公开(公告)号：US10096109B1

公开(公告)日：2018-10-09

申请号：US15475760

申请日：2017-03-31

申请人： Greg Zaharchuk ,  Enhao Gong ,  John M. Pauly

发明人： Greg Zaharchuk ,  Enhao Gong ,  John M. Pauly

IPC分类号： G06K9/00 ,  G06T7/00 ,  G06T3/60

摘要： A method of improving diagnostic and functional imaging is provided by obtaining at least two input images of a subject, using a medical imager, where each input image includes a different contrast, generating a plurality of copies of the input images using non-local mean (NLM) filtering, using an appropriately programmed computer, where each input image copy of the subject includes different spatial characteristics, obtaining at least one reference image of the subject, using the medical imager, where the reference image includes imaging characteristics that are different form the input images of the subject, training a deep network model, using data augmentation on the appropriately programmed computer, to adaptively tune model parameters to approximate the reference image from an initial set of the input and reference images, with the goal of outputting an improved quality image of other sets of low SNR low resolution images, for analysis by a physician.

公开(公告)号：US20180286037A1

公开(公告)日：2018-10-04

申请号：US15475760

申请日：2017-03-31

申请人： Greg Zaharchuk ,  Enhao Gong ,  John M. Pauly

发明人： Greg Zaharchuk ,  Enhao Gong ,  John M. Pauly

IPC分类号： G06T7/00 ,  G06T3/60

摘要： A method of improving diagnostic and functional imaging is provided by obtaining at least two input images of a subject, using a medical imager, where each input image includes a different contrast, generating a plurality of copies of the input images using non-local mean (NLM) filtering, using an appropriately programmed computer, where each input image copy of the subject includes different spatial characteristics, obtaining at least one reference image of the subject, using the medical imager, where the reference image includes imaging characteristics that are different form the input images of the subject, training a deep network model, using data augmentation on the appropriately programmed computer, to adaptively tune model parameters to approximate the reference image from an initial set of the input and reference images, with the goal of outputting an improved quality image of other sets of low SNR low resolution images, for analysis by a physician.