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:
Nov. 19, 2024

Filed:

Mar. 01, 2022
Applicants:

Jianqiang Liu, Campbell, CA (US);

Manat Maolinbay, Gilroy, CA (US);

Chwen-yuan Ku, San Jose, CA (US);

Linbo Yang, Pleasanton, CA (US);

Inventors:

Jianqiang Liu, Campbell, CA (US);

Manat Maolinbay, Gilroy, CA (US);

Chwen-yuan Ku, San Jose, CA (US);

Linbo Yang, Pleasanton, CA (US);

Assignee:

AIXSCAN Inc., Sunnyvale, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/00 (2024.01); A61B 6/02 (2006.01); A61B 6/03 (2006.01); A61B 6/04 (2006.01); A61B 6/06 (2006.01); A61B 6/08 (2006.01); A61B 6/40 (2024.01); A61B 6/42 (2024.01); A61B 6/46 (2024.01); A61B 6/58 (2024.01); G01N 23/044 (2018.01); G01N 23/083 (2018.01); G01N 23/18 (2018.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 11/00 (2006.01); G06T 17/00 (2006.01); G06V 10/25 (2022.01); G06V 10/62 (2022.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); A61B 6/50 (2024.01);
U.S. Cl.
CPC ...
A61B 6/541 (2013.01); A61B 6/025 (2013.01); A61B 6/032 (2013.01); A61B 6/035 (2013.01); A61B 6/0407 (2013.01); A61B 6/06 (2013.01); A61B 6/08 (2013.01); A61B 6/4007 (2013.01); A61B 6/4014 (2013.01); A61B 6/4021 (2013.01); A61B 6/405 (2013.01); A61B 6/4208 (2013.01); A61B 6/4283 (2013.01); A61B 6/4405 (2013.01); A61B 6/4441 (2013.01); A61B 6/4452 (2013.01); A61B 6/4476 (2013.01); A61B 6/4482 (2013.01); A61B 6/467 (2013.01); A61B 6/482 (2013.01); A61B 6/54 (2013.01); A61B 6/542 (2013.01); A61B 6/56 (2013.01); A61B 6/583 (2013.01); G01N 23/044 (2018.02); G01N 23/083 (2013.01); G01N 23/18 (2013.01); G06T 7/0012 (2013.01); G06T 7/0016 (2013.01); G06T 7/11 (2017.01); G06T 11/003 (2013.01); G06T 11/006 (2013.01); G06T 17/00 (2013.01); G06V 10/25 (2022.01); G06V 10/62 (2022.01); G16H 10/60 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); A61B 6/4275 (2013.01); A61B 6/502 (2013.01); G01N 2223/401 (2013.01); G06T 2200/24 (2013.01); G06T 2207/10076 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30064 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30168 (2013.01); G06T 2210/41 (2013.01); G06V 2201/032 (2022.01);
Abstract

Disclosed are image recognition Artificial Intelligence (AI) training methods for multiple pulsed X-ray source-in-motion tomosynthesis imaging system. Image recognition AI training can be performed three ways: first, using existing acquired chest CT data set with known nodules to generate synthetic tomosynthesis Images, no X-ray radiation applied; second, taking X-ray raw images with anthropomorphic chest phantoms with simulated lung nodules, applying X-ray beam on phantom only; third, acquiring X-ray images using multiple pulsed source-in-motion tomosynthesis images from real patients with real known nodules and without nodules. An X-ray image recognition training network that is configured to receive X-ray training images, automatically determine whether the received images indicate a nodule or lesion condition. After training, image knowledge is updated and stored at knowledge database.


Find Patent Forward Citations

Loading…