TY - JOUR
T1 - Auto-Regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data
AU - Metcalfe-Smith, E.
AU - Meeus, E.M.
AU - Novak, J.
AU - Dehghani, H.
AU - Peet, A.C.
AU - Zarinabad, N.
N1 - This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
PY - 2019/8/30
Y1 - 2019/8/30
N2 - Objective: A new method for fitting diffusion-weighted magnetic resonance imaging (DW-MRI) data composed of an unknown number of multi-exponential components is presented and evaluated. Methods: The auto-regressive discrete acquisition points transformation (ADAPT) method is an adaption of the auto-regressive moving average system, which allows for the modeling of multi-exponential data and enables the estimation of the number of exponential components without prior assumptions. ADAPT was evaluated on simulated DW-MRI data. The optimum ADAPT fit was then applied to human brain DWI data and the correlation between the ADAPT coefficients and the parameters of the commonly used bi-exponential intravoxel incoherent motion (IVIM) method were investigated. Results: The ADAPT method can correctly identify the number of components and model the exponential data. The ADAPT coefficients were found to have strong correlations with the IVIM parameters. ADAPT(1,1)-β0 correlated with IVIM-D: ρ = 0.708, P <; 0.001. ADAPT(1,1)-α1 correlated with IVIM-f: ρ = 0.667, P <; 0.001. ADAPT(1,1)-β1 correlated with IVIM-D * : ρ = 0.741, P <; 0.001). Conclusion: ADAPT provides a method that can identify the number of exponential components in DWI data without prior assumptions, and determine potential complex diffusion biomarkers. Significance: ADAPT has the potential to provide a generalized fitting method for discrete multi-exponential data, and determine meaningful coefficients without prior information.
AB - Objective: A new method for fitting diffusion-weighted magnetic resonance imaging (DW-MRI) data composed of an unknown number of multi-exponential components is presented and evaluated. Methods: The auto-regressive discrete acquisition points transformation (ADAPT) method is an adaption of the auto-regressive moving average system, which allows for the modeling of multi-exponential data and enables the estimation of the number of exponential components without prior assumptions. ADAPT was evaluated on simulated DW-MRI data. The optimum ADAPT fit was then applied to human brain DWI data and the correlation between the ADAPT coefficients and the parameters of the commonly used bi-exponential intravoxel incoherent motion (IVIM) method were investigated. Results: The ADAPT method can correctly identify the number of components and model the exponential data. The ADAPT coefficients were found to have strong correlations with the IVIM parameters. ADAPT(1,1)-β0 correlated with IVIM-D: ρ = 0.708, P <; 0.001. ADAPT(1,1)-α1 correlated with IVIM-f: ρ = 0.667, P <; 0.001. ADAPT(1,1)-β1 correlated with IVIM-D * : ρ = 0.741, P <; 0.001). Conclusion: ADAPT provides a method that can identify the number of exponential components in DWI data without prior assumptions, and determine potential complex diffusion biomarkers. Significance: ADAPT has the potential to provide a generalized fitting method for discrete multi-exponential data, and determine meaningful coefficients without prior information.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85071712249&partnerID=MN8TOARS
UR - https://ieeexplore.ieee.org/document/8625389
U2 - 10.1109/TBME.2019.2893523
DO - 10.1109/TBME.2019.2893523
M3 - Article
SN - 1558-2531
VL - 66
SP - 2617
EP - 2628
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 9
ER -