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Abstract #3047

Robust estimation with convexity constraints for improved diffusion kurtosis imaging of the human heart in vivo

Sam Coveney1, Maryam Afzali1, Lars Mueller1, Tom Dela Haije2, Irvin Teh1, Filip Szczepankiewicz3, Derek Jones4, and Jurgen E Schneider1
1Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, 2University of Copenhagen, Copenhagen, Denmark, 3Medical Radiation Physics, Lund University, Lund, Sweden, 4Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom

Synopsis

Keywords: DWI/DTI/DKI, DWI/DTI/DKI

Motivation: Fitting signal representations to multi-shell diffusion data may require constraints to avoid violating principles such as diffusion propagator non-negativity. However, constrained fitting is not intrinsically robust to signal corruptions, which are common in cardiac diffusion weighted imaging.

Goal(s): We sought to determine how convexity constraints and robust estimation affect cardiac Diffusion Kurtosis Imaging (cDKI) measures.

Approach: We developed fitting methods combining robust estimation with convexity constraints. We applied these methods to healthy volunteer data from a Connectom scanner.

Results: Constrained fitting improved kurtosis measures and gave more physiologically plausible results compared to unconstrained fitting. Robust constrained fitting yielded the best results overall.

Impact: Robust estimation has now been combined with fitting constraints, allowing for reliable estimation of kurtosis measures from in vivo multi-shell cardiac diffusion MRI data. This approach will improve fidelity of kurtosis quantification in the human heart in vivo.

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Keywords