IP Library Granted Patent US 12,373,965
Granted Patent B2
US 12,373,965 · App. 17/526,935 · Granted Jul 29, 2025

Registration of time-separated X-Ray images

Inventors: Amir Lev-Tov (Tel Aviv, IL); Shay Shimon Peretz (Tel Aviv, IL); Yaniv Ben Zriham (Binyamina, IL); Moshe Shoham (Hoshaya, IL)
Assignee: Mazor Robotics Ltd.
G06T7/38G06T7/215G06T7/337G06V10/25G16H30/20G16H30/40G06T2207/10116G06T2207/30244G06V2201/033
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 12,373,965
App. No.
17/526,935
Granted
Jul 29, 2025
Kind
B2
Abstract

A method according to one embodiment of the present disclosure comprises receiving a first image of a patient's anatomy, the first image generated at a first time and depicting a plurality of rigid elements; receiving a second image of the patient's anatomy, the second image generated at a second time after the first time and depicting the plurality of rigid elements; determining a transformation from the first image to the second image for each one of the plurality of rigid elements to yield a set of transformations; calculating a homography for each transformation in the set of transformations to yield a set of homographies; and identifying, using the set of homographies, a common portion of each transformation attributable to a change in camera pose, and an individual portion of each transformation attributable to a change in rigid element pose.

Claims (39)

1. A method comprising:

receiving a first image of a patient's anatomy, the first image generated at a first time and depicting a plurality of rigid elements, each of the plurality of rigid elements movable with respect to at least one other of the plurality of rigid elements;

receiving a second image of the patient's anatomy, the second image generated at a second time after the first time and depicting the plurality of rigid elements;

determining a transformation from the first image to the second image for each one of the plurality of rigid elements to yield a set of transformations; and

identifying, using the set of transformations, a common portion of each transformation attributable to a change in camera pose by utilizing clustering to isolate transformations in the set of transformations that result solely from the change in camera pose from transformations in the set of transformations that result from a combination of the change in camera pose and the change in rigid element pose; and

identifying, using the set of transformations, an individual portion of each transformation attributable to a change in rigid element pose by utilizing the clustering.

2. The method of claim 1 , further comprising:

registering the second image to the first image based on the identified common portion of each transformation.

3. The method of claim 1 , further comprising:

updating a pre-operative model based on the individual portion of each transformation.

4. The method of claim 1 , further comprising:

updating a registration of one of a robotic space or a navigation space to an image space based on one of the common portion of each transformation or the individual portion of each transformation.

5. The method of claim 1 , wherein each transformation is a homography, and the set of transformations is a set of homographies.

6. The method of claim 1 , wherein the step of identifying comprises determining a most coherent cluster and using a mean of the most coherent cluster as a transformation corresponding to the change in camera pose in a step of projecting the second image onto the first image.

7. The method of claim 1 , wherein the step of registering comprises correlating both the first image and the second image to a common vector space.

8. The method of claim 1 , wherein the first image is a preoperative image.

9. The method of claim 1 , wherein at least one of the first image and the second image is an intraoperative image.

10. The method of claim 1 , wherein determining the transformation comprises identifying at least four points on each one of the plurality of rigid elements as depicted in the first image, and a corresponding at least four points on each one of the plurality of rigid elements as depicted in the second image.

11. The method of claim 1 , wherein the first image and the second image are two-dimensional.

12. The method of claim 1 , wherein the first image and the second image are three-dimensional.

13. The method of claim 1 , wherein the plurality of rigid elements includes a plurality of vertebrae of the patient's spine.

14. The method of claim 1 , wherein the plurality of rigid elements comprises at least one implant.

15. The method of claim 1 , further comprising quantifying a change in pose of at least one of the plurality of rigid elements from the first time to the second time.

