Miller, R. J. H., Lemley, M., Shanbhag, A., Ramirez, G., Liang, J. X., Builoff, V., Kavanagh, P., Sharir, T., Hauser, M. T., Ruddy, T. D., Fish, M. B., Bateman, T. M., Acampa, W., Einstein, A. J., Dorbala, S., Di Carli, M. F., Feher, A., Miller, E. J., Sinusas, A. J., … Slomka, P. J. (2024). The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0). Journal of Nuclear Medicine, 65(11), 1795–1801. https://doi.org/10.2967/jnumed.124.268292

Villar-Calle, P., Kochav, J. D., Vadaketh, K., Chiu, C., Tak, K., Agoglia, H., Liberman, N., Nguyen, K. L., Vizcarra-Tellez, A., Wu, A., RoyChoudhury, A., Khalique, O. K., Judd, R. M., Kim, R. J., Shah, D. J., Heitner, J. F., Farzaneh-Far, A., Shenoy, C., Owyang, C. G., … Kim, J. (2024). Tissue-Based Predictors of Impaired Right Ventricular Strain in Coronary Artery Disease: A Multicenter Stress Perfusion Study. Circulation: Cardiovascular Imaging, 17(8). https://doi.org/10.1161/circimaging.124.016852

Randazzo, M. J., Elias, P., Poterucha, T. J., Sharir, T., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T., Dorbala, S., Di Carli, M., Castillo, M., Liang, J. X., Miller, R. J. H., Dey, D., Berman, D. S., Slomka, P. J., & Einstein, A. J. (2024). Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography. European Heart Journal - Cardiovascular Imaging, 25(7), 996–1006. https://doi.org/10.1093/ehjci/jeae055

Williams, M. C., Bednarski, B. P., Pieszko, K., Miller, R. J. H., Kwiecinski, J., Shanbhag, A., Liang, J. X., Huang, C., Sharir, T., Dorbala, S., Di Carli, M. F., Einstein, A. J., Sinusas, A. J., Miller, E. J., Bateman, T. M., Fish, M. B., Ruddy, T. D., Acampa, W., Hauser, M. T., … Slomka, P. J. (2023). Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. European Journal of Nuclear Medicine and Molecular Imaging, 50(9), 2656–2668. https://doi.org/10.1007/s00259-023-06218-z

Hage, F. G., Einstein, A. J., Ananthasubramaniam, K., Bourque, J. M., Case, J., DePuey, E. G., Hendel, R. C., Henzlova, M. J., Shah, N. R., Abbott, B. G., Al Jaroudi, W., Better, N., Doukky, R., Duvall, W. L., Malhotra, S., Pagnanelli, R., Peix, A., Reyes, E., Saeed, I. M., … Winchester, D. E. (2023). Quality metrics for single-photon emission computed tomography myocardial perfusion imaging: an ASNC information statement. Journal of Nuclear Cardiology, 30(2), 864–907. https://doi.org/10.1007/s12350-022-03162-7

El Harake, J., Sayseng, V., Grondin, J., Weber, R., Einstein, A. J., & Konofagou, E. (2023). Preliminary Feasibility of Stress Myocardial Elastography for the Detection of Coronary Artery Disease. Ultrasound in Medicine & Biology, 49(2), 549–559. https://doi.org/10.1016/j.ultrasmedbio.2022.10.007

Singh, A., Miller, R. J. H., Otaki, Y., Kavanagh, P., Hauser, M. T., Tzolos, E., Kwiecinski, J., Van Kriekinge, S., Wei, C.-C., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., … Slomka, P. J. (2023). Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC: Cardiovascular Imaging, 16(2), 209–220. https://doi.org/10.1016/j.jcmg.2022.07.017

Miller, R. J. H., Singh, A., Otaki, Y., Tamarappoo, B. K., Kavanagh, P., Parekh, T., Hu, L.-H., Gransar, H., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M. F., Liang, J. X., … Slomka, P. J. (2022). Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal of Nuclear Medicine and Molecular Imaging, 50(2), 387–397. https://doi.org/10.1007/s00259-022-05972-w

Ibrahim, J., Nieves, R. A., Barakat, A. F., Hynal, K., Shpilsky, D., & Soman, P. (2022). DSPECT-specific normative limits for left ventricular size and function. Journal of Nuclear Cardiology, 29(6), 3293–3299. https://doi.org/10.1007/s12350-022-02932-7

Han, D., Rozanski, A., Miller, R. J. H., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Liang, J. X., Dey, D., Berman, D. S., & Slomka, P. J. (2022). Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry. Journal of Nuclear Cardiology, 29(6), 3221–3232. https://doi.org/10.1007/s12350-021-02829-x

Han, D., Rozanski, A., Gransar, H., Tzolos, E., Miller, R. J. H., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Liang, J. X., Hu, L.-H., Dey, D., Berman, D. S., & Slomka, P. J. (2022). Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry. Journal of Nuclear Cardiology, 29(6), 3003–3014. https://doi.org/10.1007/s12350-021-02810-8

Miller, R. J. H., Kuronuma, K., Singh, A., Otaki, Y., Hayes, S., Chareonthaitawee, P., Kavanagh, P., Parekh, T., Tamarappoo, B. K., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T., Dorbala, S., Di Carli, M. F., … Slomka, P. J. (2022). Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. Journal of Nuclear Medicine, jnumed.121.263686. https://doi.org/10.2967/jnumed.121.263686

Eisenberg, E., Miller, R. J. H., Hu, L.-H., Rios, R., Betancur, J., Azadani, P., Han, D., Sharir, T., Einstein, A. J., Bokhari, S., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Liang, J. X., … Slomka, P. J. (2022). Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. Journal of Nuclear Cardiology, 29(5), 2295–2307. https://doi.org/10.1007/s12350-021-02698-4

Miller, R. J. H., Hauser, M. T., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Huang, C., Liang, J. X., Han, D., Dey, D., Berman, D. S., & Slomka, P. J. (2022). Machine learning to predict abnormal myocardial perfusion from pre-test features. Journal of Nuclear Cardiology, 29(5), 2393–2403. https://doi.org/10.1007/s12350-022-03012-6

Tamarappoo, B. K., Otaki, Y., Sharir, T., Hu, L.-H., Gransar, H., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Eisenberg, E., Liang, J. X., Dey, D., Berman, D. S., & Slomka, P. J. (2022). Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study. Circulation: Cardiovascular Imaging, 15(6). https://doi.org/10.1161/circimaging.121.012741

Rios, R., Miller, R. J. H., Manral, N., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Van Kriekinge, S. D., Kavanagh, P. B., Parekh, T., Liang, J. X., Dey, D., Berman, D. S., & Slomka, P. J. (2022). Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Computers in Biology and Medicine, 145, 105449. https://doi.org/10.1016/j.compbiomed.2022.105449