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Purpose <p>As MRI frequently requires general anesthesia in pediatric patients, there is an undersupply in clinical routine. This may result in delayed examinations or use of CT, especially in emergency settings. To enable ad-hoc MRI scans and rule out increased intracranial pressure or intracranial mass lesions, we combined various acceleration techniques with Deep-Learning reconstruction, generating ultra-fast diagnostic images sufficient to exclude critical pathologies.</p> Methods <p>Thirty-six MRI datasets of infants with a&#xa0;median age of 35.2 months (SD ± 23.2) and anesthesia-free imaging were retrospectively evaluated. Imaging was performed using ultra-fast T2-weighted sequences in three planes (slice thickness 5 mm; total acquisition time&#xa0;47s). Four readers evaluated subjective image quality using a&#xa0;5-point Likert-scale. Readers were asked to indicate how safe they felt about assessing possible midline displacement or mass lesion. For semi-quantitative analysis, readers reported diameters of lateral and third ventricles. Gwet’s AC2 and intraclass correlation (ICC) were used for interrater agreement.</p> Results <p>94.4% of datasets showed at least acceptable diagnostic confidence. Readers felt confident excluding acute intracranial pathology. 52.1% of ultra-fast sequences demonstrated good to excellent image quality. 72.6% were rated with good or excellent diagnostic confidence. Interrater reliability demonstrated almost excellent agreement of diagnostic confidence (Gwet’s AC2 ≥ 0.886) and image quality (Gwet’s AC2 ≥ 0.942). Excellent agreement regarding ventricular width (ICC values ≥ 0.966) was shown for all measurements.</p> Conclusion <p>The use of deep-learning reconstruction algorithms in pediatric brain MRI is feasible, allowing anesthesia-free emergency imaging. This will reduce periprocedural risk, number of necessary CT scans, and lower healthcare costs.</p>

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Ultra-Fast Sub-Minute Brain MRI with Deep-Learning-reconstruction for Anesthesia-Free Emergency Imaging in Children

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Sebastian Altmann,
  • Nils F Grauhan,
  • Mario A A Mercado,
  • Haidara Almansour,
  • Roman H Paul,
  • Vannessa Ines Schöffling,
  • Malte Ottenhausen,
  • Marc A Brockmann,
  • Ahmed E Othman

摘要

Purpose

As MRI frequently requires general anesthesia in pediatric patients, there is an undersupply in clinical routine. This may result in delayed examinations or use of CT, especially in emergency settings. To enable ad-hoc MRI scans and rule out increased intracranial pressure or intracranial mass lesions, we combined various acceleration techniques with Deep-Learning reconstruction, generating ultra-fast diagnostic images sufficient to exclude critical pathologies.

Methods

Thirty-six MRI datasets of infants with a median age of 35.2 months (SD ± 23.2) and anesthesia-free imaging were retrospectively evaluated. Imaging was performed using ultra-fast T2-weighted sequences in three planes (slice thickness 5 mm; total acquisition time 47s). Four readers evaluated subjective image quality using a 5-point Likert-scale. Readers were asked to indicate how safe they felt about assessing possible midline displacement or mass lesion. For semi-quantitative analysis, readers reported diameters of lateral and third ventricles. Gwet’s AC2 and intraclass correlation (ICC) were used for interrater agreement.

Results

94.4% of datasets showed at least acceptable diagnostic confidence. Readers felt confident excluding acute intracranial pathology. 52.1% of ultra-fast sequences demonstrated good to excellent image quality. 72.6% were rated with good or excellent diagnostic confidence. Interrater reliability demonstrated almost excellent agreement of diagnostic confidence (Gwet’s AC2 ≥ 0.886) and image quality (Gwet’s AC2 ≥ 0.942). Excellent agreement regarding ventricular width (ICC values ≥ 0.966) was shown for all measurements.

Conclusion

The use of deep-learning reconstruction algorithms in pediatric brain MRI is feasible, allowing anesthesia-free emergency imaging. This will reduce periprocedural risk, number of necessary CT scans, and lower healthcare costs.