CCP SyneRBI

Collaborative Computational Project in Synergistic Reconstruction for Biomedical Imaging

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Palak has won joint 3rd place in this year's CoSeC Impact award

Palak has won joint 3rd place in this year's CoSeC Impact award with a case study on her work on TOF-PET and KEM for PET/MR. CoSeC (Computational Science Centre for Research Communities) is the organisation that supports CCP SyneRBI, CCPi (and other CCPs/HECs), and therefore SIRF and associated projects including STIR.

You can find the case studies on the CoSeC website at SCD CoSeC Impact Award 2021: Case Studies (stfc.ac.uk)

Hackathon on algorithms benchmark for medical imaging reconstruction

PET++, CCP SyneRBI and CCPi jointly organize two hackathons on algorithms benchmark for medical imaging reconstruction. The first one will take place on November 2021 and the second one in January 2022. The goal of the hackathons is to establish a benchmark between the numerous iterative algorithms for CT and PET reconstructions which have been proposed in the recent years, with a focus on randomized algorithms.

More info at https://petpp.github.io/hackathon.html

Release of SIRF 3.1.0

We are delighted to announce the release of our Synergistic Image Reconstruction Framework (SIRF) and associated projects Version 3.1. This release adds a few new features and quite a lot of new notebooks in SIRF-Exercises. It is currently in heavy use in our Training School on Synergistic Image Reconstruction, jointly organised with CCPi.

The main changes of the 3.1 release are:

SIRF v3.1.0:

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Release of SIRF 3.0.0

We are delighted to announce the release of our Synergistic Image Reconstruction Framework (SIRF) Version 3.0.0.

The main changes of the 3.0.0 release are:

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Royal Society Theme issue Synergistic tomographic image reconstruction: part 1

Royal Society Theme issue Synergistic tomographic image reconstruction: part 1

Compiled and edited by Charalampos Tsoumpas, Jakob Sauer Jørgensen, Christoph Kolbitsch and Kris Thielemans.

Traditionally, tomographic image reconstruction has focused on estimating images from a single modality and acquisition. This theme issue focuses on synergistic image reconstruction which aims to utilise both similarities and complementary information between different data to make the synergistic combination of data offer more than the sum of its parts.

New publication in collaboration with our colleagues from Sydney etc. on the use of synergistic reconstruction (HKEM) for SPECT using PET side information

Marquis, H., D. Deidda, A. Gillman, K. P. Willowson, Y. Gholami, T. Hioki, E. Eslick, K. Thielemans, and D. L. Bailey. ‘Theranostic SPECT Reconstruction for Improved Resolution: Application to Radionuclide Therapy Dosimetry’. EJNMMI Physics 8, no. 1 (17 February 2021): 16. https://doi.org/10.1186/s40658-021-00362-x.

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New publication in collaboration with our colleagues from UCL etc. on estimating uncertainty in various stages of the processing pipeline for neuro-PET

Markiewicz, Pawel J., Julian C. Matthews, John Ashburner, David M. Cash, David L. Thomas, Enrico De Vita, Anna Barnes, et al. ‘Uncertainty Analysis of MR-PET Image Registration for Precision Neuro-PET Imaging.’ NeuroImage, 12 February 2021, 117821. https://doi.org/10.1016/j.neuroimage.2021.117821.

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CCP SyneRBI Awards 2021

CCPSyneRBI gives 3 awards annually to the top 3 contributors (early career researchers) to the project, whether for contributions in software, education or administrative matters.

We are delighted to announce that Year 2021 awards go to:

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