
PurposeFree‐water elimination DTI (FWE‐DTI) has been used widely to distinguish increases of free‐water partial‐volume effects from tissue’s diffusion in healthy aging and degenerative diseases. Because the FWE‐DTI fitting is only well‐posed for multishell acquisitions, a regularized gradient descent (RGD) method was proposed to enable application to single‐shell data, more common in the clinic. However, the validity of the RGD method has been poorly assessed. This study aims to quantify the specificity of FWE‐DTI procedures on single‐shell and multishell data.MethodsDifferent FWE‐DTI fitting procedures were tested on an open‐source in vivo diffusion data set and single‐shell and multishell synthetic signals, including the RGD and standard nonlinear least‐squares methods. Single‐voxel simulations were carried out to compare initialization approaches. A multivoxel phantom simulation was performed to evaluate the effect of spatial regularization when comparing between methods. To test the algorithms’ specificity, phantoms with two different types of lesions were simulated: with altered mean diffusivity or with modified free water.ResultsPlausible parameter maps were obtained with RGD from single‐shell in vivo data. The plausibility of these maps was shown to be determined by the initialization. Tests with simulated lesions inserted into the in vivo data revealed that the RGD approach cannot distinguish free water from tissue mean‐diffusivity alterations, contrarily to the nonlinear least‐squares algorithm.ConclusionThe RGD FWE‐DTI method has limited specificity; thus, its results from single‐shell data should be carefully interpreted. When possible, multishell acquisitions and the nonlinear least‐squares approach should be preferred instead.
Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Biological Physics (physics.bio-ph), Brain, Water, FOS: Physical sciences, Physics - Biological Physics, Medical Physics (physics.med-ph), Physics - Medical Physics, Algorithms
Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Biological Physics (physics.bio-ph), Brain, Water, FOS: Physical sciences, Physics - Biological Physics, Medical Physics (physics.med-ph), Physics - Medical Physics, Algorithms
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