zhang** contains slides, jupyter notebooks and three 60-90 minute lectures.
- [General image reconstruction lecture](https://www.youtube.com/watch?v=9d6miyrBvKw)
**Carola Schoenlieb**. This lecture shows how image reconstruction can be described as an inverse problem. Different approaches to solve this problem are presented starting with variational regularisation and leading to deep learning.
- [An introduction to continuous optimization for imaging (Champolle_Pock)](https://hal.archives-ouvertes.fr/hal-01346507/document)
**Antonin Chambolle, Thomas Pock**. Acta Numerica, Cambridge University Press (CUP), 2016, Acta Numerica, 25, pp.161-319. 10.1017/S096249291600009X.
Review paper on state-of-the-art methods.
- [A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science](https://epubs.siam.org/doi/abs/10.1137/09076934X)
**Ernie Esser, Xiaoqun Zhang, and Tony F. Chan**, SIAM J. Imaging Sci., 3(4), 1015–1046
10.1137/09076934X
PGHG paper

## Videos of SIRF/CIL training school @Fully3D 2021

1. [Fully3D 2021 - SIRF/CIL training school - 01 CT intro](https://youtu.be/pYHTCOrY7YI)
**Jakob Sauer Jorgensen**. *Core Imaging Library (CIL)*
*Content*: What is CIL?, CIL module structure and contents, applications of CIL: Cone-beam laminography, Dynamic CT with directional TV, Hyperspectral neutron CT, MRI with motion compensation using SIRF and CIL, overview of training course
*Notebooks*: 01_intro_walnut_conebeam, 02_intro_sandstone_parallel_roi, 03_preprocessing, 04_FBP_CGLS_SIRT, additional_exercises_data_resources

2. [Fully3D 2021 - SIRF/CIL training school - 02 MR intro](https://youtu.be/V5GlajPJzss)
**Christoph Kolbitsch**. *Training school on image reconstruction with SIRF and CIL 2021*
*Content*: Overview, ISMRMRD data format support
*Notebooks*: a_fully_sampled, b_kspace_filter, c_coil_combination, d_undersampled_reconstruction, e_advanced_recon

3. [Fully3D 2021 - SIRF/CIL training school - 03 PET intro](https://youtu.be/-67l4wp1R_A)
**Kris Thielemans**. *SIRF-CIL Training School*
*Content*: Intro to PET: Application, Physics, Events, data storage, data model, SIRF software architecture, STIR software

4. [Fully3D 2021 - SIRF/CIL training school - 04 Iterative image reconstruction - basics](https://youtu.be/KSiYYdyn8Sc)
**Andrew Reader**. *The Why and How of iterative image reconstruction*
*Content*: Least squares, maximum likelihood, Poisson noise, regularisation for noise and for missing data

5. [Fully3D 2021 - SIRF/CIL training school - 05 Implementation of OSEM,... in SIRF](https://youtu.be/4A--H5jBVPw)
**Kris Thielemans**. *Implementation of MLEM, OSMAPOSL,… in SIRF*
*Content*: Differentiation of implementation levels: e.g. Using existing algorithms vs. Implementing algorithm "from scratch"

6. [Fully3D 2021 - SIRF/CIL training school - 06 Optimisation based reconstruction with CIL](https://youtu.be/n2py8X_WdVE)
**Jakob Sauer Jorgensen**. *Optimisation-based reconstruction with CIL*
*Content*: specifying optimisation problems and running optimisation algorithms, different regularisers, data fidelities and constraints, Priors: Tikhonov, total variation, non-smooth vs. Smooth priors, FISTA
*Notebooks*: 01_optimisation_gd_fista, 05_Laminography_with_TV

7. [Fully3D 2021 - SIRF/CIL training school - 07 Notebooks: MR-specific, week 2](https://youtu.be/18i6j477rvY)
**Christoph Kolbitsch**. *MR-specific notebooks of week 2*
*Content*: parallel imaging, GRAPPA, iterative reconstruction
*Notebooks*: c_coil_combination, d_undersampled_reconstructions, e_advanced_recon, f_create_undersampled_kspace

8. [Fully3D 2021 - SIRF/CIL training school - 08 Iterative image reconstruction - PET specific](https://youtu.be/an9Yjbusv58)
**Andrew Reader**. *Iterative Image reconstruction - PETSpecific*
*Content*: algorithms: PET-specific: Poisson log-likelihood, MLEM, OSEM, Regularization: PET quadratic prior: dePiero's update function

9. [Fully3D 2021 - SIRF/CIL training school - 09 Notebooks: PET specific, week 2](https://youtu.be/58npFJiZAfQ)
**Kris Thielemans**. *PET-specific notebooks for week 2*
*Notebooks*: ML_reconstruction, DIY_OSEM, MAPEM

10. [Fully3D 2021 - SIRF/CIL training school - 10 Guided and synergistic image reconstruction - concepts](https://youtu.be/UPMENOl1cDA)
**Andrew Reader**. *Concepts in guided reconstruction and synergistic reconstruction*
*Content*: Kernel Method (KEM),

11. [Fully3D 2021 - SIRF/CIL training school - 11 Synergistic reconstruction - approaches](https://youtu.be/Xicdg0WyekM)
**Kris Thielemans**. *Synergistic reconstruction - approaches*
*Content*: Joint Total Variation: Parallel Level Sets, Total Nuclear Variation

12. [Fully3D 2021 - SIRF/CIL training school - 12 Notebooks: Synergistic reconstruction](https://youtu.be/8fXJhIlTXRM)
**Evangelis Papoutsellis**. *SIRF-notebooks on synergistic reconstruction*
*Notebooks*: 01_Colour_Processing, 02_Dynamic_CT, 03_Hyperspectral_reconstruction

13. [Fully3D 2021 - SIRF/CIL training school - 13 Deep learning for post-recon processing](https://youtu.be/ah9YzZ2BNg8)
**Andrew Reader**. *Deep learning for post-reconstruction processing*
*Content*: Use of CNNs for PET, mapping to anatomically-guided reconstructions by a CNN

14. [Fully3D 2021 - SIRF/CIL training school - 14 Notebooks: Deep learning](https://youtu.be/honO2Goi-aI)
**Georg Schramm**. *SIRF-notebooks on Deep Learning*
*Notebooks*: 00_introduction, 01_tf_data, 02_tf_models, 03_training

# Back to main
[Main page](https://hackmd.io/oNneaDUOQNK6XdDRmooITw?view#SIRF-training-school-PSMR-TBP-2022---Main)