IP Library › Granted Patent US 12,433,502
Granted Patent B2
US 12,433,502 · App. 17/706,458 · Granted Oct 7, 2025

Magnetic resonance volumetric imaging

Inventors: Iwan Kawrykow (Sofia, BG); Georgi Gerganov (Sofia, BG); James F. Dempsey (Pebble Beach, CA)
Assignee: ViewRay Systems, Inc.
A61B5/055A61B5/7257G01R33/285G01R33/56325G06T7/74A61B90/37A61B2090/374G01R33/4808G01R33/4824G01R33/5608G01R33/56509G01R33/5676
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Quick Facts
Patent No.
US 12,433,502
App. No.
17/706,458
Granted
Oct 7, 2025
Kind
B2
Abstract

Reference data relating to a portion of a patient anatomy during patient motion can be acquired from a magnetic resonance imaging system (MRI) to develop a patient motion library. During a time of interest, tracking data is acquired that can be related to the reference data. Partial volumetric data is acquired during the time of interest and at approximately the same time as the acquisition of the tracking data. A volumetric image of patient anatomy that represents a particular motion state can be constructed from the acquired partial volumetric data and acquired tracking data.

Claims (46)

1. A computer program product comprising a non-transitory machine-readable medium storing instructions which, when executed by at least one processor forming at least part of a computing system, result in operations comprising:

determining, prior to a current treatment of a patient, a correlation between tracking data and volumetric data; wherein the tracking data is k-space data;

obtaining, prior to the current treatment of the patient, reference data related to the tracking data;

partitioning, prior to the current treatment of the patient, the reference data into a plurality of motion states; wherein the motion states are divided from an imaged motion;

generating a prediction of achievability of a treatment plan based on a current patient condition, the generating utilizing the correlation between the tracking data and the volumetric data, along with the reference data; and

updating the treatment plan based on the prediction of achievability.

2. The computer program product of claim 1 , the operations further comprising delivering radiotherapy based on the updated treatment plan.

3. The computer program product of claim 2 , the operations further comprising utilizing using a particular set of tracking data that is a good predictor of patient motion.

4. The computer program product of claim 2 , the operations further comprising utilizing using a particular set of tracking data that is a good predictor of patient volumetric motion.

5. The computer program product of claim 2 , the operations further comprising utilizing using a particular set of tracking data that at least partially predicts patient motion.

6. The computer program product of claim 2 , the operations further comprising utilizing using a particular set of tracking data that at least partially predicts patient volumetric motion.

7. The computer program product of claim 2 , wherein determining the correlation between tracking data and volumetric data further comprises utilizing a model that predicts motion of patient anatomy.

8. The computer program product of claim 7 , the operations further comprising training the model by comparing tracking data and volumetric data received during the radiotherapy.

9. The computer program product of claim 1 , wherein the updated treatment plan is generated without performing volumetric image reconstruction.

10. The computer program product of claim 1 , wherein the tracking data is planar k-space data.

11. The computer program product of claim 1 , wherein the tracking data is three-dimensional k-space data.

12. The computer program product of claim 1 , wherein the tracking data corresponds to a subset of radial k-space data that is used to reconstruct the portion of the patient anatomy corresponding to a closest match of the tracking data with the reference data.

13. The computer program product of claim 1 , wherein the reference data is k-space data.

14. The computer program product of claim 1 , wherein the reference data is planar image data.

15. The computer program product of claim 1 , wherein the partial volumetric data is k-space data.

16. The computer program product of claim 1 , wherein the partial volumetric data is planar image data.

17. The computer program product of claim 1 , the operations further comprising accumulating the volumetric data into bins corresponding to one or more motion states.

18. A system comprising:

at least one programmable processor; and

a non-transitory machine-readable medium storing instructions which, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

determining, prior to a current treatment of a patient, a correlation between tracking data and volumetric data; wherein the tracking data is k-space data;

obtaining, prior to the current treatment of the patient, reference data related to the tracking data;

partitioning, prior to the current treatment of the patient, the reference data into a plurality of motion states; wherein the motion states are divided from an imaged motion;

generating a prediction of achievability of a treatment plan based on a current patient condition, the generating utilizing the correlation between the tracking data and the volumetric data, along with the reference data; and

updating the treatment plan based on the prediction of achievability.

19. The system of claim 18 , the operations further comprising delivering radiotherapy based on the updated treatment plan.

20. The system of claim 19 , the operations further comprising utilizing using a particular set of tracking data that is a good predictor of patient motion.

21. The system of claim 19 , the operations further comprising utilizing using a particular set of tracking data that is a good predictor of patient volumetric motion.

22. The system of claim 19 , the operations further comprising utilizing using a particular set of tracking data that at least partially predicts patient motion.

23. The system of claim 19 , the operations further comprising utilizing using a particular set of tracking data that at least partially predicts patient volumetric motion.

24. The system of claim 19 , wherein determining the correlation between tracking data and volumetric data further comprises utilizing a model that predicts motion of patient anatomy.

25. The system of claim 24 , the operations further comprising training the model by comparing tracking data and volumetric data received during the radiotherapy.

26. The system of claim 18 , wherein the updated treatment plan is generated without performing volumetric image reconstruction.

27. The system of claim 18 , wherein the tracking data is planar k-space data.

28. The system of claim 18 , wherein the tracking data is three-dimensional k-space data.

29. The system of claim 18 , wherein the tracking data corresponds to a subset of radial k-space data that is used to reconstruct the portion of the patient anatomy corresponding to a closest match of the tracking data with the reference data.

30. The system of claim 18 , wherein the reference data is k-space data.

31. The system of claim 18 , wherein the reference data is planar image data.

32. The system of claim 18 , wherein the partial volumetric data is k-space data.

33. The system of claim 18 , wherein the partial volumetric data is planar image data.

34. The system of claim 18 , the operations further comprising accumulating the volumetric data into bins corresponding to one or more motion states.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 12, 2024
From: VIEWRAY, INC.; VIEWRAY TECHNOLOGIES, INC.
To: VIEWRAY SYSTEMS, INC.
Reel/Frame 067096/0625 →
SECURITY INTEREST Recorded Mar 24, 2023
From: VIEWRAY TECHNOLOGIES, INC.; VIEWRAY, INC.
To: MIDCAP FUNDING IV TRUST
Reel/Frame 063157/0703 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 30, 2022
From: KAWRYKOW, IWAN; GERGANOV, GEORGI; DEMPSEY, JAMES F.
To: VIEWRAY TECHNOLOGIES, INC.
Reel/Frame 059448/0640 →
Continuity (3)
Continuation 15628255 · Jun 20, 2017
Provisional Application 62353530 · Jun 22, 2016
Related Publication 20220218223A1 · Jul 14, 2022
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