ex vivo diffusion mri (Bruker Corporation)
Structured Review
![(A) A view of the [ t − 1 , t + 1 ] layers of a deep feed forward neural network h V , E , σ , w . The input layer (left) is parsed against a “hidden” layer (middle) trained on annotated datasets, which corresponds to the correct output node depending on the weights obtained for each node. (B) The network architecture we propose for use in parameter estimation <t>from</t> <t>diffusion</t> <t>MRI</t> data, h v 0 ; H . In contrast to the traditional feed‐forward neural network, the weightings are checked against the preset test matrix of possible contributing signals. The weights given to each entry of this solution space are then used to generate the corresponding output node. This architecture is theoretically generalizable to any single‐ or multitensor representation of the diffusion MR signal.](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_2189/pmc12862189/pmc12862189__HBM-47-e70460-g004.jpg)
Ex Vivo Diffusion Mri, supplied by Bruker Corporation, used in various techniques. Bioz Stars score: 97/100, based on 3081 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/diffusion+mri/Diffusion/pmc12862189-178-1-16
Average 97 stars, based on 3081 article reviews
Images
1) Product Images from "Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations"
Article Title: Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations
Journal: Human Brain Mapping
doi: 10.1002/hbm.70460
Figure Legend Snippet: (A) A view of the [ t − 1 , t + 1 ] layers of a deep feed forward neural network h V , E , σ , w . The input layer (left) is parsed against a “hidden” layer (middle) trained on annotated datasets, which corresponds to the correct output node depending on the weights obtained for each node. (B) The network architecture we propose for use in parameter estimation from diffusion MRI data, h v 0 ; H . In contrast to the traditional feed‐forward neural network, the weightings are checked against the preset test matrix of possible contributing signals. The weights given to each entry of this solution space are then used to generate the corresponding output node. This architecture is theoretically generalizable to any single‐ or multitensor representation of the diffusion MR signal.
Techniques Used: Diffusion-based Assay
Figure Legend Snippet: Axial slices of DBSI and DBSIpy analysis of one representative ex vivo mouse diffusion MRI data. Note that as the b 0 SNR degrades, features in the DBSIpy estimated parameter maps retain good conspicuity. Qualitatively, DBSI parameter maps lack the same contrast‐to‐noise as their DBSIpy counterparts even in the high‐SNR regime. Furthermore, the DBSI parameter maps match or exceed the loss in quality observed in the input data.
Techniques Used: Ex Vivo, Diffusion-based Assay
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