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Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity
- Source :
- Academic radiology.
- Publication Year :
- 2022
-
Abstract
- To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBEPatients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBEA total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBEThe deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique.
- Subjects :
- Radiology, Nuclear Medicine and imaging
Subjects
Details
- ISSN :
- 18784046
- Database :
- OpenAIRE
- Journal :
- Academic radiology
- Accession number :
- edsair.doi.dedup.....142291d779b4028f43d96486971db6db