Results 151 to 160 of about 65,529 (267)
A Comparative Evaluation of Super-Resolution Methods for Spectral Images Using Pretrained RGB Models. [PDF]
Shokoohi N +3 more
europepmc +1 more source
Evaluation of SSIM loss function in RIR generator GANs
Mehmet Pekmezci, Yakup Genç
semanticscholar +1 more source
Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations
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
YUV-based SVD-VGG hybrid fusion for multimodal MRI-PET image integration. [PDF]
S S V V Ramesh K, Selva Kumar S.
europepmc +1 more source
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
CLIP-RL: Closed-Loop Video Inpainting with Detection-Guided Reinforcement Learning. [PDF]
Wang M, Ren J, Wang B, Tang X.
europepmc +1 more source
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
Infrared Image Denoising Algorithm Based on Wavelet Transform and Self-Attention Mechanism. [PDF]
Li H, Zhang Y, Yang L, Zhang H.
europepmc +1 more source
Few‐shot learning for highly accelerated 3D time‐of‐flight MRA reconstruction
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

