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

Coveney, Sam, Afzali, Maryam, Mueller, Lars, Teh, Irvin, Szczepankiewicz, Filip, Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049 and Schneider, Jurgen E. 2026. Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging. Magnetic Resonance in Medicine 95 (1) , pp. 220-233. 10.1002/mrm.70037

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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. 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Psychology Research Institutes & Centres > Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Wiley
ISSN:	0740-3194
Date of First Compliant Deposit:	7 August 2025
Date of Acceptance:	29 July 2025
Last Modified:	02 Dec 2025 15:36
URI:	https://orca.cardiff.ac.uk/id/eprint/180288

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