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.
arXiv:2609.27848v1 Announce Type: new Abstract: The BIC-MAC challenge targets whole-body pseudo-CT synthesis from NAC-PET, Dixon MRI, and a 2D topogram for CT-less PET attenuation correction. We prop...
The paper presents a method for the BIC-MAC challenge, aiming to generate pseudo‑CT images from NAC‑PET, DIXON MRI, and a topogram, and to evaluate both the pseudo‑CT and the resulting attenuation‑corrected PET. The authors improve upon a 3D U‑Net baseline by focusing on loss design: they compute an L1 error in the Carney attenuation‑coefficient space, weighted by anatomical region, and incorporate DIXON MRI as additional input only after this loss was applied. Finally, they fuse two independently trained models via a fixed convex combination, achieving better performance than either model alone and topping the public validation leaderboard.
arXiv:2606. 13341v1 Announce Type: cross Abstract: We present a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) for multimodal CT-PET image synthesis.
The paper introduces DB‑SUiT, a surface‑based diffusion bridge that translates cortical MRI to PET images directly on the cortical manifold. It employs a conditional spherical U‑shaped vision Transformer to capture multi‑scale surface features and long‑range dependencies while incorporating demographic and subcortical information. Evaluations on two dementia datasets show that the synthesized PET surfaces outperform MRI and PET volumes in automated classification and achieve high diagnostic accuracy in a blinded reader study.
arXiv:2609.12860v1 Announce Type: new Abstract: Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and tr...
The paper introduces LowBridge, a method for cross‑modal medical image segmentation that leverages shared low‑level features such as edges between MRI and CT scans. It trains a generative model to reconstruct source‑modality images from edge maps and then trains a segmentation network on these generated images. At test time, edge features from target‑modality images are fed into the generative model to produce source‑style images, which are segmented by the pretrained network, achieving state‑of‑the‑art results on multiple public datasets.
arXiv:2505. 07687v4 Announce Type: replace-cross Abstract: Multi-modal medical image synthesis is pivotal for alleviating clinical data scarcity, yet existing methods fail to reconcile global anatomical consistency with high-fidelity local detail.
arXiv:2603.23295v2 Announce Type: replace Abstract: Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imagin...
The paper introduces a one‑pass conditional 3D rectified flow (3D Flow) framework for denoising whole‑body PET images, employing an optimized non‑uniform sampling strategy and a linear‑interpolant velocity‑matching objective. It reconstructs a full 3D volume in about 30 seconds, dramatically faster than multi‑hour 3D diffusion models, while maintaining high global image quality and lesion conspicuity even at ultra‑low doses (down to 1/100 of standard). Zero‑shot transfer tests on independent clinical data demonstrate robust performance across datasets and unseen dose levels.
arXiv:2610.23325v1 Announce Type: cross Abstract: This study investigates CT-to-PET image synthesis using TransNSNet, a Transformer-enhanced multi-scale adversarial framework. A paired CT-PET dataset...
arXiv:2609.08627v1 Announce Type: cross Abstract: In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, c...
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited ava...