Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.
Hyunsuk YooRoh-Eul YooSeung Hong ChoiInpyeong HwangJi Ye LeeJune Young SeoSeok Young KohKyu Sung ChoiKoung Mi KangTae Jin YunPublished in: European radiology (2023)
• By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.
Keyphrases
- deep learning
- image quality
- contrast enhanced
- computed tomography
- convolutional neural network
- magnetic resonance imaging
- artificial intelligence
- machine learning
- diffusion weighted imaging
- dual energy
- randomized controlled trial
- systematic review
- magnetic resonance
- quantum dots
- sensitive detection
- loop mediated isothermal amplification