IP Library › Granted Patent US 11,058,319
Granted Patent B2
US 11,058,319 · App. 15/623,515 · Granted Jul 13, 2021

Method for positioning a positionable table

Inventors: Thomas Boettger (Erlangen, DE); Daniel Lerch (Weilersbach, DE); Carsten Thierfelder (Pinzberg, DE); Lisa Vallines (Majadahonda, DE); Fernando Vega (Erlangen, DE)
Assignee: SIEMENS HEALTHCARE GMBH
A61B5/055A61B6/0407A61B6/0487A61B6/03A61B6/04
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Quick Facts
Patent No.
US 11,058,319
App. No.
15/623,515
Granted
Jul 13, 2021
Kind
B2
Abstract

A method is for positioning a positionable table for a patient inside a medical imaging device. In an embodiment, the method includes a determination of a table position as a function of an organ or body part of the patient for examination; an ascertainment of correction data for correcting the table position; a determination of a corrected table position based on the ascertained correction data; and a positioning of the table at the corrected table position.

Claims (38)

1. A method for positioning a positionable table for a patient inside a medical imaging device, the method comprising:

determining, via a processor of the medical imaging device, a first approximate table position as a function of an organ or body part of the patient for examination;

ascertaining, via a processor of the medical imaging device, correction data from a database of a plurality patient models for correction of the determined first approximate table position to a second more precise table position relative to the first approximate table position by importing patient data for correction of the first table position;

determining, via a processor of the medical imaging device, a corrected table position based on the imported patient data and a self-learning algorithm; and

positioning, via a processor of the medical imaging device, the table at the corrected table position before initiating a medical imaging procedure via the medical imaging device, wherein the patient data includes at least one of a gender, clinical indication, and an age of the patient included in an examination report prepared prior to positioning the table, wherein the self-learning algorithm is trained by storing, in a memory, imported patient data of a plurality of patients and manual corrections of table positions that follows automatic positionings of table positions.

2. The method of claim 1 , wherein the table position comprises at least one of a vertical table position and a longitudinal table position.

3. The method of claim 1 , wherein the weight of the patient is automatically determined by a weighing appliance of the medical imaging device.

4. The method of claim 1 , wherein at least one of a position and a height of the patient is automatically determined by a camera.

5. The method of claim 1 , wherein the correction data is ascertained based on previous manual corrections of the table in conjunction with corresponding patient data of the patient for examination.

6. The method of claim 1 , wherein the correction data is ascertained based on an interpolation between existing data points.

7. The method of claim 1 , wherein the correction data is ascertained based on an adjustment theory between existing data points.

8. A medical imaging device including a positionable table for positioning a patient, comprising:

a memory storing computer-readable instructions; and one or more processors configured to execute the computer-readable instructions such that the one or more processors are configured to

determine a first table position as a function of an organ or body part of the patient for examination based on a look-up table stored in the memory;

ascertain correction data for correcting the first table position to a more precise second table position relative to the first table position by importing patient data for correction of the first table position;

determine a corrected table position based on the imported patient data and a self-learning algorithm; and

at least one motor for positioning the table at the corrected table position based on an output of the self-learning algorithm, wherein the patient data includes at least one of a gender, clinical indication, and an age of the patient, wherein the self-learning algorithm is trained by storing, in a memory, imported patient data of a plurality of patients and manual corrections of table positions that follows automatic positionings of table positions.

9. The medical imaging device of claim 8 , wherein the one or more processors are configured to ascertain the correction data based on patient data of the patient for examination.

10. The medical imaging device of claim 9 , wherein the patient data comprises at least one of a date of birth, a weight, a height, a body mass index, and an ethnicity of the patient.

11. The medical imaging device of claim 10 , further comprising:

a weighing appliance for automatic determination of the weight of the patient.

12. The medical imaging device of claim 10 , further comprising:

a camera for automatic determination of at least one of a position and height of the patient.

13. The medical imaging device of claim 10 , wherein the one or more processors configured to ascertain the correction data based on previous manual positionings of the table in conjunction with corresponding patient data of the patient for examination.

14. A medical imaging device including a positionable table for positioning a patient, comprising:

a memory storing computer-readable instructions; and

one or more processors configured to execute the computer-readable instructions such that the one or more processors are configured to

determine a first approximate table position as a function of an organ or body part of the patient for examination;

ascertain correction data for correction of the first approximate table position to a second more precise table position relative to the first approximate table position by importing patient data for correction of the first table position;

determine a corrected table position based on the imported patient data and a self-learning algorithm; and

at least one motor to position the table based upon the corrected table position before initiating a medical imaging procedure via the medical imaging device, wherein the patient data includes at least one of a gender, clinical indication, and an age of the patient, wherein the self-learning algorithm is trained by storing, in a memory, imported patient data of a plurality of patients and manual corrections of table positions that follows automatic positionings of table positions.

15. The method of claim 3 , wherein at least one of a position and height of the patient is automatically determined by a camera.

16. The medical imaging device of claim 14 , wherein the correction data is ascertained based on previous manual positionings of the table in conjunction with corresponding patient data of the patient for examination.

17. The method of claim 1 , wherein the patient data includes a date of birth of the patient.

18. The method of claim 1 , wherein the patient data includes a weight of the patient.

19. The method of claim 1 , wherein the patient data includes a height of the patient.

20. The method of claim 1 , wherein the patient data includes a body mass index of the patient.

21. The method of claim 1 , wherein the patient data includes an ethnicity of the patient.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 20, 2023
From: SIEMENS HEALTHCARE GMBH
To: SIEMENS HEALTHINEERS AG
Reel/Frame 066267/0346 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 9, 2018
From: SIEMENS SHANGHAI MEDICAL EQUIPMENT LTD.
To: SIEMENS HEALTHCARE GMBH
Reel/Frame 046595/0499 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 7, 2018
From: VALLINES, LISA
To: SIEMENS SHANGHAI MEDICAL EQUIPMENT LTD.
Reel/Frame 046569/0689 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 7, 2018
From: BOETTGER, THOMAS; LERCH, DANIEL; THIERFELDER, CARSTEN; VEGA, FERNANDO
To: SIEMENS HEALTHCARE GMBH
Reel/Frame 046569/0707 →
Priority Claims (1)
DE 102016211720.3 · Jun 29, 2016 · national
Continuity (1)
Related Publication 20170311842A1 · Nov 2, 2017