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arXiv Computer Vision By Rory Bell, Artemis Bouzaki, Jiaming Cao, Jasmine Morrison, Chelsea Sargeant

Multimodal pseudo-CT synthesis for PET attenuation correction using separate modality encoding and topogram conditioning

Read the original on arXiv Computer Vision →

The paper presents a multimodal 3D patch-based U‑Net for generating pseudo‑CT images from non‑attenuation‑corrected PET (NAC‑PET), MRI, and 2D topograms. It employs separate PET and MR encoders, multi‑scale feature fusion, and FiLM‑based topogram conditioning at the bottleneck to integrate complementary cross‑modal information while reducing dependence on precise voxel‑wise correspondence. The authors participated in the BIC‑MAC Challenge and released their final model on GitHub.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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