US 12,080,003 B2
Systems and methods for three-dimensional navigation of objects
Eric Finley, San Diego, CA (US); Nissim Avitan, San Diego, CA (US); Justin Smith, San Diego, CA (US); and Yehiel Shilo, San Diego, CA (US)
Assigned to Nuvasive Inc., San Diego, CA (US)
Appl. No. 17/760,963
Filed by Nuvasive, Inc., San Diego, CA (US)
PCT Filed Sep. 24, 2020, PCT No. PCT/US2020/052462
§ 371(c)(1), (2) Date Mar. 16, 2022,
PCT Pub. No. WO2021/061960, PCT Pub. Date Apr. 1, 2021.
Claims priority of provisional application 62/905,370, filed on Sep. 24, 2019.
Prior Publication US 2022/0343518 A1, Oct. 27, 2022
Int. Cl. G06T 7/33 (2017.01); G06T 5/50 (2006.01); G06T 19/20 (2011.01)
CPC G06T 7/33 (2017.01) [G06T 5/50 (2013.01); G06T 19/20 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30204 (2013.01); G06T 2219/2004 (2013.01); G06T 2219/2016 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method for registering a first three-dimensional (3D) medical image dataset taken with a first image capturing device with a second 3D dataset taken with a second image capturing device, the method comprising:
receiving first 3D medical image dataset of anatomical features of a subject with one or more markers, the first 3D medical image dataset acquired in a first 3D coordinate system using the first image capturing device;
receiving the second 3D dataset with the one or more markers, the second 3D medical image dataset in a second 3D coordinate system using the second image capturing device;
obtaining prior knowledge of the one or more markers;
finding a plurality of voxel blobs from the first 3D medical image dataset based on the prior knowledge of the one or more markers;
clustering the plurality of voxel blobs into a list of clusters of voxels, each of the clusters representing a candidate of the one or more markers;
for each cluster of voxels, finding a line passing at least a number of voxels in the cluster;
for each cluster of voxels, roughly fitting the cluster to one or more pre-determined marker types with one or more parameters, and fine-tune fitting the cluster to the one or more pre-determined marker types with more than two parameters, thereby
generating a corresponding fitting orientation; and
finding an optimal registration transformation among a plurality of combinations, wherein each combination includes at least three points in the first coordinate system and corresponding points in the second coordinate system determined by the second image capturing device.