Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning
Rory Bell, Artemis Bouzaki, Jiaming Cao, Jasmine Morrison, Chelsea Sargeant
Abstract
We participated in the BIC-MAC Challenge with a multimodal 3D patch-based U-Net for pseudo-CT generation from NAC-PET, MRI, and 2D topograms. By using separate PET and MR encoders, multi-scale feature fusion, and FiLM-based topogram conditioning at the bottleneck, we obtain a model that integrates complementary cross-modal information while reducing reliance on precise voxel-wise correspondence between modalities. Our final submission can be found: https://github.com/rrr-uom-projects/BIC-MAC-MICCAI2026
Create a lesson
Related papers
Highly accelerated 3D Cartesian MPnRAGE with implicit neural representation reconstruction
Natascha Niessen, Ana Beatriz Solana, Carolin M. Pirkl et al.
Informed Sinogram Interpolation for Sparse View Reconstruction
Yuejie Liu, Alessandro Lupoli, David Uribe Gallo et al.
Constrained Color Carrier: Characterization-Preserving Conditional Color Rendering in Multi-Illuminant Camera Profiles
Xilai Liang
Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting
Shiwen An, Konstantinos Slavakis
StainBridge: Stain-Aware Pairwise Registration of Serial Renal Biopsy Whole-Slide Images Across Structural and Immunohistochemical Stains
Ellen Wei, Bohang Jiang, Yanfan Zhu et al.
Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission
Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du et al.