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

Magnetic Resonance Materials in Physics Biology and Medicine, vol. 37, pp. 507–528

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. MATERIALS AND METHODS: To achieve this, we trained all DUNets using the same learning rate and number of epochs, with variations in 5 acquisition protocols, 24 loss function weightings, and 2 ground truths. We calculated evaluation metrics for two metric regions of interest (ROI). We employed both Analysis of Variance (ANOVA) and Mixed Effects Model (MEM) to assess the statistical significance of the independent parameters, aiming to compare their efficacy in revealing differences and interactions among fixed parameters. RESULTS: ANOVA analysis showed that, except for the acquisition protocol, fixed variables were statistically insignificant. In contrast, MEM analysis revealed that all fixed parameters and their interactions held statistical significance. This emphasizes the need for advanced statistical analysis in comparative studies, where MEM can uncover finer distinctions often overlooked by ANOVA. DISCUSSION: These findings highlight the importance of utilizing appropriate statistical analysis when comparing different deep learning models. Additionally, the surprising effectiveness of the UNet architecture in enhancing various acquisition protocols underscores the potential for developing improved methods for characterizing and training deep learning models. This study serves as a stepping stone toward enhancing the transparency and comparability of deep learning techniques for medical imaging applications.

Authors 5

1. Rishabh Sharma

   University of Houston

   Affiliation as printed

   Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA

2. Panagiotis Tsiamyrtzis

   Athens University of Economics and Business · Politecnico di Milano

   Affiliation as printed

   Department of Mechanical Engineering, Politecnico Di Milano, Milan, Italy

   Department of Statistics, Athens University of Economics and Business, Athens, Greece

3. ANDREW G. WEBB Aachen C.J. Gorter Center for High Field MRI Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

4. Ernst L. Leiss

   University of Houston

   Affiliation as printed

   Department of Computer Science, University of Houston, Houston, TX, USA

5. Nikolaos V. Tsekos corresponding

   University of Houston

   Affiliation as printed

   Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA. nvtsekos@central.uh.edu

   Medical Robotics and Imaging Lab, Department of Computer Science, 501, Philip G. Hoffman Hall, University of Houston, 4800 Calhoun Road, Houston, TX, 77204, USA