access icon free Visual Exploration of Diffierences Among DTI Fiber Models

In-vivo studies of fibrous structures require non-invasive tools, of which one is fiber tracking based on Diffiusion tensor imaging (DTI) datasets. Different fiber models can be produced from diffierent DTI images, which may vary from subject to subject due to variations in anatomy, motions in scanning, and signal noises. Additionally, parameters of the tracking method also have a great influence on resulting models. Illustrating, exploring, and analyzing diffierences among DTI fiber models are crucial for the purposes of group comparison, atlas construction, and uncertainty analysis. Conventional approaches illustrate fiber models in 3D space and explore diffierences either voxel-wisely or fiber-based. However, these approaches rely on accurate alignment processes and may easily be disturbed by visual clutters. We introduce a two-phase projection technique to illustrate a complex 3D fiber model with a unique 2D map to characterize features for further exploration and analysis. Moreover, regions of significant diffierences among the maps are marked out. In these 2D maps, diffierences can be easily distinguished without occlusions that often occur in 3D spaces. To facilitate comparative analysis from multiple perspectives, we design an interface for interactive exploration. The effiectiveness of our approach is evaluated with two datasets.

Inspec keywords: biodiffusion; medical image processing; biomedical MRI; data visualisation; feature extraction

Other keywords: uncertainty analysis; atlas construction; 2D map; visual exploration; DTI images; fiber tracking; noninvasive tools; complex 3D fiber model; tracking method; visual clutters; 3D space; interactive exploration; signal noises; Diffusion tensor imaging datasets; two-phase projection technique; DTI fiber models; 2D maps

Subjects: Biomedical magnetic resonance imaging and spectroscopy; Medical magnetic resonance imaging and spectroscopy; Biology and medical computing; Image recognition; Computer vision and image processing techniques; Graphics techniques; Patient diagnostic methods and instrumentation

http://iet.metastore.ingenta.com/content/journals/10.1049/cje.2018.06.015
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content/journals/10.1049/cje.2018.06.015
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