Diffusion Tensor Imaging quality control : artifacts assessment and correction. A. Coste, S. Gouttard, C. Vachet, G. Gerig. Medical Imaging Seminar
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1 Diffusion Tensor Imaging quality control : artifacts assessment and correction A. Coste, S. Gouttard, C. Vachet, G. Gerig Medical Imaging Seminar
2 Overview Introduction DWI DTI Artifact Assessment Artifact Consequences Artifact Detection Possible Correction Conclusion 2
3 Introduction DTI is a powerful technique giving insights into white matter organization Allows Tractography Increased Artifacts Reduced Signal to Noise Ratio Need of Quality Control Need of new processing methods to process DTI due to new kind of artifacts 3
4 Diffusion Weighted Imaging Acquisition of multiple set of MRI with several values of the exciting magnetic field and with different orientation. In our protocol we use 25 different directions b value direction 4
5 Diffusion Tensor Imaging DTI is computed with an overdetermined system based on all images of the DWI set. Each voxel contains a second order diffusion Tensor : D is symmetric and positive definite To visualize the tensor we use Eigen decomposition to get the local diffusion characteristics 5
6 DTI Visualization Ellipsoid parametrization with eigen vectors and eigenvalues Interpretation of diffusion properties 6
7 DTI Visualization 7
8 Fractional Anisotropy Fractional Anisotropy (FA) 0 1 8
9 Artifact Assessment Quantitative assessment Unexpected evolution of FA over time Diffusion evolution inconsistent Qualitative assessment blurry FA images Red Color FA images Red Tensors FA 9 age
10 Artifact Assessment Quantitative assessment Unexpected evolution of FA over time Diffusion evolution inconsistent Qualitative assessment blurry FA images Red Color FA images Red Tensors FA Position on fiber 10
11 Artifact Assessment blurry FA images Good case Bad case 11
12 Artifact Assessment Red ColorFA images Good case Bad case 12
13 Artifact Assessment Red Tensors Good case Bad case 13
14 Artifact Assessment Quantitative assessment Unexpected evolution of FA over time Diffusion evolution inconsistent Qualitative assessment blurry FA images Red Color FA images Red Tensors Something is wrong but what? Processing Acquisition 14
15 Artifact Consequences Wrong calculation of diffusion characteristics Poor quality tensors used in atlas building Unknown influence of it in atlas building Reliability of conclusions based on these data 15
16 Statistical Assessment Our data set is composed of DWI sets 4 acquisition sites across the US : ACE / IBIS Network University of North Carolina at Chapel Hill University of Washington in Seattle Washington University in St. Louis Children Hospital of Pennsylvania in Philadelphia Artifact ratio within the data set : 16
17 Artifact Detection Visual QC ColorFA-OnlineQuickViewer 17
18 Artifact Detection Visual Quality Control 18
19 Artifact Detection Principal Direction Space Principal Eigen Vector Field 19
20 Artifact Detection Principal Direction Spherical Histogram Entropy Based Diffusion Imaging Quality Control via Principal Orientation Distribution : M. Farzinfar et al 20
21 Artifact Detection Entropy Entropy case 21
22 Possible Correction The Leave One Out Method (LOO) on DWI set 22
23 Possible Correction Leave One Out improved results : 23
24 Possible Correction Analysis with Principle Direction Distribution 24
25 Possible Correction Leave Multiple Out Method (LMO) LOO is not generalizable Ongoing research Combination of directions depending on patient position Influence of each gradient direction Computation of spherical Voronoi Tessellation 25
26 Conclusion Identify and characterize directional artifact Possible automated detection via entropy Possible correction with Leave One / Multiple Out Further investigation on acquisition Overall Objective: prevent the artifact occurrence 26
27 Acknowledgment I would like to thank : Dr Guido Gerig Sylvain Gouttard Clement Vachet People in Utah, University of North Carolina and at the Montreal Neurological Institute Everyone for your attention 27
28 Questions Questions? 28
29 Tensor Estimation Method 29
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