Track-weighted imaging methods: extracting information from a streamlines tractogram.
Fernando CalamantePublished in: Magma (New York, N.Y.) (2017)
A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T2 map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
Keyphrases
- resting state
- functional connectivity
- deep learning
- contrast enhanced
- convolutional neural network
- magnetic resonance
- optical coherence tomography
- health information
- high resolution
- magnetic resonance imaging
- white matter
- artificial intelligence
- diffusion weighted imaging
- healthcare
- machine learning
- computed tomography
- primary care
- oxidative stress
- social media
- multiple sclerosis
- cerebral ischemia
- mass spectrometry
- brain injury
- quality improvement