ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function

Zhengyang Ming; Arutyun Pogosyan; Chang Gao; Caroline M Colbert; Holden H Wu; J Paul Finn; Dan Ruan; Peng Hu; Anthony G Christodoulou; Kim-Lien Nguyen

doi:10.1002/nbm.5091

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function

NMR Biomed. 2024 Apr;37(4):e5091. doi: 10.1002/nbm.5091. Epub 2024 Jan 9.

Authors

Zhengyang Ming^{1

2}, Arutyun Pogosyan³, Chang Gao^{1

2}, Caroline M Colbert^{1

2

3}, Holden H Wu^{1

2

4}, J Paul Finn^{1

2}, Dan Ruan^{1

4

5}, Peng Hu^{1

2

4}, Anthony G Christodoulou^{4

6}, Kim-Lien Nguyen^{1

2

3

4}

Affiliations

¹ Physics and Biology in Medicine Graduate Program, University of California, Los Angeles, California, USA.
² Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
³ Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA.
⁴ Department of Bioengineering, University of California, Los Angeles, California, USA.
⁵ Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
⁶ Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

PMID: 38196195
PMCID: PMC10947936 (available on 2025-04-01)
DOI: 10.1002/nbm.5091

Abstract

Background: Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion.

Methods: A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function.

Results: A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible.

Conclusion: ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

Keywords: ECG free; cardiac motion; cine MRI; clustering; golden-step acquisition; self-gating.

MeSH terms

Cardiac-Gated Imaging Techniques / methods
Cluster Analysis
Humans
Image Interpretation, Computer-Assisted* / methods
Magnetic Resonance Imaging, Cine* / methods
Reproducibility of Results
Ventricular Function

Abstract

MeSH terms

Grants and funding