Aims and objectives
Structural MRI (sMRI) of the brain in the clinical environment usually consist in a qualitative analysis,
in which the neuroradiologist detects deviations or anomalies by visual inspection.
A quantitative analysis can complement the specialist's opinion,
allowing a comparative study among subjects.
to perform a reliable analysis,
it is necessary to consider each measure’s distribution in the healthy population.
In this work we used a large sample from several public databases to study the influence of acquisition parameters on the measurement of neuromorphometric features computed...
Methods and materials
FreeSurfer  is a software package that can enhance the visual information in resonance images by means of performing structural measurements.
Using FreeSurfer v6.0,
2700 MRIs were processed and 272 morphometric features that characterize each brain's cortical and subcortical anatomy were obtained.
An example of FreeSurfer segmentation is shown in Figure 2,
where each colour represents a cortical or subcortical structure.
From the segmentation of these structures,
area and volume values can be measured.
The effects of the different acquisition parameters on the morphological...
The use of different acquisition protocols induces spurious patterns in the data.
In other words,
different protocols produce a bias in the morphologic measurements.
Using random forest classifiers,
we determined that the influence of the field strength is more significant than that of the MRI-machine manufacturer.
Images obtained using different field strengths can be easily separated with this type of classifier.
Figure 4 shows the 1.5 T and 3 T separation when applying a dimensionality reduction technique called t-SNE .
A harmonization algorithm was developed...
FreeSurfer’s morphometric measurements are influenced by the MRI acquisition parameters.
we introduced a method to remove those effects by using standardization techniques,
allowing the translation of automatic quantitative MRI analysis from research to clinical applications.
We developed a technique for harmonizing data extracted from sMRI acquired using different field strengths and measured its effectiveness in terms of the ability to confound a random forest classifier.
Our method consisted in calculating a 10-year-bin z-score for each class (1.5 T and 3.0 T).
Our approach succeeds...
et al (2011).
Scikit-learn: Machine Learning in Python,
Journal of Machine Learning Research,
The changing brain in healthy aging: A multi-MRI machine and multicenter surface-based morphometry study.
In Proceedings of SPIE - The International Society for Optical Engineering.
Article number 101600B.
& Alzheimers Disease Neuroimaging Initiative.
Looking for Alzheimer's...