BIC-MAC: Big Cross-Modal Attenuation Correction Challenge | Zenodo Skip to main Communities My dashboard Log in Sign up There is a newer version of the record available. Published April 24, 2026 | Version v1 Other Open BIC-MAC: Big Cross-Modal Attenuation Correction Challenge Authors/Creators Hinge, Christian 1 Ladefoged, Claes Nøhr 2, 3 Andersen, Flemming Littrup 2 Schramm, Georg 4 Korsholm, Kirsten 2 Law, Ian 2 Show affiliations 1. University of Copenhagen 2.
Rigshospitalet, Department of Clinical Physiology and Nuclear Medicine 3.
Technical University of Denmark Department of Mathematical Modelling and Computation 4. KU Leuven Description The advent of Long Axial Field-of-View (LAFOV) PET scanners has shifted the dosimetry paradigm in PET/CT imaging. The high sensitivity of these systems allows for substantial reductions in radiotracer activity, rendering the volumetric CT component the dominant source of ionizing radiation [1]. For dose-sensitive populations such as pediatric and obstetric cohorts, eliminating the volumetric CT entirely is highly desirable [2], [3]. However, the CT serves a dual purpose: providing anatomical context and enabling attenuation correction (AC) for PET reconstruction, as the attenuation map is typically derived directly from the CT [4]. Similarly, whole-body studies acquired on PET/MRI systems require estimation of the attenuation map from MR images [5]. In both scenarios, the absence of a CT poses a reconstruction challenge. To address this, the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge tasks participants with synthesizing a 3D pseudo-CT from other available modalities. We present a novel multimodal dataset comprising whole-body PET, CT, Topogram (scout radiograph), and MRI for 99 healthy volunteers. The cohort is age- and sex-stratified, with data acquired on Siemens Biograph Vision Quadra and Siemens MAGNETOM Vida scanners. Participants will receive a training set of 75 cases containing Non-Attenuation Corrected (NAC) [18F]FDG PET images, scan-planning Topograms, and same-day DIXON MRI, alongside reference CT and CT-based attenuation-corrected PET (CTAC-PET) images. Critically, we also provide scatter maps, sinograms, and Docker containers with open-source reconstruction software, enabling closed-loop optimization on the training set - a capability previously restricted to hospital sites with access to proprietary vendor software [6]. The challenge comprises a single task: generate a pseudo-CT from the available input modalities. The pseudo-CT will be used to reconstruct PET images, which are then quantitatively compared against reference CTAC-PET images. Both static and dynamic PET reconstructions are evaluated to assess downstream accuracy across different clinical contexts. A defining technical characteristic of this challenge is the integration of modalities with different dimensionalities and acquisition geometries. While the 3D NAC-PET and 2D Topograms are spatially aligned with the target attenuation map, both lack anatomical detail. In contrast, whole-body MRI offers high bone and soft-tissue contrast but is acquired in a different scanner geometry with different patient positioning and body deformations. Consequently, participants must develop algorithms capable of fusing spatially unaligned information from 3D volumetric MRI with that of the 3D NAC-PET and 2D Topograms. Files 303-Big_Cross-Modal_Attenuation_Correction_Challenge_2026-04-22T16-37-06.pdf Files (128.8 kB) Name Size Download all 303-Big_Cross-Modal_Attenuation_Correction_Challenge_2026-04-22T16-37-06.pdf md5:27efab2858db22cbf59b4291e00704af 128.8 kB Preview Download 380 Views 480 Downloads Show more details All versions This version Views Total views 380 364 Downloads Total downloads 480 473 Data volume Total data volume 67.1 MB 66.2 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Attenuation Correction Cross-Modal Synthesis Registration Multimodal PET MRI Whole-body [18F]FDG CT MICCAI 2026 challenge Details DOI DOI Badge DOI 10.5281/zenodo.19731820 Markdown [](https://doi.org/10.5281/zenodo.19731820) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19731820.svg :target: https://doi.org/10.5281/zenodo.19731820 HTML <a href="https://doi.org/10.5281/zenodo.19731820"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19731820.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19731820.svg Target URL https://doi.org/10.5281/zenodo.19731820 Resource type Other Publisher Zenodo Conference International Conference on Medical Image Computing and Computer Assisted Intervention 2026 (MICCAI) Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Citation Export Technical metadata Created April 24, 2026 Modified April 24, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies