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Computer applications, Oncology, eHealth, MR-Diffusion/Perfusion, MR-Functional imaging, Computer Applications-Detection, diagnosis
G. C. Manikis1, K. Nikiforaki1, N. Papanikolaou2, C. Matos2, K. Marias1; 1Heraklion/GR, 2Lisbon/PT
Methods and materials
Import raw medical imaging data
The platform supports image import from most widely used MRI scanners,
and accesses the DICOM header in order to retrieve information on the acquisition protocol.
Region of Interest (ROI) delineation
Once data are imported properly,
the platform allows interaction with the user for selecting the ROI(s) for analysis Fig. 1.
ROI delineation is performed either manually by contour drawing within the slices or by importing the ROI(s) that are stored locally as medical imaging files (i.e.
Manual drawing is assisted with tools for: a) adjusting the levels of the image,
and b) navigation through the b-value acquisitions and the slices of the data to best determine the site of interest for analysis.
Pre-processing of the DW-MRI data
The platform is equipped with fully automated filters that are applied iteratively to multiple 3D data acquired at different b-values and reduce noise without affecting signal abundant areas.
DW-MRI model fitting
Diffusion analysis is based on a pixel-by-pixel basis using Gaussian mono-exponential model ,
the Intravoxel Incoherent Motion (IVIM) ,
and the non-Gaussian mono- and bi-exponential kurtosis [3-4].
The platform also supports statistical analysis to determine the fitting accuracy of each model on the data.
b-value specific SNR maps are generated in order to provide pixel-based information about data quality.