File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1002/nbm.3722
- Scopus: eid_2-s2.0-85016398824
- WOS: WOS:000405325100011
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Tensor estimation for double-pulsed diffusional kurtosis imaging
Title | Tensor estimation for double-pulsed diffusional kurtosis imaging |
---|---|
Authors | |
Keywords | brain DKI double diffusion encoding kurtosis least squares microscopic diffusion anisotropy MRI tensor |
Issue Date | 2017 |
Publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291099-1492 |
Citation | NMR in Biomedicine, 2017, v. 30 n. 7, p. e3722 How to Cite? |
Abstract | Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented. |
Persistent Identifier | http://hdl.handle.net/10722/240232 |
ISSN | 2021 Impact Factor: 4.478 2020 SCImago Journal Rankings: 1.278 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shaw, CB | - |
dc.contributor.author | Hui, SK | - |
dc.contributor.author | Helpern, JA | - |
dc.contributor.author | Jensen, JH | - |
dc.date.accessioned | 2017-04-19T08:21:38Z | - |
dc.date.available | 2017-04-19T08:21:38Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | NMR in Biomedicine, 2017, v. 30 n. 7, p. e3722 | - |
dc.identifier.issn | 0952-3480 | - |
dc.identifier.uri | http://hdl.handle.net/10722/240232 | - |
dc.description.abstract | Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented. | - |
dc.language | eng | - |
dc.publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291099-1492 | - |
dc.relation.ispartof | NMR in Biomedicine | - |
dc.rights | NMR in Biomedicine. Copyright © John Wiley & Sons Ltd. | - |
dc.rights | This is the peer reviewed version of the following article: NMR in Biomedicine, 2017, v. 30 n. 7, p. e3722, which has been published in final form at https://doi.org/10.1002/nbm.3722. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. | - |
dc.subject | brain | - |
dc.subject | DKI | - |
dc.subject | double diffusion encoding | - |
dc.subject | kurtosis | - |
dc.subject | least squares | - |
dc.subject | microscopic diffusion anisotropy | - |
dc.subject | MRI | - |
dc.subject | tensor | - |
dc.title | Tensor estimation for double-pulsed diffusional kurtosis imaging | - |
dc.type | Article | - |
dc.identifier.email | Hui, SK: edshui@hku.hk | - |
dc.identifier.authority | Hui, SK=rp01832 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1002/nbm.3722 | - |
dc.identifier.scopus | eid_2-s2.0-85016398824 | - |
dc.identifier.hkuros | 271741 | - |
dc.identifier.volume | 30 | - |
dc.identifier.issue | 7 | - |
dc.identifier.spage | e3722 | - |
dc.identifier.epage | e3722 | - |
dc.identifier.isi | WOS:000405325100011 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0952-3480 | - |