Extended neighbourhood based linear reconstruction of Diffusion Kurtosis Imaging
- Resource Type
- Conference
- Authors
- Raja, Rajikha; Sinha, Neelam; Saini, Jitender
- Source
- TENCON 2015 - 2015 IEEE Region 10 Conference. :1-6 Nov, 2015
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Tensile stress
Estimation
Image reconstruction
Diffusion tensor imaging
Correlation
Robustness
Least squares approximations
Diffusion Weighted MRI
Diffusion Tensor Imaging
Diffusion Kurtosis Imaging
Linear Least Squares Estimation
Neighbourhood correlation
- Language
- ISSN
- 2159-3442
2159-3450
Diffusion weighted magnetic resonance imaging(DW-MRI) is used for the quantification of water diffusion with the availability of various tensor based models such as Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI). The increased significance of DKI parameters for the assessment of neurologic diseases as compared to DTI parameters has been shown in several recent studies. Kurtosis tensors were reconstructed using either linear or non-linear least squares approaches including several variants of these approaches. In this work, we proposed an extended linear least squares(LLS) reconstruction of DKI parameters which makes use of the correlation existing in the DW-MRI data in order to have a robust and accurate estimation of the kurtosis parameters. All the available methods of DKI reconstruction uses an independent voxel-wise estimation of the kurtosis parameters. The proposed method attempts to make use of the spatial correlation in DW-MRI by including the neighbourhood voxels for the estimation of kurtosis parameters voxel-wise. Our study includes simulation and real data experiments for validation of the proposed method. The estimation from the proposed method revealed better details and accuracy as compared to LLS and weighted LLS approaches. The proposed method is also robust to noise which is illustrated by using noise corrupted data for different levels of added rician noise.