This two-day symposium on March 9-10 2026, featuring invited internationally-leading researchers covering recent advances in AI and image reconstruction for biomedical imaging, is intended to enhance UK and international networking, research progress and education in this important area. The symposium is part of the EPSRC funded computational collaborative project in synergistic reconstruction in biomedical imaging (SyneRBI).
We have an exciting line up of invited speakers attending from across the globe, including: Charles Bouman (Purdue), Alexandre Bousse (Brest), Hyungjin Chung (EverEx), Joyita Dutta (UMass), Florian Knoll (FAU Erlangen), Erich Kobler (Linz), Abolfazl Mehranian (GE Healthcare), Jae Sung Lee (Seoul), Jinyi Qi (UC Davis), Julia Schnabel (TU Munich), Carola Schönlieb (Cambridge) Julian Tachella (CNRS, Lyon), Andrew Wang (Blur Labs), Guobao Wang (UC Davis)
Organising committee
A. J. Reader (King’s College London), D. Atkinson (University College London), M. Ehrhardt (University of Bath), C. Kolbitsch (Physikalisch-Technische Bundesanstalt), J. C. Matthews (University of Manchester), E. Pasca (Science & Technology Facilities Council), A. Tavares (University of Edinburg), K. Thielemans (University College London), C. Tsoumpas (University of Groningen) .
Location
Programme
Confirmed Invited Speakers
| Guobao Wang | Bridging Physics and Learning: A Decade of Kernel Methods for Image Reconstruction |
| Jinyi Qi | Image reconstruction for positronium lifetime tomography |
| Erich Kobler | DEALing with Image Reconstruction: Deep Attentive Least Squares |
| Florian Knoll | Exploring the limitations of deep learning for MR image reconstruction |
| Alexandre Bousse | Unsupervised machine and deep learning in CT and PET/CT: reconstruction and generative models |
| Carola Schonlieb | TBC |
| Charles Bouman | Generative plug-and-play for medical image reconstruction |
| Georg Schramm | DL for list-mode EM recon |
| Abolfazl Mehranian | PET and SPECT AI aided reconstruction solutions: perspectives from industry |
| Julia Schnabel | Self-supervised motion reconstruction for precision MR(A)I |
| Hyungjin Chung | Test-time adaptation in diffusion models for medical image reconstruction |
| Jae Sung Lee | The evolving roles and perspective of AI in nuclear medicine |
| Joyita Dutta | AI for post-reconstruction medical image enhancement |
Confirmed Contributing Speakers
| Corentin Constanza | Unrolled MAPEM for SPECT reconstruction |
| Zekai Li | Hybrid kernelised PET reconstruction allowing for negative values |
| Natascha Niessen | INR meets Multi-Contrast MRI Reconstruction |
| Evangelos Papoutsellis | Why do we regularise in every iteration for imaging inverse problems? |
| Alessandro Perelli | Stochastic Multiresolution Image Sketching for Inverse Imaging Problems |
| Daniel Sanderson | Decomposed Diffusion Reconstruction for Multiple Artifact Correction in Computed Tomography Imaging |
| Jelmer van Lune | Physics-Guided Reconstruction of Quantitative MRI from Conventional Contrasts by Self-Supervised Deep Learning |
| Zi Wang | Physics-Informed Synthetic Data Learning: Boosting Multi-Scenario Fast MRI Reconstruction with Only One Model |
Submissions
Submissions are now closed. Please note that papers presented at the symposium could be submitted to the Frontiers in Nuclear Medicine, research topic: Rapid Image Reconstruction. A number of waivers are available for the article processing costs.
Registration
Registration is mandatory but free (spaces are limited). Funding towards travel and accommodation might be available for early-career researchers- please request this when registering via the link above.
In-person attendance is preferred and encouraged, but this will be a hybrid event to broaden access.
Satellite Hackathons
Associated to the AI-RBI symposium, there will be two hackathons, requiring separate registration. Please check their dedicated web-pages for more information:
- STIR Hackathon 2026, 5-6 Mar 2026.
- Hackathon on software for machine learning approaches in biomedical imaging, 11-12 March.
Further Information
Please check the current page for latest information.