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Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging

Coveney, Sam ; Afzali, Maryam ; Mueller, Lars ; Teh, Irvin ; Szczepankiewicz, Filip LU orcid ; Jones, Derek K. and Schneider, Jürgen E. (2026) In Magnetic Resonance in Medicine 95(1). p.220-233
Abstract

Purpose: Cardiac diffusion tensor imaging (cDTI) can investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation such as diffusion kurtosis imaging (DKI). cDTI is prone to image corruption, which is usually treated with shot rejection but which can be handled more generally with robust estimation. Unconstrained fitting allows DKI parameters to violate necessary constraints on signal behavior, causing errors in diffusion and kurtosis measures. Methods: We developed robust constrained weighted least squares (RCWLS) specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom... (More)

Purpose: Cardiac diffusion tensor imaging (cDTI) can investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation such as diffusion kurtosis imaging (DKI). cDTI is prone to image corruption, which is usually treated with shot rejection but which can be handled more generally with robust estimation. Unconstrained fitting allows DKI parameters to violate necessary constraints on signal behavior, causing errors in diffusion and kurtosis measures. Methods: We developed robust constrained weighted least squares (RCWLS) specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner up to b-value (Formula presented.), we compared fitting techniques with/without robustness and with/without constraints. Results: Constraints, but not robustness, made a significant difference on all measures. Robust fitting corrected large errors for some subjects. RCWLS was the only technique that showed radial kurtosis to be larger than axial kurtosis for all subjects, which is expected in myocardium due to increased restrictions to diffusion perpendicular to the primary myocyte direction. For (Formula presented.), RCWLS gave the following measures across subjects: mean diffusivity (MD) (Formula presented.), fractional anisotropy (FA) (Formula presented.), mean kurtosis (MK) (Formula presented.), axial kurtosis (AK) (Formula presented.), radial kurtosis (RK) (Formula presented.), and RK/AK (Formula presented.). Conclusion: Fitting techniques utilizing both robust estimation and convexity constraints, such as RCWLS, are essential to obtain robust and feasible diffusion and kurtosis measures from in vivo cardiac DKI.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
cardiac diffusion tensor imaging, constrained estimation, diffusion kurtosis imaging, magnetic resonance imaging, robust estimation
in
Magnetic Resonance in Medicine
volume
95
issue
1
pages
220 - 233
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:40851293
  • scopus:105013992540
ISSN
0740-3194
DOI
10.1002/mrm.70037
language
English
LU publication?
yes
id
80274a43-ac9b-4691-b49a-28eb5f541e1a
date added to LUP
2025-11-17 14:01:40
date last changed
2025-12-19 16:16:32
@article{80274a43-ac9b-4691-b49a-28eb5f541e1a,
  abstract     = {{<p>Purpose: Cardiac diffusion tensor imaging (cDTI) can investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation such as diffusion kurtosis imaging (DKI). cDTI is prone to image corruption, which is usually treated with shot rejection but which can be handled more generally with robust estimation. Unconstrained fitting allows DKI parameters to violate necessary constraints on signal behavior, causing errors in diffusion and kurtosis measures. Methods: We developed robust constrained weighted least squares (RCWLS) specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner up to b-value (Formula presented.), we compared fitting techniques with/without robustness and with/without constraints. Results: Constraints, but not robustness, made a significant difference on all measures. Robust fitting corrected large errors for some subjects. RCWLS was the only technique that showed radial kurtosis to be larger than axial kurtosis for all subjects, which is expected in myocardium due to increased restrictions to diffusion perpendicular to the primary myocyte direction. For (Formula presented.), RCWLS gave the following measures across subjects: mean diffusivity (MD) (Formula presented.), fractional anisotropy (FA) (Formula presented.), mean kurtosis (MK) (Formula presented.), axial kurtosis (AK) (Formula presented.), radial kurtosis (RK) (Formula presented.), and RK/AK (Formula presented.). Conclusion: Fitting techniques utilizing both robust estimation and convexity constraints, such as RCWLS, are essential to obtain robust and feasible diffusion and kurtosis measures from in vivo cardiac DKI.</p>}},
  author       = {{Coveney, Sam and Afzali, Maryam and Mueller, Lars and Teh, Irvin and Szczepankiewicz, Filip and Jones, Derek K. and Schneider, Jürgen E.}},
  issn         = {{0740-3194}},
  keywords     = {{cardiac diffusion tensor imaging; constrained estimation; diffusion kurtosis imaging; magnetic resonance imaging; robust estimation}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{220--233}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Magnetic Resonance in Medicine}},
  title        = {{Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging}},
  url          = {{http://dx.doi.org/10.1002/mrm.70037}},
  doi          = {{10.1002/mrm.70037}},
  volume       = {{95}},
  year         = {{2026}},
}