The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 05, 2021

Filed:

Nov. 06, 2017
Applicant:

The University of North Carolina AT Chapel Hill, Chapel Hill, NC (US);

Inventors:

Weili Lin, Chapel Hill, NC (US);

Dinggang Shen, Chapel Hill, NC (US);

Jeffrey Keith Smith, Sanford, NC (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/70 (2018.01); G06F 16/583 (2019.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06F 16/583 (2019.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G16H 40/63 (2018.01); G16H 50/70 (2018.01); G06T 2207/10016 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10104 (2013.01); G06T 2207/20081 (2013.01);
Abstract

A method for smart image protocoling includes, using a medical imaging device, obtaining, using a first medical imaging sequence, a first set of medical images of a patient. Anatomical and, if present, disease features are extracted from the first set of medical images. A machine learning trained algorithm is used to determine, in real time, and based on the extracted anatomical and/or disease features, whether a desired medical imaging goal is achieved for the patient. In response to determining that the desired medical imaging goal is achieved, at least one image from the first set of medical images is output as a final image. In response to determining that the desired medical imaging goal has not been achieved, the machine learning trained algorithm is used to select a second medical imaging sequence. A second set of medical images of the patient is obtained using the second medical imaging sequence. The above outlined procedures will be repeated until the final imaging goal is achieved for a patient.


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