Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.
Dana J LinPatricia M JohnsonFlorian KnollYvonne W LuiPublished in: Journal of magnetic resonance imaging : JMRI (2020)
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
Keyphrases
- deep learning
- artificial intelligence
- big data
- machine learning
- convolutional neural network
- high resolution
- image quality
- electronic health record
- healthcare
- magnetic resonance
- mental health
- heart failure
- contrast enhanced
- computed tomography
- blood brain barrier
- photodynamic therapy
- palliative care
- data analysis
- cerebral ischemia
- brain injury
- white matter