公开(公告)号：US11089970B2

公开(公告)日：2021-08-17

申请号：US15172204

申请日：2016-06-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Thomas Lindner ,  Michael Helle

IPC: A61B5/026 ,  G01R33/54 ,  G01R33/563 ,  A61B5/00 ,  G01R33/483

Abstract: A magnetic resonance imaging system (100) for acquiring magnetic resonance data (141) from an imaging zone (108) includes a memory (134, 136) for storing machine executable instructions (150, 152, 154, 156) and pulse sequence commands (140). The pulse sequence commands cause the magnetic resonance imaging system to provide at least one spatially selective saturation pulse (408, 410) to at least one selected volume (124, 124′) that is at least partially outside of a region of interest (123) and within the imaging zone. The magnetic resonance imaging system performs a non-selective inversion (412) of spins in the region of interest followed by a readout (414) of the magnetic resonance data which is reconstructed (202) into an image (142).

公开(公告)号：US11633123B2

公开(公告)日：2023-04-25

申请号：US16759755

申请日：2018-10-26

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Axel Saalbach ,  Steffen Weiss ,  Karsten Sommer ,  Christophe Schuelke ,  Michael Helle

IPC: A61B5/055 ,  G16H30/40 ,  A61B5/00

Abstract: A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data. The first machine learning model includes a first input and a first output, a processor, wherein execution of the machine executable instructions causes the processor to control the magnetic resonance imaging system to repeatedly perform an acquisition and analysis process including: acquiring a dataset including a subset of the set of magnetic resonance imaging data from an imaging zone of the magnetic resonance imaging system according to the pulse sequence commands, providing the dataset to the first input of the first machine learning model, in response to the providing, receiving a prediction of a motion artifact level of the acquired magnetic resonance imaging data from the first output of the first machine learning model, the motion artifact level characterizing a number and/or extent of motion artifacts present in the acquired magnetic resonance imaging data.