---
# System prepended metadata

title: Medical Imaging Software

---

# SIAM-IS Minisymposium

## Title
Open Source Software Solutions for Imaging Inverse Problems


## Abstract
<!-- This minisymposium aims to promote Open Source Software (OSS) for Imaging Inverse Problems. OSS  -->
Open source software has had a profound impact on both industry and academia over the last 50 years, revolutionising the way we develop, share, and collaborate on software solutions.
In the sphere of inverse problems, it has allowed access to many modalities of practical importance, such as in medical imaging (MRI, CT, PET, etc.), seismic imaging, materials science, and many more.
This has democratised the access to realistic imaging frameworks, 
paving the way for the development of reproducible and reliable methods in these critical research domains.
The aim of this minisymposium is to provide a venue to disseminate recent open source software solutions for imaging inverse problems, foster collaborations, and contribute to the ongoing efforts to make imaging research more accessible and reproducible.



## Email Template:

Dear Prof Fessler

I hope you are doing well. 
We are planning to organise a minisymposium entitled "Open Source Software Solutions for Imaging Inverse Problems”
at the SIAM conference on Imaging Sciences  (https://www.siam.org/conferences/cm/conference/is24), which will take place from May 28-31, 2024 in Atlanta, Georgia, USA. 
Please find a tentative abstract at the end of this email.

Given your expertise and contributions in this field, we would be delighted if you could present a talk at our mini-symposium. Your work on MIRT would be particularly interesting and befitting, but please feel free to suggest a different topic.

Please note that we cannot cover any costs related to the participation in this conference. We hope you will be able to join us and would greatly appreaciate if you could provide a response soon (by 20 October). 
If you are interested in participating, we kindly ask for a tentative title for your talk. 
Alternatively, if you are unavailable but can recommend a researcher or colleague who would be a valuable addition to our minisymposium, please let us know.
Note that the conference allows **only one talk per speaker**.

Should you have any comments or questions please don’t hesitate to reach out to us.

Kind regards,
Vaggelis, Zeljko

## Abstract
Open source software has had a profound impact on both industry and academia over the last 50 years, revolutionising the way we develop, share, and collaborate on software solutions.
In the sphere of inverse problems, it has allowed access to many modalities of practical importance, such as in medical imaging (MRI, CT, PET, etc.), seismic imaging, materials science, and many more.
This has democratised the access to realistic imaging frameworks, 
paving the way for the development of reproducible and reliable methods in  critical research domains.
The aim of this minisymposium is to provide a venue to disseminate recent open source software solutions for imaging inverse problems, foster collaborations, and contribute to the ongoing efforts to make imaging research more accessible and reproducible.







<!-- In Imaging Inverse Problems, it allows access to cutting-edge algorithms and tools applied on imaging datasets, ideally open sourced.  -->
<!-- 
In Imaging Inverse Problems, it allows the scientific community to have access to cutting-edge algorithms and tools applied on imaging datasets, ideally open sourced. 
This accessibility not only democratizes the field but also fosters collaboration among experts from diverse backgrounds, leading to innovative approaches and solutions.  -->
<!-- Additionally, this event places a strong emphasis on reproducible research, encouraging participants to adhere to open data practices and sharing their methodologies openly, e.g., code, data, and computational environment.  -->
<!-- This ensures transparency and reliability of research findings, further enhancing the credibility of advancements made in various imaging applications, including conventional image processing, medical imaging, materials science, seismic imaging, and many more. 
We hope that our session will offer participants an opportunity to learn about open source software for imaging inverse problems, foster meaningful collaborations, and contribute to the ongoing efforts to make imaging research more accessible and reproducible.
 -->

## Invited speakers 
- [x] (Accepted) [MIRT](https://github.com/JeffFessler/MIRT.jl)  (Jeff Fessler, Julia), email: fessler@umich.edu 
- [x] (Accepted) [Deepinv](https://github.com/deepinv/deepinv) (Julian Tachella, Python), email: julian.tachella@cnrs.fr
- [x] [Accepted (SCICO)](https://github.com/lanl/scico) (Brendt Wohlberg, Python) - email: brendt@ieee.org (not sure...)
- [x] ~~[Final, Cannot attend](https://pylops.readthedocs.io/en/stable/) (Matteo Ravasi, Python) - email: matteo.ravasi@kaust.edu.sa~~
- [x] [GlobalBioIM, Waiting for coauthor/codev](https://biomedical-imaging-group.github.io/GlobalBioIm/) (Emmanuel Soubies, Matlab) - emmanuel.soubies@cnrs.fr
- [x] (Accepted) [CuQiPy](https://cuqi-dtu.github.io/CUQIpy/#) (Jakob Jorgensen, Python) - email: jakj@dtu.dk
- [ ] CIL (Vaggelis?)
- [x] SIRF (Kris? but probably would need someone else)
- [x] ~~[pyEIT, cannot](https://github.com/eitcom/pyEIT) Feng Fu email: fengfu@fmmu.edu.cn , send to Benyuan Liu from github repo~~
- [x] [Accepted](https://github.com/ucl-bug/jwave), email: stanziola.antonio@gmail.com
- [ ] [Tomosipo](https://opg.optica.org/oe/fulltext.cfm?uri=oe-29-24-40494&id=464992)
- [ ] [Omega](https://github.com/villekf/OMEGA)
- [x] [Waiting for reply] Ander Biguri

Accepted
1. [CuQiPy](https://cuqi-dtu.github.io/CUQIpy/#) (Jakob Jorgensen, Python) **Title** *CUQIpy: Computational Uncertainty Quantification for Inverse Problems in Python*
2. [MIRT](https://github.com/JeffFessler/MIRT.jl)  (Jeffrey A Fessler, Julia) **Title** *JuliaImageRecon: efficient, reproducible and open-source image reconstruction*
3. [Deepinv](https://github.com/deepinv/deepinv) (DongDong Chen, Python) **Title** *Deep Inverse: a PyTorch library for solving inverse problems with deep learning*
4. [jWave] Antonio Stanziola **Title** *TBD*
5. [SIRF/STIR] Kris Thielemans **Title** *TBD*
6. CIL (Vaggelis)
7. SCICO (Michael Thompson colab of Brendt Wohlberg)


Other modalities? 
Check/confirm that the above are used enough



## For the Mini sub:

1) CuQiPy, Jakob Jorgensen, Technical University of Denmark , CUQIpy: Computational Uncertainty Quantification for Inverse Problems in Python
2) MIRT, Jeffrey A Fessler, Univ. of Michigan, JuliaImageRecon: efficient, reproducible and open-source image reconstruction
3) Deepinv, DongDong Chen, Heriot-Watt University, Deep Inverse: a PyTorch library for solving inverse problems with deep learning
4) jWave, Antonio Stanziola, UCL, TBD
5) CIL, Evangelos Papoutsellis, Finden Ltd, TBD
6) SIRF, Kris Thielemans, UCL, TBD
7) SCICO, Cristina Garcia-Cardona, Los Alamos National Laboratory, Computational Imaging with SCICO
8) 

