Introduction: Diffusion tensor imaging (DTI) is the most commonly usedtechnique to extract microstructural features from a set of diffusionweightedimages. In addition to the metrics obtained with DTI, diffusion kurtosisimaging (DKI) can provide non-Gaussian diffusion measures by means ofthe kurtosis tensor.DKI has shown to be more sensitive to tissue microstructural changes inboth normal and pathological neural tissue.In a clinical setting, however, these benefits are often nullified by numerousacquisition artifacts. The aim of this study was compare two preprocessingsoftware for DTI apply to DKI. Also, the major preprocessing,processing and post-processing procedures applied to DKI data are discussed.Materials and Methods: The reproducibility typical to DKI parameters obtainedfrom the same dataset using two DTI analysis software tools wasevaluated by the image quality measurements in regions of interest on 10DKI datasets. The data were corrected for motion and eddy current artifactsusing two different softwares: ExploreDTI (http://www.exploredti.com)and TORTOISE DIFF_PREP (https://science.nichd.nih.gov/confluence/display/nihpd/TORTOISE).The data analysis was performed using in-house developed software implementedin Python.Results: The performances of these approaches were compared with MonteCarlo simulations. A quantitative analysis of differences of typical DKI mapsobtained from data preprocessed with these two packages was performedand the advantages and disadvantages of each tool are highlighted.Conclusion: This work is aimed at providing useful indications for applicationof DKI in clinical settings where artifacts in diffusion weighted imagesare common and may affect DKI measurements and the lack of standardprocedures for post-processing might become a significant issue for theuse of DKI in clinical routine.
|Numero di pagine||1|
|Stato di pubblicazione||Published - 2016|