Biexponential and diffusional kurtosis imaging, and generalised diffusion-tensor imaging (GDTI) with rank-4 tensors: a study in a group of healthy subjects

Object Clinical diffusion imaging is based on two assumptions of limited validity: that the radial projections of the diffusion propagator are Gaussian, and that a single directional diffusivity maximum exists in each voxel. The former can be removed using the biexponential and diffusional kurtosis models, the latter using generalised diffusion-tensor imaging. This study provides normative data for these three models. Materials and methods Eighteen healthy subjects were imaged. Maps of the biexponential parameters D _fast, D _slow and f _slow, of D and K from the diffusional kurtosis model, and of diffusivity D′ were obtained. Maps of generalised anisotropy (GA) and scaled entropy(SE) were also generated, for second and fourth rank tensors. Normative values were obtained for 26 regions. Results In grey versus white matter, D _slow and D′ were higher and D _fast, f _slow and K were lower. With respect to maps of D′, anatomical contrast was stronger in maps of D _slow and K. Elevating tensor rank increased SE, generally more significantly than GA, in: anterior limb of internal capsule, corpus callosum, deep frontal and subcortical white matter, along superior longitudinal fasciculus and cingulum. Conclusion The values reported herein can be used for reference in future studies and in clinical settings.     

Estimating the parameters of neural mass models including time delay and nonlinearity using a particle filter: a preliminary study toward model‐based EEG analysis

Electroencephalogram (EEG ) and local field potential (LFP ) signals are measured for both experimental and clinical purposes which include sleep stage analyses, brain–computer interfaces, and disease diagnosis. EEG and LFP data analyses are typically based on models assuming that the measured data is generated from a biological system and estimate the model parameter values that most accurately reproduce the measured data. Thus, use of a biologically plausible model is important for a model‐based analysis. However, analyses using models that include time delay and nonlinearity have not been reported, even though they are biologically important for EEG generation mechanisms. In this study, we developed a parameter estimation method that uses a particle filter for models with time delay and nonlinearity, which was evaluated with simulations. Simulated EEG data were generated from neural mass models (NMMs ). The NMM parameters were estimated from the generated data. Furthermore, parameters for modeling EEG features of patients with Alzheimer's disease were included in the NMM ; the disease parameters were estimated from the simulated EEG data. We observed that NMM parameters, as well as the disease parameters, were accurately estimated from the simulated data. We conclude that the validity of our method for estimating parameters of NMMs including time delay and nonlinearity is confirmed for simulated EEG data, and these results show the possibility of using our method for model‐based analysis with real EEG data. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Monitoring the survival of islet transplants by MRI using a novel technique for their automated detection and quantification

Object  There is a clinical need to be able to assess graft loss of transplanted pancreatic islets (PI) non-invasively with clear-cut quantification of islet survival. We tracked transplanted PI in diabetic mice during the early post-transplant period by magnetic resonance imaging (MRI) and quantified the islet loss using automatic segmentation technique. Materials and methods  Magnetically labeled islet iso-, allo- and xenografts were injected into the right liver lobes. Animals underwent MRI scanning during 14 days after PI transplantation. MR images were processed using custom-made software, which automatically detects hypointense regions representing PI. It is based on morphological top-hat and bottom-hat transforms. Results  Manually and automatically detected areas, corresponding to PI, differed by 4% in phantoms. Signal loss regions due to PI decreased comparably in all groups during the first week post transplant. Throughout the second week post-transplant, the signal loss area continued in a steep decline in case of allografts and xenografts, whereas the decline in case of isografts slowed down. Conclusion  Automatic segmentation allows for the more reproducible, objective assessment of transplanted PI. Quantification confirms the assumption that a significant number of islets are destroyed in the first week following transplantation irrespective of allografts, xenografts or isografts.     

Global hybrid modeling and control of a buck converter: A novel concept

Several attempts have been made to design suitable controllers for DC–DC converters. However, these designs suffer from model inaccuracy or their inability to desirably function in both continuous and discontinuous current modes. This paper presents a novel switching scheme based on hybrid modeling to control a buck converter using mixed logical dynamical (MLD) methodologies. The proposed method is capable of globally controlling the converter in both continuous and discontinuous current modes of operation by considering all constraints in the physical plant such as maximum inductor current and capacitor voltage limits. Different loads and input voltage disturbances are simulated in MATLAB and results are presented to demonstrate the suitability of the controller. The transient and steady‐state performance of the closed‐loop control over a wide range of operating points shows satisfactory operation of the proposed controller. Copyright © 2008 John Wiley & Sons, Ltd.