Poojar, P., Qian, E., Fernandes, T. T., Nunes, R. G., Fung, M., Quarterman, P., Jambawalikar, S. R., Lignelli, A., & Geethanath, S. (2023). Tailored magnetic resonance fingerprinting. Magnetic Resonance Imaging, 99, 81–90. https://doi.org/10.1016/j.mri.2023.02.002

Zhang, X., Angelini, E. D., Haghpanah, F. S., Laine, A. F., Sun, Y., Hiura, G. T., Dashnaw, S. M., Prince, M. R., Hoffman, E. A., Ambale-Venkatesh, B., Lima, J. A., Wild, J. M., Hughes, E. W., Barr, R. G., & Shen, W. (2022). Quantification of lung ventilation defects on hyperpolarized MRI: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD study. Magnetic Resonance Imaging, 92, 140–149. https://doi.org/10.1016/j.mri.2022.06.016

Manso Jimeno, M., Ravi, K. S., Jin, Z., Oyekunle, D., Ogbole, G., & Geethanath, S. (2022). ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. Magnetic Resonance Imaging, 89, 42–48. https://doi.org/10.1016/j.mri.2022.02.002

Tong, G., Gaspar, A. S., Qian, E., Ravi, K. S., Vaughan, J. T., Nunes, R. G., & Geethanath, S. (2022). A framework for validating open-source pulse sequences. Magnetic Resonance Imaging, 87, 7–18. https://doi.org/10.1016/j.mri.2021.11.014

Igwe, K. C., Lao, P. J., Vorburger, R. S., Banerjee, A., Rivera, A., Chesebro, A., Laing, K., Manly, J. J., & Brickman, A. M. (2022). Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging. Magnetic Resonance Imaging, 85, 71–79. https://doi.org/10.1016/j.mri.2021.10.007