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AI model enhances CT-less PET attenuation correction for medical imaging

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach for CT-less PET attenuation correction by synthesizing multimodal pseudo-CT images. Their method, submitted to the BIC-MAC 2026 Challenge, utilizes a modified nnU-Net architecture incorporating both anatomical and physical supervision. The system leverages a frozen TotalSegmentator feature extractor for anatomical guidance and a differentiable projection loss for physical supervision, building upon weights pre-trained on the SynthRAD Challenge dataset. AI

IMPACT This research could improve the accuracy and efficiency of PET scans by enabling CT-less attenuation correction, potentially leading to better diagnoses and treatment planning.

RANK_REASON The item describes a research paper submitted to a challenge, detailing a novel method for medical image processing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model enhances CT-less PET attenuation correction for medical imaging

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The item describes a research paper submitted to a challenge, detailing a novel method for medical image processing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Petros Chatzitoulousis, George K. Matsopoulos ·

    Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge

    arXiv:2608.15721v1 Announce Type: new Abstract: This report describes our submission to the Big Cross-Modal Attenuation Correction (BIC-MAC) 2026 Challenge for CT-less PET attenuation correction through multimodal pseudo-CT synthesis. We build upon a standard nnU-Net architecture…

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