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<p>Image quality and diagnostic performance between a fast spin-echo sequence with standard parameters (FSE-SD) and a deep learning-reconstructed fast spin echo with low parameters (FSE-DL) are compared for temporomandibular joint (TMJ) magnetic resonance imaging (MRI) at 1.5-T and 3-T scanners. This study enrolled 183 patients (94 scanned at 1.5-T and 89 at 3-T) who underwent both FSE-SD and FSE-DL acquisitions for temporomandibular disorders. Scan time, subjective image quality, detection rates of pathological features, and the inter-protocol and inter-reader agreement were assessed by two readers. The Wilcoxon signed-rank test, McNemar test, and unweighted/linearly weighted Cohen’s <i>κ</i> statistics were used. Using the deep learning algorithm, total scan time at 1.5-T was reduced from 316 s (FSE-SD) to 211 s (FSE-DL), representing a 33.23% reduction. At 3-T, scan time was similarly shortened from 329 s (FSE-SD) to 189 s (FSE-DL), with a 42.55% decrease. Compared with FSE-SD, FSE-DL showed lower noise and fewer artifacts, with overall image quality similar or slightly improved at both 1.5-T and 3-T; most of these differences were statistically significant. Diagnostic confidence was not significantly different between the two protocols. Interreader and interprotocol agreement ranged from moderate to almost perfect (<i>κ</i> values ranging from 0.602 to 1.000). Detection rates of major TMJ pathologies were similar between the two protocols for both readers at 1.5-T and 3-T (<i>p</i> ⩾ 0.375). FSE-DL achieved a significant reduction in scan time for TMJ MRI at both 1.5-T and 3-T while maintaining overall image quality, and demonstrated pathological feature detection performance comparable to that of the FSE-SD.</p>

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Deep Learning-Based Reconstruction of Temporomandibular Joint MRI at 1.5-T and 3-T

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Xin Zhou,
  • Xiaomin Li,
  • Zhengjia Zhang,
  • Hairong Ma,
  • Ran Cao,
  • Jianwei Wu,
  • Qi Sun,
  • Songtao Ai

摘要

Image quality and diagnostic performance between a fast spin-echo sequence with standard parameters (FSE-SD) and a deep learning-reconstructed fast spin echo with low parameters (FSE-DL) are compared for temporomandibular joint (TMJ) magnetic resonance imaging (MRI) at 1.5-T and 3-T scanners. This study enrolled 183 patients (94 scanned at 1.5-T and 89 at 3-T) who underwent both FSE-SD and FSE-DL acquisitions for temporomandibular disorders. Scan time, subjective image quality, detection rates of pathological features, and the inter-protocol and inter-reader agreement were assessed by two readers. The Wilcoxon signed-rank test, McNemar test, and unweighted/linearly weighted Cohen’s κ statistics were used. Using the deep learning algorithm, total scan time at 1.5-T was reduced from 316 s (FSE-SD) to 211 s (FSE-DL), representing a 33.23% reduction. At 3-T, scan time was similarly shortened from 329 s (FSE-SD) to 189 s (FSE-DL), with a 42.55% decrease. Compared with FSE-SD, FSE-DL showed lower noise and fewer artifacts, with overall image quality similar or slightly improved at both 1.5-T and 3-T; most of these differences were statistically significant. Diagnostic confidence was not significantly different between the two protocols. Interreader and interprotocol agreement ranged from moderate to almost perfect (κ values ranging from 0.602 to 1.000). Detection rates of major TMJ pathologies were similar between the two protocols for both readers at 1.5-T and 3-T (p ⩾ 0.375). FSE-DL achieved a significant reduction in scan time for TMJ MRI at both 1.5-T and 3-T while maintaining overall image quality, and demonstrated pathological feature detection performance comparable to that of the FSE-SD.