16. A system, comprising:

at least one processor; and

a memory storing instructions for execution by the processor that, when executed, cause the processor to:

receive a first image of a patient's anatomy, the first image generated at a first time and depicting a plurality of rigid elements, each of the plurality of rigid elements movable with respect to at least one other of the plurality of rigid elements;

receive a second image of the patient's anatomy, the second image generated at a second time after the first time and depicting the plurality of rigid elements;

determine a transformation from the first image to the second image for each one of the plurality of rigid elements to yield a set of transformations; and

identify, using the set of transformations, a common portion of each transformation attributable to a change in camera pose by utilizing clustering to isolate transformations in the set of transformations that result solely from the change in camera pose from transformations in the set of transformations that result from a combination of the change in camera pose and the change in rigid element pose; and

identify, using the set of transformations, an individual portion of each transformation attributable to a change in rigid element pose by utilizing the clustering.

17. The system of claim 16 , wherein the memory stores instructions for execution by the processor that, when executed, cause the processor to:

register the second image to the first image based on the identified common portion of each transformation.

18. The system of claim 16 , wherein the memory stores instructions for execution by the processor that, when executed, cause the processor to:

update a pre-operative model based on the individual portion of each transformation.

19. The system of claim 16 , wherein the memory stores instructions for execution by the processor that, when executed, cause the processor to:

update a registration of one of a robotic space or a navigation space to an image space based on one of the common portion of each transformation or the individual portion of each transformation.

20. The system of claim 16 , wherein the memory stores instructions for execution by the processor that, when executed, cause the processor to:

determine a most coherent cluster and using a mean of the most coherent cluster as a transformation corresponding to the change in camera pose in a step of projecting the second image onto the first image.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 15, 2021
From: LEV-TOV, AMIR; PERETZ, SHAY S.; BEN ZRIHAM, YANIV; SHOHAM, MOSHE
To: MAZOR ROBOTICS LTD.
Reel/Frame 058117/0421 →
Continuity (2)
Provisional Application 63125822 · Dec 15, 2020
Related Publication 20220189047A1 · Jun 16, 2022
References Cited (23)
US 7855723B2 · Preiss et al. · 2010 [cited by applicant]
US 8218905B2 · Dekel et al. · 2012 [cited by applicant]
US 8676298B2 · Wang et al. · 2014 [cited by applicant]
US 8724865B2 · Hipp et al. · 2014 [cited by applicant]
US 9265463B1 · Hipp et al. · 2016 [cited by applicant]
US 9561004B2 · Forsberg · 2017 [cited by applicant]
US 9901407B2 · Breisacher et al. · 2018 [cited by applicant]
US 20080262345A1 · Fichtinger et al. · 2008 [cited by applicant]
US 20170091919A1 · Karino · 2017 [cited by examiner]
US 20170119316A1 · Herrmann et al. · 2017 [cited by applicant]
US 20170273614A1 · Giphart et al. · 2017 [cited by applicant]
US 20200058098A1 · Hirakawa · 2020 [cited by applicant]
EP 1968015 · 2008 [cited by applicant]
EP 1968015A1 · 2008 [cited by examiner]
EP 3714792 · 2020 [cited by applicant]
EP 3714792A1 · 2020 [cited by examiner]
WO WO2019102473 · 2019 [cited by applicant]
El-Zahraa et al. “Current trends in medical image registration and fusion,” Egyptian Informatics Journal, 2016, vol. 17, pp. 99-124. [cited by applicant]
Forsberg et al. “Model-based registration for assessment of spinal deformities in idiopathic scoliosis,” Physics in Medicine and Biology, vol. 59, No. 2, pp. 311-326. [cited by applicant]
Hill et al. “Medical image registration,” Physics in Medicine and Biology, 2001, vol. 46, pp. R1-R45. [cited by applicant]
Brown “A Survey of Image Registration Techniques,” ACM Computing Surveys, Dec. 1992, vol. 24, No. 4, pp. 325-376. [cited by applicant]
International Search Report and Written Opinion for International (PCT) Patent Application No. PCT/IL2021/051452, dated Mar. 30, 2022, 18 pages. [cited by applicant]
Extended Search Report for European Patent Application No. 25153837.7, dated Apr. 16, 2025, 7 pages. [cited by applicant]