A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers.
Andra-Iza IugaPhilip Santiago RauenFlorian SiedekNils Große-HokampKristina SonnabendDavid MaintzSimon LennartzGrischa BratkePublished in: The British journal of radiology (2023)
Combining compressed SENSE with a newly developed deep learning-based algorithm using convolutional neural networks allows a 64% reduction in scan time for 2D imaging of the knee. Implementation of the new deep learning-based algorithm in clinical routine in near future should enable better image quality/resolution with constant scan time, or reduced acquisition times while maintaining diagnostic quality.
Keyphrases
- deep learning
- convolutional neural network
- image quality
- computed tomography
- magnetic resonance
- total knee arthroplasty
- artificial intelligence
- dual energy
- machine learning
- knee osteoarthritis
- healthcare
- quality improvement
- primary care
- high resolution
- contrast enhanced
- magnetic resonance imaging
- single molecule
- anterior cruciate ligament
- clinical practice
- current status
- anterior cruciate ligament reconstruction