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Abstract #2650

Single-spiral MRE image reconstruction using zero-shot self-supervised deep learning towards real-time stiffness mapping

Stefan Martin1, Felix Frederik Zimmermann1, Jakob Schattenfroh2, Patrick Schuenke1, Ingolf Sack2, Christoph Kolbitsch1, and Andreas Kofler1
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 2Charité - Universitätsmedizin Berlin, Berlin, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, Elastography

Motivation: MR elastography provides important clinical information but is time consuming. Single-spiral acquisitions can speed up data acquisition but require advanced image reconstruction schemes. State-of-the-art deep learning methods require high-quality training data which is often not available for MRE.

Goal(s): Developing zero-shot self-supervised deep learning approach towards real-time MRE from single-spiral acquisitions.

Approach: We developed a PyPulseq spiral MRE sequence with a self-supervised deep learning reconstruction for single-subject data. This approach leverages inherent similarities and redundancies within MRE scans and incorporates a GPU-accelerated k-MDEV inversion.

Results: Precise elastograms from single spiral arm MRE aquistions with a PyPulseq MRE sequence.

Impact: The proposed self-supervised reconstruction ensures accurate elastogram estimation from highly undersampled data, reducing scan time. These developments promise to further enhance MRE’s impact and efficiency in clinical practice.

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Keywords