Auto-regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data

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Auto-regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data. / Metcalfe-Smith, Emma; Meeus, Emma; Novak, Jan; Dehghani, Hamid; Peet, Andrew; Zarinabad, Niloufar.

In: IEEE Transactions on Biomedical Engineering, Vol. 66, No. 9, 09.2019, p. 2617-2628.

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@article{cb5d370554e94872b2836e9c67d8b5e7,
title = "Auto-regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data",
abstract = "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.",
author = "Emma Metcalfe-Smith and Emma Meeus and Jan Novak and Hamid Dehghani and Andrew Peet and Niloufar Zarinabad",
year = "2019",
month = sep,
doi = "10.1109/TBME.2019.2893523",
language = "English",
volume = "66",
pages = "2617--2628",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "9",

}

RIS

TY - JOUR

T1 - Auto-regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data

AU - Metcalfe-Smith, Emma

AU - Meeus, Emma

AU - Novak, Jan

AU - Dehghani, Hamid

AU - Peet, Andrew

AU - Zarinabad, Niloufar

PY - 2019/9

Y1 - 2019/9

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?scp=85071712249&partnerID=8YFLogxK

U2 - 10.1109/TBME.2019.2893523

DO - 10.1109/TBME.2019.2893523

M3 - Article

C2 - 30676937

VL - 66

SP - 2617

EP - 2628

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 9

ER -