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AI models tackle PET attenuation correction challenge · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two research papers detail approaches to the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge, focusing on synthesizing pseudo-CT images from PET, MRI, and topogram data. The first paper highlights the importance of region-weighted loss functions and model fusion, achieving top performance by combining two independently trained models. The second paper introduces a multimodal 3D U-Net with separate PET and MR encoders and FiLM-based topogram conditioning, emphasizing reduced reliance on precise voxel-wise correspondence. AI

IMPACT Advances in multimodal AI for medical imaging could improve diagnostic accuracy and treatment planning.

RANK_REASON Two academic papers published on arXiv detailing methods for a specific challenge in medical imaging AI.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI models tackle PET attenuation correction challenge · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Khoa Tuan Nguyen, Joris Vankerschaver, Wesley De Neve ·

    Region-Weighted Losses and Model Fusion for Cross-Modal PET Attenuation Correction

    arXiv:2608.21881v1 Announce Type: new Abstract: We describe our approach to the Big Cross-Modal Attenuation Correction (BIC-MAC) challenge, which asks for a pseudo-CT in Hounsfield Units to be synthesized from Non-Attenuation-Corrected PET (NAC-PET), DIXON MRI and a topogram, and…

  2. arXiv cs.CV TIER_1 English(EN) · 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

    arXiv:2608.21481v1 Announce Type: cross 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 topogr…

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