Object: We propose a new tracking method based on time-of-arrival (TOA) maps derived from simulated diffusion processes.
Materials And Methods: The proposed diffusion simulation-based tracking consists of three steps that are successively evaluated on small overlapping sub-regions in a diffusion tensor field. First, the diffusion process is simulated for several time steps. Second, a TOA map is created to store simulation results for the individual time steps that are required for the tract reconstruction. Third, the fiber pathway is reconstructed on the TOA map and concatenated between neighboring sub-regions. This new approach is compared with probabilistic and streamline tracking. All methods are applied to synthetic phantom data for an easier evaluation of their fiber reconstruction quality.
Results: The comparison of the tracking results did show severe problems for the streamline approach in the reconstruction of crossing fibers, for example. The probabilistic method was able to resolve the crossing, but could not handle strong curvature. The new diffusion simulation-based tracking could reconstruct all problematic fiber constellations.
Conclusion: The proposed diffusion simulation-based tracking method used the whole tensor information of a neighborhood of voxels and is, therefore, able to handle problematic tracking situations better than established methods.
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http://dx.doi.org/10.1007/s10334-009-0195-x | DOI Listing |
Sci Rep
December 2024
School of Electrical Engineering, Kookmin University, Seoul, 02707, Republic of Korea.
This study optimizes V and ΔV in amorphous indium-gallium-zinc-oxide (a-IGZO) field-effect transistors (FETs) by examining the influence of both channel length (L) and Ga composition. It was observed that as the ratio of In: Ga: Zn changed from 1:1:1 to 0.307:0.
View Article and Find Full Text PDFbioRxiv
December 2024
Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pre-trained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific p values, achieving high accuracy and generalizability.
View Article and Find Full Text PDFJ Prof Nurs
December 2024
Massachusetts General Hospital Institute of Health Professions, Boston, USA; British Columbia Institute of Technology, School of Health Sciences, Burnaby, Canada.
Background: Nursing education has seen a shift towards simulation-based education (SBE) to meet the demands of a rapidly evolving healthcare landscape. Maryland's Clinical Simulation Resource Consortium (MCSRC) aimed to enhance SBE utilization; however, noted a decline in SBE usage post pandemic, prompting an investigation into nurse administrators' perspectives on replacing clinical hours with SBE.
Methods: This descriptive quality improvement study was informed by Rogers' Diffusion of Innovation (DOI) Theory.
Magn Reson Med
November 2024
Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, The University of Nottingham, Nottingham, UK.
Purpose: To investigate and explain observed features of the placental DWI signal in healthy and compromised pregnancies using a mathematical model of maternal blood flow.
Methods: Thirteen healthy and nine compromised third trimester pregnancies underwent pulse gradient spin echo DWI MRI, with the results compared to MRI data simulated from a 2D mathematical model of maternal blood flow through the placenta. Both sets of data were fitted to an intravoxel incoherent motion (IVIM) model, and a rebound model (defined within text), which described voxels that did not decay monotonically.
Elife
November 2024
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom.
This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.
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