公开(公告)号：EP4437496A1

公开(公告)日：2024-10-02

申请号：EP22899558.5

申请日：2022-11-23

申请人： GE Precision Healthcare LLC ,  The Board of Trustees of the Leland Stanford Junior University

发明人： PAL, Debashish ,  CHAUDHARI, Akshay ,  LOENING, Andreas ,  SHOKROLLAHI, Peyman ,  ZAMBRANO CHAVES, Juan Manuel ,  SHEN, Yaxi ,  DORAISWAMY, Vignesh ,  PRASAD, Raghu ,  DHARESHWAR, Supreeth ,  BAHRAMI, Naeim

IPC分类号： G06T7/00 ,  G06N3/04 ,  G06N3/08 ,  G06T5/00 ,  G16H30/20 ,  G16H30/40

CPC分类号： G16H30/20 ,  G06T7/00 ,  G16H15/00 ,  G16H40/60 ,  G16H40/20 ,  G06N3/045 ,  G06N3/0442 ,  G06N3/09 ,  G06N3/084

公开(公告)号：EP4437465A1

公开(公告)日：2024-10-02

申请号：EP22899388.7

申请日：2022-11-23

申请人： GE Precision Healthcare LLC ,  The Board of Trustees of the Leland Stanford Junior University

发明人： PAL, Debashish ,  SHEN, Yaxi ,  NICHOLS, Steven ,  SINGH, Ravi Raj ,  CIANO, Amanda ,  CHOUDHURY, Arindam Dutta ,  LANGLOTZ, Curt ,  LOENING, Andreas ,  CHAUDHARI, Akshay ,  BAHRAMI, Naeim

IPC分类号： G06N20/00

CPC分类号： G06N20/00 ,  G16H40/20 ,  G16H70/20 ,  G06N5/04 ,  G16H30/40 ,  G16H40/67

公开(公告)号：EP4147644A1

公开(公告)日：2023-03-15

申请号：EP22192553.0

申请日：2022-08-29

申请人： GE Precision Healthcare LLC ,  The Board of Trustees of the Leland Stanford Junior University

发明人： WANG, Adam S. ,  PAL, Debashish ,  IMRAN, Abdullah-Al-Zubaer ,  WANG, Sen ,  ZUCKER, Evan ,  PATEL, Bhavik Natvar

IPC分类号： A61B6/00

摘要： Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.