Motion-robust free-running volumetric cardiovascular MRI

TL;DR

This paper introduces CORe, a novel outlier mitigation method for free-running cardiovascular MRI that improves image quality and flow measurement accuracy by effectively suppressing motion artifacts through joint estimation and physics-guided sparsity.

Contribution

The paper presents CORe, a new outlier rejection technique that enhances motion robustness in free-running CMR by modeling and jointly estimating outliers with MR physics constraints.

Findings

CORe outperforms traditional methods in simulations with lower error and higher similarity.

Expert evaluation shows CORe better suppresses artifacts while preserving image sharpness.

Flow measurements are more reliable and consistent with CORe, especially during motion or stress.

Abstract

PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications. CODE & DATA: Implementation code and datasets are available on GitHub at http://github.com/OSU-MR/motion-robust-CMR