ArticleMagma (New York, N.Y.)2026
Memory-efficient image reconstruction using diffusion models for accelerated 3D non-Cartesian UTE imaging.
Article in Magma (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeAccelerated 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 is proposed.
methodsThe method leverages a volumetric extension of the NCSN++ architecture with efficient NUFFT operations and integrates a conjugate gradient-based data consistency mechanism within a predictor-corrector sampling scheme.
resultsThis approach enables robust and high-fidelity reconstructions from as few as 4000 spokes-representing up to 50 × undersampling relative to Nyquist. Generalizable performance across knee, ankle, and out-of-distribution shoulder datasets is demonstrated and superior image quality compared to compressed sensing and conventional iterative methods, especially at high acceleration factors is shown.
conclusionThe proposed framework marks a significant step toward practical deployment of diffusion models in non-Cartesian MRI reconstruction.
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