Abstract #1105

Flexible kt-GRAPPA-initiated compressed sensing reconstruction approach for accelerated dual-venc 5D flow imaging

Thara Nallamothu^1,2, Elizabeth Weiss^1,2, Justin Baraboo^1,2, Joshua D. Robinson³, Cynthia K. Rigsby^1,4, Christopher Roy⁵, Matthias Stuber⁵, and Michael Markl^1,2

¹Radiology, Feinberg School of Medicine, Northwestern Medicine, Chicago, IL, United States, ²Biomedical Engineering, Northwestern University, Chicago, IL, United States, ³Department of Cardiology, Ann & Robert Lurie Children’s Hospital, Chicago, IL, United States, ⁴Department of Medical Imaging, Ann & Robert Lurie Children’s Hospital, Chicago, IL, United States, ⁵Radiology, University Hospital Lausanne (CHUV)), Lausanne, Switzerland

Synopsis

Keywords: Image Reconstruction, Velocity & Flow, Image reconstruction, GRAPPA, compressed sensing, 5D flow, dual-venc, cardiovascular flow

Motivation: Dual-venc 5D flow has been proposed to capture an increased dynamic range of hemodynamics over both the cardiac and respiratory cycles. However, scan time is prohibitively long.

Goal(s): We propose a novel reconstruction method for accelerated dual-venc 5D flow that uses kt-GRAPPA-initiated compressed sensing to enable nearly 50% reduction in scan time.

Approach: We retrospectively simulated dual-venc 5D flow acceleration in N=8 healthy volunteers. Kt-GRAPPA kernels were trained on reference data and applied to fill undersampled velocity encodes before input to compressed sensing.

Results: Kt-GRAPPA-initiated compressed sensing had <10% error in flow measurements with double the acceleration compared to standard dual-venc 5D flow.

Impact: The proposed reconstruction method enables accelerated dual-venc 5D flow with approximately 50% reduction in scan time, increasing feasibility for a clinical setting. Potential applications include detailed analysis of cardiovascular hemodynamics over the respiratory cycle in patients with congenital heart disease.

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