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AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size.

, , , , , , , , , and . NeuroImage, (2020)

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AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size., , , , , , , , , and . NeuroImage, (2020)A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients., , , , , , , , , and 18 other author(s). NeuroImage, (2017)3D Ultrasound and MRI in Assessing Resection Margins during Tongue Cancer Surgery: A Research Protocol for a Clinical Diagnostic Accuracy Study., , , , , , , , , and . J. Imaging, 9 (9): 174 (2023)Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data., , , , , , , , and . NeuroImage, (2014)Validation of kinetic modeling of 15OH2O PET using an image derived input function on hybrid PET/MRI., , , , , , and . NeuroImage, (2021)Measurement of brain oxygenation changes using dynamic T1-weighted imaging., , , and . NeuroImage, (2013)Combined PET/MR imaging in neurology: MR-based attenuation correction implies a strong spatial bias when ignoring bone., , , , , , , , and . NeuroImage, (2014)