Granted Patent B2

US 12,567,501 · App. 17/422,352 · Granted Mar 3, 2026

Inventors: Ivana Ivanovska (Aschheim, DE); Hagen Kaiser (Icking, DE); Pablo Aponte (Haar, DE); Jochen Veigel (Rosenheim, DE)

Assignee: Brainlab SE

G16H40/63G16H20/40

Abstract

Claims (57)

1 . A computer-implemented method of estimating stability of co-registering point-clouds during a point set registration, the point-clouds defining a geometric shape of a three-dimensional surface section of a body, comprising:

acquiring, via signals received from one or more sensors, 3D-surface data which describes spatial positions of a first plurality of points defining the geometric shape of the three-dimensional surface section;

determining 3D-variability data based on the 3D-surface data, which describes variability of the spatial positions of the first plurality of points with respect to each other and across the three-dimensional surface section, wherein a 3D-score factor is established which quantifies the determined variability of the spatial positions as a single numeric value for the entire three-dimensional surface section;

acquiring, via signals received from one or more additional sensors, auxiliary data which describes a value distribution of a condition variable detected over the surface section wherein the condition variable describes a property of the surface section of at least one of:

an emission;

a transmittance; and/or

a reflection

of electromagnetic radiation;

determining auxiliary-variability data based on the auxiliary data, which describes variability of the value distribution of the condition variable, wherein an auxiliary-score factor is established which quantifies the determined variability of the value distribution of the condition variable over the surface section as a single numeric value for the entire three-dimensional surface section; and

determining evaluation data based on the 3D-variability data and the auxiliary-variability data, involving:

determining, based at least on the 3D-score factor and the auxiliary-score factor, the evaluation data describing an expected stability of co-registering, in a three-dimensional space, the surface section as defined by a first dataset including the 3D-surface data and the auxiliary data, with the surface section as defined by a second dataset describing at least spatial positions of a second plurality of points defining the geometric shape of the surface section.

2 . The method according to claim 1 , wherein the auxiliary-score factor is established based on at least one of:

distinctive values of the condition variable, which are assigned to a finite number of sections of the value distribution;

a spread between the highest and the lowest value of the condition variable detected within the surface section; and

a continuous value distribution over the surface section.

3 . The method according to claim 1 , further comprising determining overall-variability data, which describes an overall-variability combining the 3D-variability and the auxiliary-variability, wherein an overall-score factor is established as a function of the 3D-score factor and the auxiliary-score factor, which describes the determined overall-variability, specifically as a numeric value.

4 . The method according to claim 3 , wherein at least one of the 3D-score factor, the auxiliary-score factor, the overall score-factor and the expected stability of co-registering the surface section is displayed on a graphical-user-interface (GUI), in a colour coded fashion and/or as a numeric value.

5 . The method according to claim 1 , wherein the surface section is automatically selected from a plurality of possible surface sections of the body, for which a best 3D-score factor, auxiliary-score factor, overall score-factor and/or expected stability of co-registering the surface section is determined.

6 . The method according to claim 1 , wherein an Iterative-Closest-Point (ICP) algorithm or a Normal-Distribution-Transform (NDT) algorithm is performed to co-register the surface section defined by the first plurality of points with the surface section defined by the second plurality of points, depending on whether the determined expected stability is above a predefined minimum stability threshold.

7 . The method according to claim 1 , wherein determining, based at least on the 3D-score factor and the auxiliary-score factor, the evaluation data describing the expected stability of co-registering the surface section comprises:

determining an expected positional deviation in co-registering the surface section, based on the 3D-score factor and the auxiliary-score factor and a model built from a plurality of co-registrations each performed for a respective surface section from a plurality of different surface sections,

wherein the model describes, over the plurality of different surface sections, a mathematical correspondence between: (1) an actual positional deviation measured after performing a respective co-registration for a respective surface section, and (2) a 3D-score factor and/or an auxiliary-score factor, that are determined for the respective surface section.

8 . The method according to claim 7 , wherein the surface section is a previously unknown surface section in addition to the plurality of different surface sections.

