
PurposeAn emerging topic in diffusion magnetic resonance is imaging blood microcirculation alongside water diffusion using the intravoxel incoherent motion (IVIM) model. Recently, a combined IVIM diffusion tensor imaging (IVIM‐DTI) model was proposed, which accounts for both anisotropic pseudo‐diffusion due to blood microcirculation and anisotropic diffusion due to tissue microstructures. In this article, we propose a robust IVIM‐DTI approach for simultaneous diffusion and pseudo‐diffusion tensor imaging.MethodsConventional IVIM estimation methods can be broadly divided into two‐step (diffusion and pseudo‐diffusion estimated separately) and one‐step (diffusion and pseudo‐diffusion estimated simultaneously) methods. Here, both methods were applied on the IVIM‐DTI model. An improved one‐step method based on damped Gauss–Newton algorithm and a Gaussian prior for the model parameters was also introduced. The sensitivities of these methods to different parameter initializations were tested with realistic in silico simulations and experimental in vivo data.ResultsThe one‐step damped Gauss–Newton method with a Gaussian prior was less sensitive to noise and the choice of initial parameters and delivered more accurate estimates of IVIM‐DTI parameters compared to the other methods.ConclusionOne‐step estimation using damped Gauss–Newton and a Gaussian prior is a robust method for simultaneous diffusion and pseudo‐diffusion tensor imaging using IVIM‐DTI model. Magn Reson Med 79:2367–2378, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Computer. Automation, Microcirculation, diffusion, Normal Distribution, Brain, Reproducibility of Results, Signal-To-Noise Ratio, perfusion, Healthy Volunteers, Full Papers—Computer Processing and Modeling, Motion, Magnetic resonance imaging, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Image Interpretation, Computer-Assisted, Anisotropy, Humans, Computer Simulation, QC, Algorithms, intravoxel incoherent motion, QB
Computer. Automation, Microcirculation, diffusion, Normal Distribution, Brain, Reproducibility of Results, Signal-To-Noise Ratio, perfusion, Healthy Volunteers, Full Papers—Computer Processing and Modeling, Motion, Magnetic resonance imaging, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Image Interpretation, Computer-Assisted, Anisotropy, Humans, Computer Simulation, QC, Algorithms, intravoxel incoherent motion, QB
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
