Abstract #1202

Super-Resolution Image Enhancement for 3D Morphological Lung MRI with bSTAR at 0.55T

Pavlos Panos^1,2, Maurice Pradella³, Katrin Esther Hostettler⁴, Oliver Bieri ^1,2, and Grzegorz Bauman^1,2

¹Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland, ²Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland, ³Department of Radiology, University Hospital Basel, Basel, Switzerland, ⁴Clinics of Respiratory Medicine, University Hospital Basel, Basel, Switzerland

Synopsis

Keywords: Analysis/Processing, Low-Field MRI, Lung, Deep Learning

Motivation: Despite the development of various dedicated techniques for morphological lung MRI, these methods still offer limited spatial resolution compared to computed tomography.

Goal(s): With the rapid progress in AI-based image enhancement techniques, our study aims to improve lung MRI quality using a deep learning-based super-resolution model, thereby enabling better visualization of lung morphology.

Approach: We applied a pre-trained 2D image enhancement model to 3D lung MRI datasets acquired at 0.55 T to enhance image quality.

Results: The multi-orientation technique we implemented preserves anatomical accuracy while reducing noise, leading to significant improvements in lung MRI image quality.

Impact: Due to physical and physiological challenges lung MRI is currently rarely used in clinical routine¹. Our study demonstrates that a deep learning-based super-resolution image enhancement results in improved visualization of MRI lung morphology without introducing new anatomical structures.

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