4.5 Article

Correlated noise in brain magnetic resonance elastography

期刊

MAGNETIC RESONANCE IN MEDICINE
卷 87, 期 3, 页码 1313-1328

出版社

WILEY
DOI: 10.1002/mrm.29050

关键词

brain; magnetic resonance elastography; physiological noise; pulsation; viscoelasticity

资金

  1. National Institutes of Health [R01-EB027577, R01-AG058853, U01-NS112120, F31-HD103361]
  2. Delaware INBRE Program [P20-GM103446]
  3. Delaware Rehabilitation Institute

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The study aimed to investigate the impact of signal noise in magnetic resonance elastography on mechanical property estimates. Results showed that physiological and vibration noise had a greater effect on property estimation than image noise, and they were spatially correlated.
Purpose Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined. Methods To characterize the effects of signal noise in MRE, we conducted 3 experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise because of cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We use a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations. Results We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise. Conclusion Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

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