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Related Experiment Video

Updated: Jun 19, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Learning Assisted Double Inversion Recovery improves Focal Lesion Detection in Optic Neuropathy.

Dandan Zhang1, Qichao Cheng1, Ziyao Liu1

  • 1From the Department of Radiology (D.Z., Q.C., Z.L., B.S., Y.Z., X.B., Q.L., A.L.), Ophthalmology (Y.Q.), Neurology (S.W., L.C.), Qilu Hospital of Shandong University, Jinan, Shandong, China; Department of Radiology (D.Z.), The People's Hospital of Qihe, Dezhou, Shandong, China and Department of Radiology (Q.L.), The People's Hospital of XueCheng, ZaoZhuang, Shandong, China.

AJNR. American Journal of Neuroradiology
|June 17, 2026
PubMed
Summary

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This summary is machine-generated.

Deep learning-accelerated Double Inversion Recovery (DIR-DL) MRI significantly reduces optic nerve imaging time by 43.57% while maintaining excellent image quality and diagnostic confidence for optic neuropathy evaluation.

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Neuro-ophthalmology

Background:

  • Optic neuropathy diagnosis relies on Magnetic Resonance Imaging (MRI) for visualizing optic nerve abnormalities.
  • Conventional MRI protocols can be lengthy, impacting patient comfort and throughput.
  • Deep learning (DL) reconstruction offers potential for accelerated imaging without compromising diagnostic quality.

Purpose of the Study:

  • To compare the acquisition time, image quality, and diagnostic confidence of DL-accelerated DIR (DIR-DL) with conventional MRI sequences.
  • To evaluate the effectiveness of DIR-DL in patients with suspected optic neuropathy.

Main Methods:

  • Prospective study of 75 patients with suspected optic neuropathy undergoing both conventional MRI and DIR-DL protocols.

Related Experiment Videos

Last Updated: Jun 19, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • Radiologists assessed lesion extent, image quality, SNR, sharpness, artifacts, and diagnostic confidence using Likert scales.
  • Statistical analysis included logistic regression, linear mixed models, Cohen's κ, and individual equivalence index (IEI) for comparison.

    • Main Results:

    • DIR-DL reduced total acquisition time by 43.57% (12 min 56 s vs. 22 min 55 s).
    • DIR-DL demonstrated a higher proportion of excellent overall image quality (87.56%) compared to conventional sequences.
    • DIR and DIR-DL detected significantly longer lesion lengths and showed higher specificity than STIR sequences.

    Conclusions:

    • DIR-DL significantly reduces MRI scan time for optic nerve evaluation.
    • The DL-accelerated technique provides superior image quality and comparable diagnostic confidence to standard STIR MRI.
    • DIR-DL represents a clinically viable advancement for imaging optic neuropathy.