Enhancement of Image Quality in Low-Field Knee MR Imaging Using Deep Learning

Abstract

Purpose: The purpose of this study is to investigate the potential of deep learning (DL) techniques to enhance the image quality of low-field knee MR images, with the ultimate goal of approximating the standards of high-field knee MR imaging. Methods: We analyzed knee MR images collected from 45 patients with knee disorders and six normal subjects using a 3T MR scanner and those collected from 25 patients with knee disorders using a 0.4T MR scanner. Two DL models were developed: a fat-suppression contrast-generation model and a super-resolution model. These DL models were trained using 3T knee MR imaging data and applied to 0.4T knee MR imaging data. Visual assessments of anatomical structures and image noise and abnormality detection with diagnostic confidence levels on the original 0.4T MR images and those after DL enhancement were conducted by two board-certified radiologists. Statistical analyses were performed using McNemar’s test and the Wilcoxon signed-rank test. Results: DL-enhanced MR images significantly improved the depiction of anatomical structures and reduced image noise compared to the original MR images. The number of abnormal findings detected and the diagnostic confidence levels were higher in the DL-enhanced MR images, indicating the potential for more accurate diagnoses. Conclusion: DL techniques effectively enhance the image quality of low-field knee MR images by leveraging 3T MR imaging data. This enhancement significantly improves image quality and diagnostic confidence levels, making low-field MR images much more reliable for detecting abnormalities. This advancement offers a useful alternative for clinical settings, especially in resource-limited environments, without compromising diagnostic accuracy.