Speaker
Kevin Chen
(Department of Radiology, Stanford University)
Description
In this study, we aimed to generate diagnostic-quality amyloid positron emission tomography (PET) images from “low-dose” PET images, reconstructed from massively undersampled raw data, as well as simultaneously-acquired multimodal magnetic resonance imaging (MRI) contrasts used as inputs in a convolutional neural network (CNN) framework. We have shown that the synthesized images generated from a model incorporating both PET and MR inputs yield images with superior image quality and diagnostic value compared to the low-dose image as well as images synthesized from a model with PET-only inputs.
Primary author
Kevin Chen
(Department of Radiology, Stanford University)
Co-authors
Enhao Gong
(Department of Radiology, Stanford University, Stanford, CA 94305 USA)
Fabiola Macruz
(Department of Radiology, Stanford University, Stanford, CA 94305 USA)
Greg Zaharchuk
(Department of Radiology, Stanford University, Stanford, CA 94305 USA)
John Pauly
(Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA)
Junshen Xu
(Department of Engineering Physics, Tsinghua University, Beijing,China)
Mehdi Khalighi
(GE Healthcare, Menlo Park, CA USA)
Shyam Srinivas
(Department of Radiology, Stanford University, Stanford, CA 94305 USA)
