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Ultra-Low Dose PET Imaging (UDPET) Challenge 2024

Dear participants of the Ultra-Low Dose PET Imaging (UDPET) Challenge,

We would like to express our sincere gratitude for your interest and valuable contributions to the UDPET imaging challenge over the past two years. We are thrilled to announce the continuation of the challenge with UDPET 2024. This year’s challenge will incorporate a broader and more diverse dataset acquired on two commercial Total-Body PET systems, uExplorer (United Imaging) and Biograph  Vision Quadra (Siemens Healthineers), to solidify the trust in methodological developments and enhance the clinical translational potential of the outcomes.

A comprehensive workshop will be held at IEEE MIC 2024 on October 29th. Participants ranked within the top 10 will be invited to present their work at the IEEE MIC 2024 Ultra-low-dose PET imaging workshop, which can be found at the following link: https://nssmic.ieee.org/2024/program/.

 Please take note of the important dates for the challenge:

  Availability of the test dataset and evaluation code: Aug. 1st

  Pre-register via email to christoph.clement@students.unibe.ch to confirm a submission: Oct. 1st (DDL_1)

  Final submission deadline:  Oct. 8 (DDL_2)

  Notification of rankings and invitations will be sent out on: Oct. 15th

 We will provide updates and announcements regarding the challenge on our website: https://ultra-low-dose-pet.grand-challenge.org/

 To ensure successful participation and eligibility for prizes, all participants must adhere to the following requirements:

  Pre-register via Email before DDL_1

  Submit the necessary material before the DDL_2 of 23:59 (pacific day time)

  • A short paper or abstract about your method
  • Algorithms scripts or link to your Github repository
  • Generated Full dose image, recovered from dose reduced images in Test dataset

Should you have any questions or require further clarification, please do not hesitate to contact us. We wish you the best of luck in the UDPET challenge.

Best regards,

Kuangyu Shi, Rui Guo, Christoph Clement, Hanzhong Wang, Axel Rominger and Biao Li