A plug-and-play bilateral breast gradient insert prototype achieves 2.8 mT/m/A efficiency and local strengths up to 1850 mT/m, allowing b=10000 s/mm² diffusion MRI at TE=78 ms versus 161 ms with scanner gradients.
N4ITK: Improved N3 Bias Correction
6 Pith papers cite this work. Polarity classification is still indexing.
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CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
Finite-sample noise collapses the eigengap in representation covariances limiting recoverable modes K(N); multimodal learning stabilizes it via low-rank constraints, yielding better class separation quantified by truncated Mahalanobis energy approximated with a zeta function.
Articulatory configurations during vowel production create distinct electromagnetic transmission patterns through the vocal tract, confirmed by qualitative agreement between finite-element simulations and scattering-matrix measurements on two subjects.
Automated reference-region normalization of optical density in myelin histology yields substantially stronger voxel-wise correlation with 7T ex vivo MRI than unnormalized measurements, including inside white matter hyperintensities.
A public GPU workflow for non-Fourier SENSE MRI reconstruction with sensitivity and off-resonance mapping enables fast, accurate imaging from challenging spiral trajectories.
citing papers explorer
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Bilateral breast gradient insert prototype for strong diffusion encoding at 3T
A plug-and-play bilateral breast gradient insert prototype achieves 2.8 mT/m/A efficiency and local strengths up to 1850 mT/m, allowing b=10000 s/mm² diffusion MRI at TE=78 ms versus 161 ms with scanner gradients.
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CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans
CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
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Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
Finite-sample noise collapses the eigengap in representation covariances limiting recoverable modes K(N); multimodal learning stabilizes it via low-rank constraints, yielding better class separation quantified by truncated Mahalanobis energy approximated with a zeta function.
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Articulatory movements influence electromagnetic wave transmission through the vocal tract
Articulatory configurations during vowel production create distinct electromagnetic transmission patterns through the vocal tract, confirmed by qualitative agreement between finite-element simulations and scattering-matrix measurements on two subjects.
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Automated Optical Density Normalization for Myelin Quantification: Cross-Modal Validation with 7T Ex Vivo MRI
Automated reference-region normalization of optical density in myelin histology yields substantially stronger voxel-wise correlation with 7T ex vivo MRI than unnormalized measurements, including inside white matter hyperintensities.
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A GPU-enhanced workflow for non-Fourier SENSE reconstruction
A public GPU workflow for non-Fourier SENSE MRI reconstruction with sensitivity and off-resonance mapping enables fast, accurate imaging from challenging spiral trajectories.