Free-running SIMilarity-Based Angiography (SIMBA) for simplified anatomical MR imaging of the heart
John Heerfordt, Kevin K. Whitehead, Jessica A.M. Bastiaansen, Lorenzo, Di Sopra, Christopher W. Roy, J\'er\^ome Yerly, Bastien Milani, Mark A., Fogel, Matthias Stuber, Davide Piccini

TL;DR
This paper introduces SIMBA, a fast, similarity-based reconstruction algorithm for ungated free-running cardiac MRI that improves image sharpness and contrast without relying on physiological motion assumptions.
Contribution
The paper presents a novel, rapid reconstruction method called SIMBA that leverages data similarities to produce high-quality, motion-suppressed whole-heart MRA without gating.
Findings
SIMBA achieves comparable image quality to existing methods in about 20 seconds.
SIMBA provides higher blood-myocardium contrast ratio than traditional and other free-running methods.
More coronary ostia are visible with SIMBA than with non-motion-corrected data.
Abstract
Purpose: Whole-heart MRA techniques typically target pre-determined motion states and address cardiac and respiratory dynamics independently. We propose a novel fast reconstruction algorithm, applicable to ungated free-running sequences, that leverages inherent similarities in the acquired data to avoid such physiological constraints. Theory and Methods: The proposed SIMilarity-Based Angiography (SIMBA) method clusters the continuously acquired k-space data in order to find a motion-consistent subset that can be reconstructed into a motion-suppressed whole-heart MRA. Free-running 3D radial datasets from six ferumoxytol-enhanced scans of pediatric cardiac patients and twelve non-contrast scans of healthy volunteers were reconstructed with a non-motion-suppressed regridding of all the acquired data (All Data), our proposed SIMBA method, and a previously published free-running framework…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
