Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture

• URL: http://arxiv.org/abs/2211.15047v1
• Date: Mon, 28 Nov 2022 04:09:21 GMT
• Title: Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture
• Authors: Aryan Kalluvila, Neha Koonjoo, Danyal Bhutto, Marcio Rockenbach, Matthew S. Rosen
• Abstract summary: The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans to improve diagnostic capability. We propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested deep learning methods with an average PSNR of 78.83 and SSIM of 0.9551.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-field (LF) MRI scanners have the power to revolutionize medical imaging by providing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usually significantly noisier and lower quality than their high-field counterparts. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans to improve diagnostic capability. To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested deep learning methods with an average PSNR of 78.83 and SSIM of 0.9551. We tested our network on artificial noisy downsampled synthetic data from a major T1 weighted MRI image dataset called the T1-mix dataset. One board-certified radiologist scored 25 images on the Likert scale (1-5) assessing overall image quality, anatomical structure, and diagnostic confidence across our architecture and other published works (SR DenseNet, Generator Block, SRCNN, etc.). We also introduce a new type of loss function called natural log mean squared error (NLMSE). In conclusion, we present a more accurate deep learning method for single image super-resolution applied to synthetic low-field MRI via a Nested U-Net architecture.

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