ArticleMagnetic resonance in medicine2026
A Deep Nonlinear Subspace Modeling and Reconstruction for Diffusion-Weighted Imaging Using Denoising Auto-Encoder.
Article in Magnetic resonance in medicine, 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
purposeTo present a novel, nonlinear subspace modeling and joint k-q-space reconstruction technique for high-resolution, multi-band, multi-shell diffusion-weighted imaging (DWI).
methodsHigh b-value (> 1000 s/mm
resultsComparing the reconstructed data using the proposed method shows improved noise suppression compared to MUSE and more details than LLR-reconstructed images. Specifically, in the higher b-value domain, the reconstruction results show improved detectability of small structures. This bias and precision analysis showed minimal bias introduction, but higher precision with the proposed method.
conclusionOur method combines deep learning, latent signal modeling, joint k-q-space reconstruction, and biophysical simulation for diffusion data and shows strong noise suppression and a high degree of detail in reconstructed diffusion-weighted images.
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