Zaharchuk, Greg

Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update

Tejani, A. S., Klontzas, M. E., Gatti, A. A., Mongan, J. T., Moy, L., Park, S. H., Kahn, C. E., Panel, F. T. C. 2. U., Abbara, S., Afat, S., et al. (2024) · Radiology: Artificial Intelligence

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 570

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI

Gong, E., Pauly, J. M., Wintermark, M., & Zaharchuk, G. (2018) · Journal of Magnetic Resonance Imaging

Advanced MRI Techniques and ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 351

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., & Plevritis, S. K. (2014) · Radiology

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 319

Quantitative susceptibility mapping using deep neural network: QSMnet

Yoon, J., Gong, E., Chatnuntawech, I., Bilgic, B., Lee, J., Jung, W., Ko, J., Jung, H., Setsompop, K., Zaharchuk, G., et al. (2018) · NeuroImage

Advanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

Cited by 185

Amyloid-Related Imaging Abnormalities: An Update

Roytman, M., Mashriqi, F., Al-Tawil, K., Schulz, P. E., Zaharchuk, G., Benzinger, T. L. S., & Franceschi, A. M. (2023) · American Journal of Roentgenology

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 99

MR vascular fingerprinting: a new approach to compute cerebral blood volume, mean vessel radius, and oxygenation maps in the human brain

Christen, T., Pannetier, N. A., Ni, W. W., Qiu, D., Moseley, M. E., Schuff, N., & Zaharchuk, G. (2014) · NeuroImage

Advanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

Cited by 97

Low-count whole-body PET with deep learning in a multicenter and externally validated study

Chaudhari, A. S., Mittra, E., Davidzon, G. A., Gulaka, P., Gandhi, H., Brown, A., Zhang, T., Srinivas, S., Gong, E., Zaharchuk, G., et al. (2021) · npj Digital Medicine

Medical Imaging Techniques and ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 83

Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study

Grøvik, E., Yi, D., Iv, M., Tong, E., Nilsen, L. B., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., et al. (2021) · npj Digital Medicine

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 51

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

Ottesen, J. A., Yi, D., Tong, E., Iv, M., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., Rubin, D. L., et al. (2023) · Frontiers in Neuroinformatics

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 32

Improvements in PET Image Quality in Time of Flight (TOF) Simultaneous PET/MRI

Minamimoto, R., Levin, C., Jamali, M., Holley, D., Barkhodari, A., Zaharchuk, G., & Iagaru, A. (2016) · Molecular Imaging and Biology

Medical Imaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

Cited by 32

Turning brain MRI into diagnostic PET: 15O-water PET CBF synthesis from multi-contrast MRI via attention-based encoder–decoder networks

Hussein, R., Shin, D., Zhao, M. Y., Guo, J., Davidzon, G., Steinberg, G., Moseley, M., & Zaharchuk, G. (2024) · Medical Image Analysis

Advanced MRI Techniques and ApplicationsMedical Imaging Techniques and Applications

Cited by 9