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Diffusion MRI has been widely used to assess brain tissue microstructure. However, the conventional diffusion tensor imaging (DTI) is inadequate for characterizing fiber direction or fiber density in voxels with crossing fibers in brain white matter. The constrained spherical deconvolution (CSD) technique has been proposed to measure the complex fiber orientation distribution (FOD) using a single high b-value (b ≥ 3000 s/mm) to derive the intra-axonal volume fraction (V) from the calculated FOD. Recently, the spherical mean technique (SMT) was developed to fit V directly from a multi-compartment model with multi-shell b-values. Although different numbers of b-values are needed in the two techniques, both methods have been suggested to be related to the spherical mean diffusion weighted signal (S¯). The current study compared the two techniques on the same high-quality Human Connectome Project diffusion data and investigated the relation between S¯ and V systematically. At high b-values (b ≥ 3000 s/mm), S¯ is linearly related to V, and S¯ provides similar contrast with V in white matter. At low b-values (b ~ 1000 s/mm), the linear relation between S¯ and V is sensitive to the variations of intrinsic diffusivity. These results demonstrate that S¯ measured with the typical b-value of 1000 s/mm is not an indicator of V, and previous DTI studies acquired with b = 1000 s/mm cannot be re-analyzed to provide V-weighted contrast.
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http://dx.doi.org/10.1016/j.mri.2018.11.006 | DOI Listing |
IEEE Trans Biomed Eng
September 2025
Objective: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features.
View Article and Find Full Text PDFInt J Phytoremediation
September 2025
Innovative Food Technologies Development Application and Research Center, Gölköy Campus Bolu, Bioenvironment and Green Synthesis Research Group, Bolu Abant İzzet Baysal University, Bolu, Türkiye.
This study presents an eco-friendly approach for the green synthesis of manganese oxide nanoparticles (MnONPs) using () (einkorn wheat) seed extract as a reducing and stabilizing agent. The synthesized MnONPs were characterized by UV-Vis, XRD, FTIR, SEM-EDX, BET, and zeta potential analyses, which confirmed their crystalline nature, spherical morphology, and mesoporous structure with a surface area of 41.50 m/g.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Jülich Centre for Neutron Science (JCNS-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany.
The static and dynamic properties of a cyclic Rouse chain modified by the introduction of an effective, spherically symmetric, attracting potential of entropic nature are studied. It is shown that a relatively weak potential can lead to a strong contraction of the polymer chain: the radius of gyration becomes much smaller compared to the size of the free cyclic chain. The pronounced decrease in the terminal relaxation time of cyclic macromolecules in the presence of a harmonic potential compared to the Rouse relaxation time leads to a lengthening of the time interval for the transition to the normal, i.
View Article and Find Full Text PDFComput Biol Med
September 2025
Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. Electronic address:
Modelling the diffusion-relaxation magnetic resonance (MR) signal obtained from multi-parametric sequences has recently gained immense interest in the community due to new techniques significantly reducing data acquisition time. A preferred approach for examining the diffusion-relaxation MR data is to follow the continuum modelling principle that employs kernels to represent the tissue features, such as the relaxations or diffusion properties. However, constructing reasonable dictionaries with predefined signal components depends on the sampling density of model parameter space, thus leading to a geometrical increase in the number of atoms per extra tissue parameter considered in the model.
View Article and Find Full Text PDFMagn Reson Med
September 2025
Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain.
Purpose: (a) To design a methodology for drawing random samples of any Ensemble Average Propagator (EAP) (b) to modify the KomaMRI simulator to accommodate them as realistic spin movements to simulate diffusion MRI (dMRI) and (c) to compare these simulations with those based on the Diffusion Tensor (DT) model.
Theory And Methods: The rejection method is used for random sampling of EAPs: starting from a probability law that is easily sampled, and whose density function wraps the target EAP, samples are accepted when they lie inside the targeted region. This is used to sample the EAP as described by Mean Apparent Propagator MRI (MAP-MRI) and in Spherical Convolution (SC) based on Spherical Harmonics (SH).