公开(公告)号：US20210118200A1

公开(公告)日：2021-04-22

申请号：US17075411

申请日：2020-10-20

申请人： REGENTS OF THE UNIVERSITY OF MINNESOTA

发明人： Mehmet Akcakaya ,  Burhaneddin Yaman ,  Seyed Amir Hossein Hosseini

IPC分类号： G06T11/00 ,  G16H30/40 ,  G06N3/04 ,  G06N3/08

摘要： Self-supervised training of machine learning (“ML”) algorithms for reconstruction in inverse problems are described. These techniques do not require fully sampled training data. As an example, a physics-based ML reconstruction can be trained without requiring fully-sampled training data. In this way, such ML-based reconstruction algorithms can be trained on existing databases of undersampled images or in a scan-specific manner.

公开(公告)号：US12106401B2

公开(公告)日：2024-10-01

申请号：US17075411

申请日：2020-10-20

申请人： REGENTS OF THE UNIVERSITY OF MINNESOTA

发明人： Mehmet Akcakaya ,  Burhaneddin Yaman ,  Seyed Amir Hossein Hosseini

IPC分类号： G06T11/00 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40

CPC分类号： G06T11/005 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40

摘要： Self-supervised training of machine learning (“ML”) algorithms for reconstruction in inverse problems are described. These techniques do not require fully sampled training data. As an example, a physics-based ML reconstruction can be trained without requiring fully-sampled training data. In this way, such ML-based reconstruction algorithms can be trained on existing databases of undersampled images or in a scan-specific manner.