Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI

Clinical Snapshot

Key Findings

• P Value: aCNR p<0.001; aSNR p=0.003; ERD p<0.001; qualitative assessments p<0.001

• Effect Size: aCNR: 29.9 ± 22.6 (DL) vs. 26.7 ± 16.5 (conventional); aSNR: 41.6 ± 27.9 vs. 38.2 ± 20.8; ERD: 0.90 ± 0.35 mm vs. 1.35 ± 0.42 mm (lower ERD = sharper edges)

• Primary Outcome: Image quality of T2-SSHDL versus T2-SSHconv and high-resolution T2-weighted sequences, assessed by quantitative metrics and qualitative radiologist ratings

• Nnt Or Sensitivity: No sensitivity/specificity reported. ERD improvement of 0.45 mm (33% reduction) represents the most clinically interpretable sharpness gain. No NNT or diagnostic accuracy statistics calculable from reported data.

• Confidence Interval: Not reported for any primary metric

Clinical Application

Requires Philips MRI hardware with the specific DL-CS reconstruction pipeline. Not currently available across all vendor platforms. Implementation requires vendor software licensing and potentially radiologist training in interpreting DL-reconstructed images. Acquisition time advantage of single-shot sequences is a practical benefit in busy paediatric MRI lists. Paediatric sedation for MRI is a significant clinical and resource burden in Australian hospitals, with anaesthetic involvement required under ANZCA/ACHS guidelines. Reducing sedation rates would have direct implications for patient safety, theatre/anaesthetic resource utilisation, and waiting times at major paediatric centres (e.g., RCH Melbourne, Sydney Children's, PCH Perth). The technology is not currently listed on the MBS as a distinct item, and TGA approval of specific DL reconstruction software would need to be confirmed before clinical deployment. RACGP and paediatric radiology societies (RANZCR) have not yet issued specific guidance on DL-enhanced MRI reconstruction. PBS implications are indirect — reduced sedation may decrease associated medication costs and anaesthetic MBS claims. Paediatric patients aged approximately 0–16 years requiring brain MRI, particularly those at high risk of motion artefact (young children, developmentally delayed, uncooperative patients) where sedation would otherwise be considered

Abstract

OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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. KEY POINTS: 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.

References

1. 1.Baz, A. M., Bendella, Z., Katemann, C., Sprinkart, A. M., Weiss, K., Weber, O. M., Peeters, J. M., Lehnen, N. C., Clauberg, R., Luetkens, J. A., Radbruch, A., & Wichtmann, B. D. (2025). Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI. European Radiology. Advance online publication. https://doi.org/10.1007/s00330-025-11745-4