Determination of registration accuracy

Patent metadata

Field	Value
Publication number	US-11295462-B2
Application number	US-201515764789-A
Country	US
Kind code	B2
Filing date	Dec 16, 2015
Priority date	Dec 16, 2015
Publication date	Apr 5, 2022
Grant date	Apr 5, 2022

Abstract

A medical data processing method, performed by a computer (2), for determining error analysis data describing the registration accuracy of a first elastic registration between first and second image data (A, B) describing images of an anatomical structure of a patient, comprising the steps of: —acquiring the first image data (A) describing a first image of the anatomical structure, —acquiring the second image data (B) describing a second image of the anatomical structure, —determining first registration data describing a first elastic registration of the first image data (A) to the second image data (B) by mapping the first image data (A) to the second image data (B) using a registration algorithm, —determining second registration data describing a second elastic registration of the second image data (B) to the first image data (A) by mapping the second image data (B) to the first image data (A) using the registration algorithm, —determining error analysis data describing the registration accuracy of the first elastic registration based on the first registration data and the second registration data.

First claim

The invention claimed is:

1. A system, comprising at least one computer having at least one processor configured to execute a method for determining error analysis data describing registration accuracy of a first elastic registration between first and second image data describing images of an anatomical structure of a patient, the method comprising executing, by the at least one processor of the at least one computer, the steps of:
acquiring, at the at least one processor, the first image data describing a first image of the anatomical structure;
acquiring, at the at least one processor, the second image data describing a second image of the anatomical structure;
determining, by the at least one processor, first registration data describing the first elastic registration of the first image data to the second image data by mapping the first image data to the second image data using a registration algorithm;
determining, by the at least one processor, second registration data describing a second elastic registration of the second image data to the first image data by mapping the second image data to the first image data using the registration algorithm;
determining, by the at least one processor, error analysis data describing the registration accuracy of the first elastic registration based on the first registration data and the second registration data;
wherein determining, by the at least one processor, the error analysis data comprises:
transforming an original position of a data point within the first image data using the first registration data;
transforming the transformed position using the second registration data to obtain a new position of the data point;
calculating a distance between the original position and the new position of the data point to obtain an observed error specific to the anatomical structure;
wherein determining error analysis data comprises determining observed errors for a plurality of data points within the first image data;
wherein determining error analysis data comprises determining at least one statistical parameter from the plurality of observed errors;
wherein determining error analysis data comprises defining at least one data area with respect to the anatomical structure within the first image data and determining at least one local statistical parameter for the observed errors obtained for data points within the at least one data area, wherein the at least one local statistical parameter is a maximum observed error, a median observed error, a mean observed error or a standard deviation of the observed error;
wherein the method further includes the step of acquiring, at the at least one processor, critical structure data describing a position of at least one critical structure corresponding to a region of interest within the anatomical structure in the first image data and calculating the distance between the position of the at least one critical structure and a position of at least one data area within the first image data, the critical structure data representing an area within the first image data which is distinct from the at least one data area;
wherein by calculating the distance between the position of the at least one critical structure and the position of the at least one data area within the first image data, the at least one local statistical parameter for the observed errors within the at least one data area is related to the at least one critical structure.

2. A method for determining error analysis data describing registration accuracy of a first elastic registration between first and second image data describing images of an anatomical structure of a patient, the method comprising executing, by at least one processor of at least one computer, the steps of:
acquiring, at the at least one processor, the first image data describing a first image of the anatomical structure;
acquiring, at the at least one processor, the second image data describing a second image of the anatomical structure;
determining, by the at least one processor, first registration data describing the first elastic registration of the first image data to the second image data by mapping the first image data to the second image data using a registration algorithm;
determining, by the at least one processor, second registration data describing a second elastic registration of the second image data to the first image data by mapping the second image data to the first image data using the registration algorithm;
determining, by the at least one processor, error analysis data describing the registration accuracy of the first elastic registration based on the first registration data and the second registration data;
wherein determining, by the at least one processor, the error analysis data comprises:
transforming an original position of a data point within the first image data using the first registration data;
transforming the transformed position using the second registration data to obtain a new position of the data point;
calculating a distance between the original position and the new position of the data point to obtain an observed error specific to the anatomical structure;
wherein determining error analysis data comprises determining observed errors for a plurality of data points within the first image data;
wherein determining error analysis data comprises determining at least one statistical parameter from the plurality of observed errors;
wherein determining error analysis data comprises defining at least one data area with respect to the anatomical structure within the first image data and determining at least one local statistical parameter for the observed errors obtained for data points within the at least one data area, wherein the at least one local statistical parameter is a maximum observed error, a median observed error, a mean observed error or a standard deviation of the observed error;
wherein the method further includes the step of acquiring, at the at least one processor, critical structure data describing a position of at least one critical structure corresponding to a region of interest within the anatomical structure in the first image data and calculating the distance between the position of the at least one critical structure and a position of at least one data area within the first image data, the critical structure data representing an area within the first image data which is distinct from the at least one data area;
wherein by calculating the distance between the position of the at least one critical structure and the position of the at least one data area within the first image data, the at least one local statistical parameter for the observed errors within the at least one data area is related to the at least one critical structure.

3. The method according to claim 2 , wherein determining, by the at least one processor, the first registration data involves determining a first transformation vector field for transforming the first image data to the second image data and determining, by the at least one processor, the second registration data involves determining a second transformation vector field for transforming the second image data to the first image data, wherein determining the first transformation vector field is independent from determining the second transformation vector field.

4. The method according to claim 2 , further comprising the step of determining, by the at least one processor, an associating function which describes a relation of the observed errors to real mapping errors or target registration errors.

5. The method according to claim 4 , further comprising the step of determining, by the at least one processor, for each of a plurality of sample data points within the first image data a set comprising an observed error and a

Assignees

• Brainlab Ag

Inventors

• Bertram Pascal
• Corsico Elisa Garcia
• Ivanovska Ivana
• Domnik Birte

Classifications

• G06T12/30

  Image post-processing, e.g. metal artefact correction · CPC title

• G06T2207/30168

  Image quality inspection · CPC title

• G06T7/0002

  Inspection of images, e.g. flaw detection · CPC title

• G06T7/30

  Determination of transform parameters for the alignment of images, i.e. image registration · CPC title

• G06T2207/20221

  Image fusion; Image merging · CPC title