[Applications of Artificial Intelligence in MR Image Acquisition and Reconstruction].
Junghwa KangYoonho NamPublished in: Journal of the Korean Society of Radiology (2022)
Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.
Keyphrases
- artificial intelligence
- deep learning
- contrast enhanced
- big data
- convolutional neural network
- magnetic resonance imaging
- machine learning
- magnetic resonance
- computed tomography
- clinical practice
- diffusion weighted imaging
- working memory
- healthcare
- mental health
- climate change
- mass spectrometry
- photodynamic therapy
- risk assessment
- drug induced