Department of Radiology

Publications

2020

Tozlu C, Edwards D, Boes A, Labar D, K Tsagaris Z, Silverstein J, et al. Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke. Neurorehabil Neural Repair. 2020;34(5):428-439.

Du B, Tung C-. Enzyme-Assisted Photodynamic Therapy Based on Nanomaterials. ACS Biomater Sci Eng. 2020;6(5):2506-2517.

Zhang J, Liu Z, Zhang S, Zhang H, Spincemaille P, Nguyen TD, et al. Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction. Neuroimage. 2020;211:116579.

Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, et al. Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. IEEE Trans Med Imaging. 2020;39(4):1184-1194.

Wang Y, Prince MR. Quantitative Susceptibility Mapping Is Superior to T1-weighted Imaging for Detecting and Measuring Gadolinium. Radiology. 2020;297(1):151-153.

Deh K, Zaman M, Vedvyas Y, Liu Z, Gillen KM, Malley PO, et al. Validation of MRI quantitative susceptibility mapping of superparamagnetic iron oxide nanoparticles for hyperthermia applications in live subjects. Sci Rep. 2020;10(1):1171.

Bubu OM, Umasabor-Bubu OQ, Turner AD, Parekh A, Mullins AE, Kam K, et al. Self-reported obstructive sleep apnea, amyloid and tau burden, and Alzheimer's disease time-dependent progression. Alzheimers Dement. 2020.

Bao Q, Ma L, Liberman G, Solomon E, Martinho RP, Frydman L. Dynamic T mapping by multi-spin-echo spatiotemporal encoding. Magn Reson Med. 2020;84(2):895-907.

Kim H-, Park S, Rhee HY, Lee KM, Ryu C-, Lee SY, et al. Evaluation and Prediction of Early Alzheimer's Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping. Curr Alzheimer Res. 2020;17(5):428-437.

Han D, Shah S, Lee JH, Elmore K, Gransar H, Danad I, et al. An approach to evaluate myocardial perfusion defect assessment for projection-based DECT: A phantom study. Clin Imaging. 2020;63:10-15.