US 12,272,129 B2
System and method for automated transform by manifold approximation
Matthew S. Rosen, Somerville, MA (US); Bo Zhu, Cambridge, MA (US); and Bruce R. Rosen, Lexington, MA (US)
Assigned to The General Hospital Corporation, Boston, MA (US)
Filed by The General Hospital Corporation, Boston, MA (US)
Filed on Mar. 14, 2023, as Appl. No. 18/183,639.
Application 18/183,639 is a continuation of application No. 16/326,910, granted, now 11,620,772, previously published as PCT/US2017/049832, filed on Sep. 1, 2017.
Claims priority of provisional application 62/510,572, filed on May 24, 2017.
Claims priority of provisional application 62/382,490, filed on Sep. 1, 2016.
Prior Publication US 2023/0215161 A1, Jul. 6, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/82 (2022.01); A61B 5/00 (2006.01); A61B 5/055 (2006.01); A61B 6/00 (2024.01); A61B 6/03 (2006.01); A61B 6/46 (2024.01); A61B 8/00 (2006.01); A61B 8/08 (2006.01); G01R 33/12 (2006.01); G01R 33/48 (2006.01); G01R 33/56 (2006.01); G06F 18/2113 (2023.01); G06F 18/2413 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 11/00 (2006.01); G06V 10/764 (2022.01); G16H 30/40 (2018.01)
CPC G06V 10/82 (2022.01) [A61B 5/0035 (2013.01); A61B 5/0059 (2013.01); A61B 5/055 (2013.01); A61B 5/7267 (2013.01); A61B 5/7425 (2013.01); A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 6/463 (2013.01); A61B 6/5247 (2013.01); A61B 8/463 (2013.01); A61B 8/5261 (2013.01); G01R 33/12 (2013.01); G01R 33/4818 (2013.01); G01R 33/5608 (2013.01); G06F 18/2113 (2023.01); G06F 18/24143 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 11/006 (2013.01); G06V 10/764 (2022.01); G16H 30/40 (2018.01); G01R 33/4824 (2013.01); G06T 2207/20024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2210/41 (2013.01); G06V 2201/03 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A medical imaging system comprising:
an image sensor configured to acquire image data from a patient, wherein the image data is in a sensor domain;
a neural network trained in a learning process to receive image data in the sensor domain and transform the image data into an image of the patient in an image domain;
a processor configured to receive the sensor data from the image sensor and to deliver the sensor data to the neural network; and
a display configured to display the image of the patient in the image domain.