Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/10803
Title: A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms.
Austin Authors: Close, Thomas G;Tournier, Jacques-Donald;Calamante, Fernando;Johnston, Leigh A;Mareels, Iven;Connelly, Alan
Affiliation: Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, Australia
Issue Date: 8-Apr-2009
Publication information: Neuroimage 2009; 47(4): 1288-300
Abstract: The assessment of Diffusion-Weighted MRI (DW-MRI) fibre-tracking algorithms has been limited by the lack of an appropriate 'gold standard'. Practical limitations of alternative methods and physical models have meant that numerical simulations have become the method of choice in practice. However, previous numerical phantoms have consisted of separate fibres embedded in homogeneous backgrounds, which do not capture the true nature of white matter. In this paper we describe a method that is able to randomly generate numerical structures consisting of densely packed bundles of fibres, which are much more representative of human white matter, and simulate the DW-MR images that would arise from them under many imaging conditions. User-defined parameters may be adjusted to produce structures with a range of complexities that spans the levels we would expect to find in vivo. These structures are shown to contain many different features that occur in human white matter and which could confound fibre-tracking algorithms, such as tract kissing and crossing. Furthermore, combinations of such features can be sampled by the random generation of many different structures with consistent levels of complexity. The proposed software provides means for quantitative assessment via direct comparison between tracking results and the exact location of the generated fibres. This should greatly improve our understanding of algorithm performance and therefore prove an important tool for fibre tracking development.
Gov't Doc #: 19361565
URI: http://ahro.austin.org.au/austinjspui/handle/1/10803
DOI: 10.1016/j.neuroimage.2009.03.077
URL: https://pubmed.ncbi.nlm.nih.gov/19361565
Type: Journal Article
Subjects: Algorithms
Brain.anatomy & histology
Computer Simulation
Humans
Image Enhancement.methods
Image Interpretation, Computer-Assisted.methods
Magnetic Resonance Imaging.methods
Models, Anatomic
Models, Neurological
Nerve Fibers, Myelinated.ultrastructure
Pattern Recognition, Automated.methods
Reproducibility of Results
Sensitivity and Specificity
Software
Appears in Collections:Journal articles

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