Cargando…
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset
This work aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the meta-learning framework. Specifically, we develop a deep reconstruction network induced by a learnable optimization algorithm (LOA) to solve the nonconvex nonsmooth variational model of MRI ima...
Autores principales: | Bian, Wanyu, Chen, Yunmei, Ye, Xiaojing, Zhang, Qingchao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8621471/ https://www.ncbi.nlm.nih.gov/pubmed/34821862 http://dx.doi.org/10.3390/jimaging7110231 |
Ejemplares similares
-
Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction
por: Zhang, Qingchao, et al.
Publicado: (2022) -
Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement
por: Bian, Wanyu, et al.
Publicado: (2023) -
Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal
por: Dinsdale, Nicola K., et al.
Publicado: (2021) -
Reconstruction of Diverse Verrucomicrobial Genomes from Metagenome Datasets of Freshwater Reservoirs
por: Cabello-Yeves, Pedro J., et al.
Publicado: (2017) -
Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model
por: Chen, Weidong, et al.
Publicado: (2022)