Review



diffusion mri  (Siemens Healthineers)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 86
      Buy from Supplier

    Structured Review

    Siemens Healthineers diffusion mri
    Diffusion Mri, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/diffusion+gaussian+models+mri+non/pm41989408-147-9-18
    Average 86 stars, based on 1 article reviews
    diffusion mri - by Bioz Stars, 2026-10
    86/100 stars

    Images

    Related Articles

    Magnetic Resonance Imaging:

    Article Title: Interrogating cognitive and neural group variation in childhood: Examples in adolescent brain cognitive development and Oregon attention-deficit/hyperactivity disorder-1000 cohorts.
    Article Snippet: .. Children completed high-resolution T1- and T2-weighted structural MRI and diffusion MRI using scanners from GE Healthcare (Waukesha, Wisconsin), Siemens Healthcare (Erlangen, Germany), or Philips Healthcare (Andover, Massachusetts). ..

    Article Title: Non-Gaussian diffusion MRI models for preoperative assessment of microvascular invasion in hepatocellular carcinoma.
    Article Snippet: 1 Department of Radiology, First Hospital of China Medical University, Shenyang, China 2 Siemens Healthineers Ltd, MR Research Collaboration Team, Beijing, China Abstract Purpose This study aims to assess the potential value of the non-Gaussian diffusion MRI models, including intravoxel incoherent motion (IVIM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).. Methods A total of 61 consecutive patients were enrolled in this study.. Various diffusion parameters were calculated from six diffusion models: conventional diffusion-weighted imaging (DWI) and five non-Gaussian diffusion MRI models (IVIM, SEM, DKI, FROC, and CTRW).

    Diffusion-based Assay:

    Article Title: Interrogating cognitive and neural group variation in childhood: Examples in adolescent brain cognitive development and Oregon attention-deficit/hyperactivity disorder-1000 cohorts.
    Article Snippet: .. Children completed high-resolution T1- and T2-weighted structural MRI and diffusion MRI using scanners from GE Healthcare (Waukesha, Wisconsin), Siemens Healthcare (Erlangen, Germany), or Philips Healthcare (Andover, Massachusetts). ..

    Article Title: Non-Gaussian diffusion MRI models for preoperative assessment of microvascular invasion in hepatocellular carcinoma.
    Article Snippet: 1 Department of Radiology, First Hospital of China Medical University, Shenyang, China 2 Siemens Healthineers Ltd, MR Research Collaboration Team, Beijing, China Abstract Purpose This study aims to assess the potential value of the non-Gaussian diffusion MRI models, including intravoxel incoherent motion (IVIM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).. Methods A total of 61 consecutive patients were enrolled in this study.. Various diffusion parameters were calculated from six diffusion models: conventional diffusion-weighted imaging (DWI) and five non-Gaussian diffusion MRI models (IVIM, SEM, DKI, FROC, and CTRW).

    other:

    Article Title: Incorporating parenchymal heterogeneity into FLIS to improve MRI-based liver function assessment
    Article Snippet: Dynamic upper-abdominal MRI was performed on 1.5-T and 3-T systems (Magnetom Aera and Skyra, Siemens Healthineers) using the department’s routine protocol, which also included diffusion-weighted imaging.

    Imaging:

    Article Title: Non-Gaussian diffusion MRI models for preoperative assessment of microvascular invasion in hepatocellular carcinoma.
    Article Snippet: 1 Department of Radiology, First Hospital of China Medical University, Shenyang, China 2 Siemens Healthineers Ltd, MR Research Collaboration Team, Beijing, China Abstract Purpose This study aims to assess the potential value of the non-Gaussian diffusion MRI models, including intravoxel incoherent motion (IVIM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), fractional-order calculus (FROC), and continuous-time random walk (CTRW) models, for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).. Methods A total of 61 consecutive patients were enrolled in this study.. Various diffusion parameters were calculated from six diffusion models: conventional diffusion-weighted imaging (DWI) and five non-Gaussian diffusion MRI models (IVIM, SEM, DKI, FROC, and CTRW).



