Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia

Jun Ho Hwang, Chang Kyu Park, Seok Bin Kang, Man Kyu Choi, Won Hee Lee

Research output: Contribution to journalArticlepeer-review

Abstract

This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin–echo series volume isotropic turbo spin–echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN.

Original languageEnglish
Article number355
JournalLife
Volume14
Issue number3
DOIs
Publication statusPublished - Mar 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • artificial intelligence (AI)
  • deep learning
  • magnetic resonance imaging (MRI)
  • super resolution (SR)
  • trigeminal neuralgia (TN)

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