Results 151 to 160 of about 65,529 (267)

Evaluation of SSIM loss function in RIR generator GANs

open access: yesDigit. Signal Process.
Mehmet Pekmezci, Yakup Genç
semanticscholar   +1 more source

Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations

open access: yesHuman Brain Mapping, Volume 47, Issue 2, February 1, 2026.
We introduce a new framework for accelerated processing of diffusion‐weighted imaging (DWI) data using a machine learning approach to optimize parameter estimation. We demonstrate that this new method, called DBSIpy, significantly improves computational speed and robustness to Rician noise compared to the standard DBSI method, with the improvements ...
Kainen L. Utt   +3 more
wiley   +1 more source

Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification

open access: yesJournal of Magnetic Resonance Imaging, Volume 63, Issue 2, Page 525-535, February 2026.
ABSTRACT Background Magnetic Resonance Fingerprinting (MRF) is a technique that can provide rapid quantification of multiple tissue properties. Deep learning may potentially contribute to an accelerated acquisition of MRF. Purpose (1) To develop a deep learning method to accelerate the acquisition for kidney MRF; (2) to evaluate its performance in ...
Zhiqing Yin   +8 more
wiley   +1 more source

Optimized reconstruction of undersampled Dixon sequences using new memory‐efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN

open access: yesMagnetic Resonance in Medicine, Volume 95, Issue 2, Page 1189-1204, February 2026.
Abstract Purpose Fat fraction (FF) quantification in individual muscles using quantitative MRI is of major importance for monitoring disease progression and assessing disease severity in neuromuscular diseases. Undersampling of MRI acquisitions is commonly used to reduce scanning time. The present paper introduces novel unrolled neural networks for the
Sandra Martin   +6 more
wiley   +1 more source

Few‐shot learning for highly accelerated 3D time‐of‐flight MRA reconstruction

open access: yesMagnetic Resonance in Medicine, Volume 95, Issue 2, Page 770-786, February 2026.
Abstract Purpose To develop a deep learning‐based reconstruction method for highly accelerated 3D time‐of‐flight MRA (TOF‐MRA) that achieves high‐quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time‐consuming acquisition of high‐resolution, whole‐head angiograms.
Hao Li   +4 more
wiley   +1 more source

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