    Similar Products

    diffusion mri  (Philips Healthcare)
    86
    Philips Healthcare diffusion mri
    Diffusion Mri, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/angiography+coronary+quantitative/pm41989408-147-9-23
    Average 86 stars, based on 1 article reviews
    diffusion mri - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    diffusion mri  (Siemens Healthineers)
    86
    Siemens Healthineers diffusion mri
    Diffusion Mri, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/diffusion+gaussian+models+mri+non/pm41989408-147-9-18
    Average 86 stars, based on 1 article reviews
    diffusion mri - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    diffusion mri  (Janssen)
    86
    Janssen diffusion mri
    Diffusion Mri, supplied by Janssen, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/mri/pm41946011-412-0-21
    Average 86 stars, based on 1 article reviews
    diffusion mri - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    ex vivo diffusion mri  (Bruker Corporation)
    97
    Bruker Corporation ex vivo diffusion mri
    (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.
    Ex Vivo Diffusion Mri, supplied by Bruker Corporation, used in various techniques. Bioz Stars score: 97/100, based on 1 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 1 article reviews
    ex vivo diffusion mri - by Bioz Stars, 2026-10
    97/100 stars
    		  Buy from Supplier
    diffusion weighted dw mri data  (Bruker Corporation)
    97
    Bruker Corporation diffusion weighted dw mri data
    (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.
    Diffusion Weighted Dw Mri Data, supplied by Bruker Corporation, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/Diffusion/pmc12803670-48-0-19
    Average 97 stars, based on 1 article reviews
    diffusion weighted dw mri data - by Bioz Stars, 2026-10
    97/100 stars
    		  Buy from Supplier
    covering 343 diffusion weighted mri scans  (Dawley Inc)
    86
    Dawley Inc covering 343 diffusion weighted mri scans
    (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.
    Covering 343 Diffusion Weighted Mri Scans, supplied by Dawley Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/343+covering+diffusion+mri+scans+weighted/pmc12746846-171-0-9
    Average 86 stars, based on 1 article reviews
    covering 343 diffusion weighted mri scans - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    multi shell diffusion weighted mri  (Philips Healthcare)
    86
    Philips Healthcare multi shell diffusion weighted mri
    (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.
    Multi Shell Diffusion Weighted Mri, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/diffusion+mri+multi+shell+weighted/pm41510957-1192-2-6
    Average 86 stars, based on 1 article reviews
    multi shell diffusion weighted mri - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    multi shell diffusion mri  (Philips Healthcare)
    86
    Philips Healthcare multi shell diffusion mri
    (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.
    Multi Shell Diffusion Mri, supplied by Philips Healthcare, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/diffusion+mri+multi+shell+weighted/10__1097_slash_nrl__0000000000000646-60-8-21
    Average 86 stars, based on 1 article reviews
    multi shell diffusion mri - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    non gaussian diffusion mri models  (Siemens Healthineers)
    86
    Siemens Healthineers non gaussian diffusion mri models
    (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.
    Non Gaussian Diffusion Mri Models, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/diffusion+gaussian+models+mri+non/pm41081881-0-34-13
    Average 86 stars, based on 1 article reviews
    non gaussian diffusion mri models - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    diffusion mri data  (Bruker Corporation)
    97
    Bruker Corporation diffusion mri data
    (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.
    Diffusion Mri Data, supplied by Bruker Corporation, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+mri/Diffusion/pm41074918-52-2-14
    Average 97 stars, based on 1 article reviews
    diffusion mri data - by Bioz Stars, 2026-10
    97/100 stars
    		  Buy from Supplier

    Image Search Results


    (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.

    Journal: Human Brain Mapping

    Article Title: Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations

    doi: 10.1002/hbm.70460

    Figure Lengend 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.

    Article Snippet: The ex vivo diffusion MRI experiments conducted for this work were performed on a 9.4 T Bruker BioSpec small‐animal MRI system equipped with a volume transmitter coil (RF RES 400 1H 112/086 QSN TO AD) and a H 2 × 2 mouse brain surface array receiver coil (RF ARR 400 1H M.BR.

    Techniques: Diffusion-based Assay

    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.

    Journal: Human Brain Mapping

    Article Title: Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations

    doi: 10.1002/hbm.70460

    Figure Lengend 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.

    Article Snippet: The ex vivo diffusion MRI experiments conducted for this work were performed on a 9.4 T Bruker BioSpec small‐animal MRI system equipped with a volume transmitter coil (RF RES 400 1H 112/086 QSN TO AD) and a H 2 × 2 mouse brain surface array receiver coil (RF ARR 400 1H M.BR.

    Techniques: Ex Vivo, Diffusion-based Assay