IP Library › Granted Patent US 12,591,982
Granted Patent B2
US 12,591,982 · App. 18/195,009 · Granted Mar 31, 2026

Motion detection associated with a body part

Inventors: Zhongpai Gao (Rowley, MA); Abhishek Sharma (Boston, MA); Meng Zheng (Cambridge, MA); Benjamin Planche (Briarwood, NY); Ziyan Wu (Lexington, MA); Terrence Chen (Lexington, MA)
Assignee: Shanghai United Imaging Intelligence Co., Ltd.
G06T7/246A61B5/11G06V10/25G06V10/44G06T2207/10024G06T2207/10081G06T2207/10088
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,591,982
App. No.
18/195,009
Filed
May 9, 2023
Granted
Mar 31, 2026
Kind
B2
Examiner
BITAR, NANCY
Art Unit
2664
USPC
382/107
Abstract

Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.

Claims (31)

1 . An apparatus, comprising:

at least one processor configured to:

obtain, from a color image sensor installed inside a medical scanner, a first color image of a patient inside the medical scanner and a second color image of the patient inside the medical scanner, wherein the first color image depicts the patient in a first state and the second color image depicts the patient in a second state;

identify, using a first machine learning (ML) model, a first area in the first color image that corresponds to a body part of the patient;

identify, using the first ML model, a second area in the second color image that corresponds to the body part of the patient;

extract a first plurality of features from the first area of the first color image and a second plurality of features from the second area of the second color image;

determine a preliminary motion of the body part based on the first plurality of features and the second plurality of features;

calculate a movement of the medical scanner between a time when the patient is in the first state and a time when the patient is in the second state; and

determine a motion of the body part of the patient by subtracting the calculated movement of the medical scanner from the preliminary motion of the body part.

2 . The apparatus of claim 1 , wherein the medical scanner includes a computed tomography (CT) scanner or a magnetic resonance imaging (MRI) scanner.

3 . The apparatus of claim 1 , wherein the at least one processor is further configured to obtain one or more images of the medical scanner and calculate the movement of the medical scanner based on the one or more obtained images.

4 . The apparatus of claim 1 , wherein the at least one processor is further configured to receive information regarding the movement of the medical scanner from the medical scanner and calculate the movement of the medical scanner based on the received information.

5 . The apparatus of claim 1 , wherein the at least one processor is configured to determine the preliminary motion of the body part in multiple dimensions and subtract the movement of the medical scanner from at least one of the multiple dimensions.

6 . The apparatus of claim 1 , wherein the at least one processor being configured to determine the preliminary motion of the body part of the patient based on the first plurality of features and the second plurality of features comprises the at least one processor being configured to determine corresponding features from the first plurality of features and the second plurality of features, and determine the preliminary motion of the body part of the patient based on differences between the corresponding features.

7 . The apparatus of claim 6 , wherein the at least one processor being configured to determine the corresponding features from the first plurality of features and the second plurality of features comprises the at least one processor being configured to identify corresponding pixels in the first area of the first color image and the second area of the second color image that are associated with the body part of the patient, and determine the corresponding features from the first plurality of features and the second plurality of features by selecting respective features from the first plurality of features and the second plurality of features that are associated with the identified pixels.

8 . The apparatus of claim 1 , wherein the at least one processor is further configured to extract the first plurality of features from the first area of the first color image and the second plurality of features from the second area of the second color image using the first ML model or a second ML model.

9 . A method for determining a motion associated with a body part of a patient, the method comprising:

obtaining, from a color image sensor installed inside a medical scanner, a first color image of the patient inside the medical scanner and a second color image of the patient inside the medical scanner, wherein the first color image depicts the patient in a first state and the second color image depicts the patient in a second state;

identifying, using a first machine learning (ML) model, a first area in the first color image that corresponds to the body part of the patient;

identifying, using the first ML model, a second area in the second color image that corresponds to the body part of the patient;

extracting a first plurality of features from the first area of the first color image and a second plurality of features from the second area of the second color image;

determining a preliminary motion of the body part of the patient based on the first plurality of features and the second plurality of features;

calculating a movement of the medical scanner between a time when the patient is in the first state and a time when the patient is in the second state; and

determining the motion of the body part of the patient by subtracting the calculated movement of the medical scanner from the preliminary motion of the body part.

10 . The method of claim 9 , wherein the medical scanner includes a computed tomography (CT) scanner or a magnetic resonance imaging (MRI) scanner.

11 . The method of claim 9 , further comprising obtaining one or more images of the medical scanner, wherein the movement of the medical scanner is calculated based on the one or more obtained images.

12 . The method of claim 9 , further comprising receiving information regarding the movement of the medical scanner from the medical scanner, wherein the movement of the medical scanner is calculated based on the received information.

13 . The method of claim 9 , wherein the preliminary motion is determined in multiple dimensions and wherein the movement of the medical scanner is subtracted from at least one of the multiple dimensions.

14 . The method of claim 9 , wherein determining the preliminary motion associated with the body part of the patient based at least on the first plurality of features and the second plurality of features comprises determining corresponding features from the first plurality of features and the second plurality of features, and determining the preliminary motion associated with the body part of the patient based on differences between the corresponding features.

15 . The method of claim 14 , wherein determining the corresponding features from the first plurality of features and the second plurality of features comprises identifying corresponding pixels in the first area of the first color image and the second area of the second color image that are associated with the body part of the patient, and determining the corresponding features from the first plurality of features and the second plurality of features by selecting respective features from the first plurality of features and the second plurality of features that are associated with the identified pixels.

16 . The method of claim 9 , wherein the first plurality of features is extracted from the first area of the first color image and the second plurality of features is extracted from the second area of the second color image using the first ML model or a second ML model.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 9, 2023
From: GAO, ZHONGPAI; SHARMA, ABHISHEK; WU, ZIYAN; PLANCHE, BENJAMIN; ZHENG, MENG; CHEN, TERRENCE
To: UII AMERICA, INC.
Reel/Frame 063579/0953 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 9, 2023
From: UII AMERICA, INC.
To: SHANGHAI UNITED IMAGING INTELLIGENCE CO., LTD.
Reel/Frame 063580/0162 →
Continuity (1)
Related Publication 20240378731A1 · Nov 14, 2024
References Cited (10)
US 8331637B2 · Bar-Aviv · 2012 [cited by examiner]
US 10319209B2 · Carlton-Foss · 2019 [cited by applicant]
US 11576578B2 · Hao · 2023 [cited by examiner]
US 20180193667A1 · Kaiser · 2018 [cited by examiner]
US 20180300556A1 · Varerkar · 2018 [cited by examiner]
US 20190090785A1 · Heinrich et al. · 2019 [cited by applicant]
US 20200226827A1 · Kim · 2020 [cited by examiner]
US 20210290106A1 · Stever et al. · 2021 [cited by applicant]
CN 110088643B · 2019 [cited by applicant]
EP 3492945A1 · 2019 [cited by applicant]