More articles from ARTIFICIAL INTELLIGENCE
- Deep Learning–Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases
This study prospectively compared the image quality and diagnostic performance of conventional MRI and accelerated cervical MRI with DL-based reconstruction in evaluating cervical degenerative spine diseases and myelopathy. The DL-reconstructed protocol demonstrated reduction in scan time, higher or comparable SNR and CNR with higher or similar overall image quality, and higher sensitivity for detecting neural foraminal stenosis and comparable diagnostic performance for other degenerative cervical spinal diseases and myelopathy.
- AI-Generated Synthetic STIR of the Lumbar Spine from T1 and T2 MRI Sequences Trained with Open-Source Algorithms
This proof-of-concept investigation used open-source generative adversarial networks to create synthetic lumbar spine MRI STIR from T1 and T2 sequences. Evaluating radiologists found the synthetic volumes were of equal or better quality in 77% of test patients and demonstrated equivalent or decreased motion artifacts in 78% of test patients. The synthetic volumes had high positive predictive value (75%-100%) but lower sensitivity (0%-67%) for common pathologies. The results from this study are a promising first step toward expediting imaging protocols.