Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.

Bertleff, Marco, Domsch, Sebastian, Weingärtner, Sebastian, Zapp, Jascha, O'Brien, Kieran, Barth, Markus and Schad, Lothar R (2017) Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.. NMR in biomedicine, 30 12: . doi:10.1002/nbm.3833


Author Bertleff, Marco
Domsch, Sebastian
Weingärtner, Sebastian
Zapp, Jascha
O'Brien, Kieran
Barth, Markus
Schad, Lothar R
Title Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.
Journal name NMR in biomedicine   Check publisher's open access policy
ISSN 1099-1492
Publication date 2017-12-01
Sub-type Article (original research)
DOI 10.1002/nbm.3833
Open Access Status Not yet assessed
Volume 30
Issue 12
Abstract Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times.
Keyword artificial neural network
diffusion
intravoxel incoherent motion
kurtosis
machine learning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Pubmed Import
 
Versions
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Created: Wed, 15 Nov 2017, 13:48:35 EST