Complexity in Neuro-Imaging Sample Clauses

Complexity in Neuro-Imaging. In the study of neuro-degenerative pathologies and more particularly applied to Alzheimer‟s disease, various parameters are extracted from imaging that can quantify/ qualify the disease progression/ diagnosis. Parameters such as brain volume change over time, regional changes or even white matter lesions can be extracted by applying different image processing techniques onto patients‟ brain scans. However, in almost all cases, such extractions cannot be fully automated and require the intervention of an expert to slightly tune the process and/ or clean data. Let‟s take as an example the measurement of brain atrophy rates over time, i.e. the amount of brain tissue that is lost by Alzheimer‟s patients over, say, one year. This measure is relevant in that it is the most valid marker of disease activity available to date and is ideal to test the effect of drugs aimed to slow or arrest its progression. The first step to undertake in its measurement relates to noise reduction and is aimed to reduce random variations in images due to magnetic field changes and scanner calibration. Here, the MRIcro [7] imaging toolkit is used to correct images manually by checking the homogeneity of the signal over the whole brain. This process cannot be automated and requires trained users in that inhomogeneities and other artifacts may not always be obvious to a lay eye (e.g. blood vessel may look similar to brain tissues, noise can appear around the eyes area, etc). The second step involves the digital extraction of the brain through segmentation of brain from non-brain voxels*. Here, one of the tools of the fMRIB Software Library (FSL) [8] is used, namely the Brain Extraction Tool (BET). The operator manually selects areas to be included (i.e. according to shades of xxxx, thresholding, etc) and others that should be omitted from the calculation. The obtained brain volume can be compared to a set of reference brains for diagnostic purposes, or can be registered (i.e. aligned in the 3D space) to a follow-up image to compute atrophy rate. The latter is calculated using the SIENA [9] software. This gives as output the difference of the brain contours between the baseline and the follow-up image in order to compute the actual shrinkage or increase of the brain size in quantitative terms (in cc or ml), giving a volume ratio directly indicative of the disease progression. This simple example of a given process that clinical researchers usually go through to extract meaningful imaging...
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