Latest Advances in Image Acceleration: All Dimensions are Fair Game.
Camila MunozAnastasia FotakiRené M BotnarClaudia PrietoPublished in: Journal of magnetic resonance imaging : JMRI (2022)
Magnetic resonance imaging (MRI) is a versatile modality that can generate high-resolution images with a variety of tissue contrasts. However, MRI is a slow technique and requires long acquisition times, which increase with higher temporal and spatial resolution and/or when multiple contrasts and large volumetric coverage is required. In order to speedup MR data acquisition, several approaches have been introduced in the literature. Most of these techniques acquire less data than required and exploit intrinsic redundancies in the MR images to recover the information that was not sampled. This article presents a review of MR acquisition and reconstruction methods that have exploited redundancies in the temporal, spatial, and contrast/parametric dimensions to accelerate image data acquisition, focusing on cardiac and abdominal MR imaging applications. The review describes how each of these dimensions has been separately exploited for speeding up MR acquisition to then discuss more advanced techniques where multiple dimensions are exploited together for further reducing scan times. Finally, future directions for multidimensional image acceleration and remaining technical challenges are discussed. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- deep learning
- computed tomography
- magnetic resonance
- diffusion weighted imaging
- electronic health record
- high resolution
- big data
- convolutional neural network
- systematic review
- artificial intelligence
- optical coherence tomography
- machine learning
- health information
- single molecule
- current status
- social media
- high speed
- affordable care act