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#artificial intelligence Preprint Aug 2026

Physics-Guided Flow Matching for CT Image Reconstruction

Experimental results across several CT inverse problem settings show that Flow Matching-based approaches consistently outperform diffusion-based methods in terms of PSNR, SSIM, and perceptual quality, while requiring fewer sampling steps.

Davide Evangelista · 0 citations
Open access Aug 2026

Memory-efficient image reconstruction using diffusion models for accelerated 3D non-Cartesian UTE imaging.

PURPOSE Accelerated 3D non-Cartesian MRI presents unique challenges in balancing high-resolution reconstruction with computational and memory constraints. In this work, a novel, memory-efficient image reconstruction framework using score-based diffusion models tailored for highly undersampled 3D radial UTE acquisitions...

Jonas Petersen, Stefan Sommer, Thomas Küstner · 0 citations
Sep 2026

LDPM-v2: Towards undersampled MRI reconstruction with multimodal one-step latent diffusion prior.

This approach optimizes the text-to-image diffusion priors via a rectified flow strategy and an MRI-tailored variational autoencoder, and further strengthens control over the restoration process using multimodal guidance, enabling high-fidelity, one-step reconstruction.

Jing-Wei Guan, Xing-Jian Tang, Lin-Ge Li et al. · 0 citations
Aug 2026

OA-Recon: An Online Adaptive Reconstruction Framework for Sparse-View Multi-Energy CT.

Multi-energy CT (MECT) offers unique advantages in material decomposition, tissue characterization, and functional imaging, positioning it as a pivotal direction for next-generation CT. Currently, standardized scanning protocols for MECT have not yet been established. Considering growing public concern over X-ray radia...

Xi Wang, Tong Lin, Jiashun Wang et al. · 0 citations
Preprint Sep 2026

Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior

Multi-contrast MR scans contain redundant structural information that can be leveraged during reconstruction and potentially accelerate acquisition times. This idea has inspired end-to-end guided reconstruction models, leveraging one or more contrasts to guide the reconstruction of a different contrast. However, these...

Chin-May Rao, E. Ilıcak, M. V. van Osch et al. · 0 citations
#machine learning Preprint Sep 2026

CLEAR: Complex Learned Explicit Analytical Regularization for Ultra-Accelerated 4D Flow CMR Reconstruction

While compressed-sensing regularizers enable interpretable reconstruction of 4D Flow CMR through transparent variational objectives, their hand-crafted nature is too restrictive under high acceleration. State-of-the-art learning-based approaches mitigate this, but typically encode regularization implicitly through unro...

German Shâma Wache, Sebastian Neumayer · 0 citations

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