9 . A non-transitory computer-readable medium comprising instructions which, when executed by at least one processor of at least one computer, cause the at least one processor to:

acquire, via signals received from one or more sensors, 3D-surface data which describes spatial positions of a first plurality of points defining a geometric shape of a three-dimensional surface section of a body;

determine 3D-variability data based on the 3D-surface data, which describes variability of the spatial positions of the first plurality of points with respect to each other and across the three-dimensional surface section, wherein a 3D-score factor is established which quantifies describes the determined variability of the spatial positions as a single numeric value for the entire three-dimensional surface section; and

acquire, via signals received from one or more additional sensors, auxiliary data which describes a value distribution of a condition variable detected over the surface section wherein the condition variable describes a property of the surface section of at least one of:

an emission;

a transmittance; and/or

a reflection

of electromagnetic radiation;

determine auxiliary-variability data based on the auxiliary data, which describes variability of the value distribution of the condition variable, wherein an auxiliary-score factor is established which quantifies the determined variability of the value distribution over the surface section as a single numeric value for the entire three-dimensional surface section; and

determine evaluation data based on the 3D-variability data and the auxiliary-variability data, involving:

acquire error data describing, for at least one translational and/or at least one rotational degree of freedom and for each of a plurality of different surface sections that are in addition to the surface section, an actual positional deviation in co-registering, in a three-dimensional space, a respective surface section from the plurality of different surface sections,

determine a model that describes, over the plurality of different surface sections, a mathematical correspondence between: (1) an actual positional deviation measured after performing, in the three-dimensional space, a co-registration for the respective surface section, and (2) a 3D-score factor and an auxiliary-score factor, that are determined for the respective surface section, and

determine, utilizing the determined model and based on the 3D-score factor and the auxiliary-score factor, the evaluation data describing an expected positional deviation for co-registering, in the three-dimensional space, the surface section as defined by a first dataset including the 3D-surface data and the auxiliary data, with the surface section as defined by a second dataset describing at least spatial positions of a second plurality of points defining the geometric shape of the surface section.

10 . The non-transitory computer readable medium according to claim 9 , wherein the surface section is a previously unknown surface section.

11 . A medical system, comprising:

at least one computer having at least one processor; and

having associated memory for storing instructions which cause the at least one processor to:

acquire, via signals received from one or more sensors, 3D-surface data which describes spatial positions of a first plurality of points defining a geometric shape of a three-dimensional surface section of a body;

determine 3D-variability data based on the 3D-surface data, which describes variability of the spatial positions of the first plurality of points with respect to each other and across the three-dimensional surface section, wherein a 3D-score factor is established which quantifies the determined variability of the spatial positions as a single numeric value for the entire three-dimensional surface section; and

acquire, via signals received from one or more additional sensors, auxiliary data which describes a value distribution of a condition variable detected over the surface section wherein the condition variable describes a property of the surface section of at least one of:

an emission;

a transmittance; and/or

a reflection

of electromagnetic radiation;

determine auxiliary-variability data based on the auxiliary data, which describes variability of the value distribution of the condition variable, wherein an auxiliary-score factor is established which quantifies the determined variability of the value distribution over the surface section as a single numeric value for the entire three-dimensional surface section; and

determine evaluation data based on the 3D-variability data and the auxiliary-variability data, involving:

acquiring error data describing, for at least one translational and/or at least one rotational degree of freedom and for each of a plurality of different surface sections that are in addition to the surface section, an actual positional deviation in co-registering a respective surface section from the plurality of different surface sections,

determine a model that describes, over the plurality of different surface sections, a mathematical correspondence between: (1) an actual positional deviation measured after performing a co-registration in three-dimensional space, for the respective surface section, and (2) a 3D-score factor and an auxiliary-score factor, that are determined for the respective surface section,

determining, utilizing the determined model and based on the 3D-score factor and the auxiliary-score factor, the evaluation data describing an expected positional deviation for co-registering the surface section as defined by a first dataset including the 3D-surface data and the auxiliary data, with the surface section as defined by a second dataset describing at least spatial positions of a second plurality of points defining the geometric shape of the surface section;

at least one electronic data storage device storing at least the evaluation data; and

a medical device for carrying out a medical procedure on a patient, wherein the at least processor is operably coupled to:

the at least one electronic data storage device for acquiring, from the at least one data storage device, at least the evaluation data, and

the medical device for issuing a control signal to the medical device for controlling an operation of the medical device on the basis of the evaluation data.

12 . The medical system according to claim 11 , wherein the surface section is a previously unknown surface section.

Assignments (2)

Continuity (1)

References Cited (21)