Abstract #2095

Deep Learning-Based Super-Resolution Reconstruction of T2w Single-Shot Sequences in Pediatric Brain MRI

Anna Magdalena Stylianou¹, Zeynep Bendella¹, Christoph Katemann², Alois Martin Sprinkart³, Kilian Weiss², Oliver Weber², Johannes M. Peeters², Tobias Schommertz¹, Mergim Kadrija¹, Leon Bischoff³, Nils Lehnen¹, Ralf Clauberg¹, Julian Luetkens³, Alexander Radbruch¹, and Barbara Daria Wichtmann¹

¹Department of Neuroradiology, University Hospital Bonn, Bonn, Germany, ²Philips GmbH Market DACH, Hamburg, Germany, ³Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany

Synopsis

Keywords: Neuro, Brain, Deep-Learning, Super-Resolution Reconstruction

Motivation: Single-Shot sequences are essential in pediatric MRI, where motion is a major challenge, as they enable rapid image acquisition, although some image quality may be sacrificed. Balancing speed and quality is key to achieving optimal results.

Goal(s): To assess the diagnostic value of Super-Resolution reconstructed T2w Single-Shot sequences in pediatric brain MRI, using an industry-developed deep-learning algorithm that combines compressed sensing with image denoising and resolution upscaling.

Approach: Single-Shot sequences without (T2-SSH_conv) and with Super-Resolution reconstruction (T2-SSH_DL) were compared qualitatively and quantitatively.

Results: T2-SSH_DL not only showed improved image sharpness but also significantly increased lesion conspicuity and overall image quality.

Impact: Deep learning reconstruction significantly enhances the quality of rapid T2w Single-Shot sequences in pediatric brain MRI, improving image sharpness and lesion conspicuity while maintaining motion robustness. This advancement could reduce sedation requirements in pediatric neuroimaging protocols.

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