Relaxation times in solid compartments of the human brain measured with fast imaging with steady-state free precession MR fingerprinting varied less than 8.0% (95% confidence intervals: −8.0%, 8.0%... Background Only sparse literature investigates the reproducibility and repeatability of relaxometry methods in MRI. However, statistical data on reproducibility and repeatability of any quantitativ...

Deep learning–reconstructed turbo spin-echo (TSE) acquisition was interchangeable with standard TSE for detecting various spinal abnormalities at MRI, providing excellent quality and a 70% reductio... Background Deep learning (DL)–based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly ac...

Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most common examinations in clinical routine. The application of Deep Learning (DL) reconstruction for MRI is increasingly gaining attention due to its potential to improve the image quality and reduce the acquisition time simultaneously. However, the technology has not yet been implemented in clinical routine for turbo spin echo (TSE) sequences in musculoskeletal imaging. The aim of this study was therefore to assess the technical feasibility and evaluate the image quality. Sixty examinations of knee, hip, ankle, shoulder, hand, and lumbar spine in healthy volunteers at 3 T were included in this prospective, internal-review-board-approved study. Conventional (TSE_S) and DL-based TSE sequences (TSE_DL) were compared regarding image quality, anatomical structures, and diagnostic confidence. Overall image quality was rated to be excellent, with a significant improvement in edge sharpness and reduced noise compared to TSE_S (p < 0.001). No difference was found concerning the extent of artifacts, the delineation of anatomical structures, and the diagnostic confidence comparing TSE_S and TSE_DL (p > 0.05). Therefore, DL image reconstruction for TSE sequences in MSK imaging is feasible, enabling a remarkable time saving (up to 75%), whilst maintaining excellent image quality and diagnostic confidence.

Deep learning superresolution simultaneous multislice parallel imaging–accelerated knee MRI provided excellent image quality and improved diagnostic performance for articular cartilage defects while maintaining performance for meniscus and ligament tears.