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| Home > Publications Database > Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI. |
| Home > Publications Database > Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI. |
| Journal Article | DZNE-2026-00715 |
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2026
Springer
Heidelberg
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Please use a persistent id in citations: doi:10.1007/s00330-026-12482-y
Abstract: Motion and limited compliance compromise diagnostic MR image quality, particularly in pediatric patients who frequently require sedation. Single-shot sequences offer a time-efficient alternative but suffer from reduced image quality. This study aimed to evaluate the diagnostic performance of a deep learning (DL) framework combining compressed sensing (CS) and convolutional neural networks (CNNs) to enhance T2-weighted single-shot MRI (T2-SSHDL) compared with conventional CS-based reconstruction (T2-SSHconv) and routinely acquired high-resolution T2-weighted sequences.This prospective single-center study included 62 pediatric patients (mean age, 7.4 ± 4.9 years; 36 males, 26 females), who underwent T2-weighted single-shot brain MRI (29 sedated, 33 awake). Raw data were reconstructed using a DL-based pipeline and compared with conventional CS-based reconstructions. Quantitative metrics included apparent contrast-to-noise ratio (aCNR), apparent signal-to-noise ratio (aSNR), and edge rise distance (ERD). Two radiologists rated images for artifacts, sharpness, lesion conspicuity, and overall quality on a 5-point Likert scale.T2-SSHDL-sequences showed significantly higher aCNR (29.9 ± 22.6 vs. 26.7 ± 16.5; p < 0.001), aSNR (41.6 ± 27.9 vs. 38.2 ± 20.8; p = 0.003), and improved sharpness (ERD 0.90 ± 0.35 mm vs. 1.35 ± 0.42 mm; p < 0.001). Qualitative assessments confirmed superior image quality, lesion conspicuity, and sharpness (p < 0.001). Compared with high-resolution T2-weighted sequences, T2-SSHDL-sequences showed fewer motion artifacts and comparable lesion conspicuity in non-sedated patients.DL-based reconstruction significantly enhances the diagnostic quality of T2-weighted single-shot brain MRI in pediatric patients, enabling clinically usable, ultrafast, motion-robust imaging with potential to reduce the need for sedation.Question Can deep learning-based reconstruction elevate motion-robust single-shot T2-weighted pediatric brain MRI to diagnostic image quality levels, enabling reliable imaging without sedation? Findings Both quantitative and qualitative evaluations confirmed significantly improved image quality of deep learning-enhanced single-shot T2-weighted brain MRI compared with conventional reconstruction. Clinical relevance Deep learning-enhanced reconstruction improves image quality in ultrafast, motion-robust single-shot pediatric brain MRI, potentially reducing the need for sedation while preserving diagnostic accuracy. This approach may enhance patient safety and shorten examination time in routine neuroimaging.
Keyword(s): Humans (MeSH) ; Child (MeSH) ; Deep Learning (MeSH) ; Female (MeSH) ; Magnetic Resonance Imaging: methods (MeSH) ; Prospective Studies (MeSH) ; Male (MeSH) ; Brain: diagnostic imaging (MeSH) ; Motion (MeSH) ; Child, Preschool (MeSH) ; Artifacts (MeSH) ; Signal-To-Noise Ratio (MeSH) ; Convolutional Neural Networks (MeSH) ; Adolescent (MeSH) ; Image Interpretation, Computer-Assisted: methods (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Infant (MeSH) ; Brain ; Deep learning ; Magnetic resonance imaging ; Motion artifacts ; Pediatrics
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