Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.

Journal: Magma (New York, N.Y.)

PMID: 37989921

Abstract

OBJECTIVE: This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determine the validity of differences between different DUNet configurations and their impact on image quality metrics.

Authors

Rishabh Sharma

Mechanical Engineering Department, The NorthCap University, Gurugram, Haryana, India.
Panagiotis Tsiamyrtzis

Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
Andrew G Webb

Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, NL, the Netherlands.
Ernst L Leiss

Department of Computer Science, University of Houston, Houston, TX, USA.
Nikolaos V Tsekos

Department of Computer Science, University of Houston, Houston, TX, USA. nvtsekos@central.uh.edu.

Keywords

Algorithms Analysis of Variance Brain Deep Learning Humans Image Enhancement Image Processing, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer Reproducibility of Results Signal-To-Noise Ratio

External Resources

View on PubMed Access via DOI PubMed (37989921)

Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals