Honors & Awards

  • American Heart Association Predoctoral Fellowship, American Heart Association (2023)
  • National Science Foundation Graduate Fellowship, National Science Foundation (2020-2023)

Education & Certifications

  • Master of Science, Stanford University, BIOE-MS (2022)
  • Bachelors of Science, University of Delaware, Biomedical Engineering (2020)

All Publications

  • Correlated noise in brain magnetic resonance elastography MAGNETIC RESONANCE IN MEDICINE Hannum, A. J., McIlvain, G., Sowinski, D., McGarry, M. J., Johnson, C. L. 2022; 87 (3): 1313-1328


    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.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.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.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.

    View details for DOI 10.1002/mrm.29050

    View details for Web of Science ID 000710004900001

    View details for PubMedID 34687069

    View details for PubMedCentralID PMC8776601

  • Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI. Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Loecher, M., Hannum, A. J., Perotti, L. E., Ennis, D. B. 2021; 12738: 213-222


    Cardiac tagged MR images allow for deformation fields to be measured in the heart by tracking the motion of tag lines throughout the cardiac cycle. Machine learning (ML) algorithms enable accurate and robust tracking of tag lines. Herein, the use of a massive synthetic physics-driven training dataset with known ground truth was used to train an ML network to enable tracking any number of points at arbitrary positions rather than anchored to the tag lines themselves. The tag tracking and strain calculation methods were investigated in a computational deforming cardiac phantom with known (ground truth) strain values. This enabled both tag tracking and strain accuracy to be characterized for a range of image acquisition and tag tracking parameters. The methods were also tested on in vivo volunteer data. Median tracking error was <0.26mm in the computational phantom, and strain measurements were improved in vivo when using the arbitrary point tracking for a standard clinical protocol.

    View details for DOI 10.1007/978-3-030-78710-3_21

    View details for PubMedID 34590079