Technique for Assigning a Perfusion Metric to DCE MR Images

    公开(公告)号:US20220375073A1

    公开(公告)日:2022-11-24

    申请号:US17662088

    申请日:2022-05-05

    摘要: DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

    Measurement Point Determination in Medical Diagnostic Imaging

    公开(公告)号:US20190099159A1

    公开(公告)日:2019-04-04

    申请号:US15720317

    申请日:2017-09-29

    IPC分类号: A61B8/00 G06T7/62 A61B6/03

    摘要: For measurement point determination in imaging with a medical scanner, the user selects a location on the image. Rather than using that location, an “intended” location corresponding to a local boundary or landmark represented in the image is identified. The medical scanner uses the simple user interface to more exactly determine points for measurement. One or more rays are cast from the user selected location. The actual location is found by examining data along the ray or rays. For 2D imaging, the rays are cast in the plane. For 3D imaging, the ray is cast along a view direction to find the depth. The intensities along the ray or around the ray are used to find the actual location, such as by application of a machine-learnt classifier to the limited region around the ray or by finding intensities along the ray relative to a threshold.

    ANNULAR STRUCTURE REPRESENTATION
    8.
    发明申请

    公开(公告)号:US20210264644A1

    公开(公告)日:2021-08-26

    申请号:US17174471

    申请日:2021-02-12

    IPC分类号: G06T11/00 G06T7/00

    摘要: A method, apparatus, and computer readable storage medium are provided herein for constructing a representation of an annular structure associated with an anatomical object. The method includes receiving three-dimensional image data of the anatomical object and detecting at least a first landmark point and a second landmark point on the annular structure. A plane positioned between the first landmark point and the second landmark point, and oriented in accordance with a predefined angular relationship to a line connecting the first landmark point and the second landmark point is determined. A third landmark point on the annular structure which lies in the plane is also detected and the representation of the annular structure is generated using at least the first landmark point, the second landmark point, and the third landmark point. The representation is then outputted.

    Measurement point determination in medical diagnostic imaging

    公开(公告)号:US10660613B2

    公开(公告)日:2020-05-26

    申请号:US15720317

    申请日:2017-09-29

    摘要: For measurement point determination in imaging with a medical scanner, the user selects a location on the image. Rather than using that location, an “intended” location corresponding to a local boundary or landmark represented in the image is identified. The medical scanner uses the simple user interface to more exactly determine points for measurement. One or more rays are cast from the user selected location. The actual location is found by examining data along the ray or rays. For 2D imaging, the rays are cast in the plane. For 3D imaging, the ray is cast along a view direction to find the depth. The intensities along the ray or around the ray are used to find the actual location, such as by application of a machine-learnt classifier to the limited region around the ray or by finding intensities along the ray relative to a threshold.