Device and method for delineating a metal object for artifact reduction in tomography images

• US 10,453,198 B2
• Filed: 03/14/2017
• Issued: 10/22/2019
• Est. Priority Date: 03/15/2016
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Healthcare GMBH (Siemens AG)
• Inventors: Manhart, Michael
• Primary Examiner(s): Yentrapati, Avinash
• Application Number:
  US15/458,206
• Publication Number:
  US 20170270687A1
• Time in Patent Office: 952 Days
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/12 Arrangements for detecting or locating foreign bodies
• CPC Class Codes: A61B 6/4441 the rigid structure being a C-arm or U-arm
• CPC Class Codes: A61B 6/5205 involving processing of raw data to produce diagnostic data
• CPC Class Codes: A61B 6/5258 involving detection or reduction of artifacts or noise
• CPC Class Codes: G06T 11/008 Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 7/11 Region-based segmentation

Abstract

A method for delineating a metal object for artifact reduction in tomography images is provided. Projection images of a body containing the metal object are received and a volume model of the body is formed on the basis of the projection images. Voxels that are part of a region encompassing the metal object and at least one metal artifact are chosen in the volume model on the basis of segmentation criterion and combined to form a first volume mask. The first volume mask is projected onto the projection images and a respective projection mask is generated. The projection masks are is altered on the basis of pixel values of the projection images. A second volume mask is determined from the altered projection masks. The second volume mask is altered on the basis of voxel values of the volume model and the altered volume mask is supplied as a description of the metal object.

Claims

1. A method for delineating a metal object for artifact reduction in tomography images, the method comprising:

receiving, from a projection device, a plurality of projection images of a body containing the metal object;

generating a volume model of at least part of the body using the plurality of projection images;

selecting a plurality of voxels of the volume model, the plurality of voxels selected to be part of a region encompassing the metal object and at least one metal artifact based on a segmentation criterion, each of the plurality of voxels having a voxel value;

combining the plurality of voxels and the at least one metal object into a first volume mask;

projecting the first volume mask onto each of the plurality of projection images, the projecting resulting in a respective projection mask for each of the projection images;

calculating a metal allocation log-likelihood value for each pixel in the respective projection mask using a statistical mean and a statistical variance of the pixel values of the pixels encompassed by the projection mask;

calculating a background allocation log-likelihood value for each pixel in the respective projection mask using a statistical mean and a statistical variance of the pixel values of the pixels excluded from the projection mask;

defining for each pixel of the region an affiliation to the projection mask based on a comparison of the metal and background allocation log-likelihood values;

altering each respective projection mask so that the region containing the projection mask is delineated in the respective projection image;

determining a second volume mask from the altered projection masks;

altering the second volume mask based on the plurality of voxel values of the volume model; and

providing the altered second volume mask as a three-dimensional form of the metal object.

2. The method of claim 1, wherein the segmentation criterion is based on threshold value detection.

3. The method of claim 1, wherein the metal allocation log-likelihood value and the background allocation log-likelihood value are further calculated based on an edge detection.

4. The method of claim 1, wherein the metal allocation log-likelihood value and the background allocation log-likelihood value are further calculated using a vicinity description for each pixel position that is dependent on a number of adjoining pixels encompassed by the projection mask and a number of adjoining pixels excluded from the projection mask.

5. The method of claim 1, wherein metal allocation log-likelihood value and the background allocation log-likelihood value are further calculated based on a function of a non-altered projection mask.

6. The method of claim 1, wherein altering the second volume mask comprises:

altering in that in the volume model, a region containing the second volume mask is delineated;

determining a volume-based metal allocation function of voxel values to the metal body and a volume-based background allocation function of voxel values to a metal-free volume background from voxel values of voxels of the region; and

defining for each voxel of the region the affiliation to the second volume mask using each voxel'"'"'s respective voxel value, the volume based metal allocation function, and the volume based background allocation function.

7. The method of claim 6, wherein the volume based metal allocation function, the volume based background allocation function, or the volume based metal allocation function and the volume based background allocation function is a log-likelihood function.

8. The method of claim 6, wherein the volume based metal allocation function, the volume based background allocation function, or the volume based metal allocation function and the volume based background allocation function is determined based on a statistical mean or a statistical variance of the voxel values of the voxels encompassed by the second volume mask.

9. The method of claim 6, wherein the volume based metal allocation function, the volume based background allocation function, or the volume based metal allocation function and the volume based background allocation function is formed based on edge detection.

10. The method of claim 6, wherein the volume based metal allocation function, the volume based background allocation function, or the volume based metal allocation function and the volume based background allocation function consider a vicinity description for each voxel position, wherein the vicinity description is dependent on a number of adjoining voxels encompassed by the second volume mask and a number of adjoining voxels excluded from the second volume mask.

11. The method of claim 6, wherein the volume based metal allocation function, the volume based background allocation function, or the volume based metal allocation function and the volume based background allocation function are determined as a function of a non-altered second volume mask.

12. An apparatus for delineating a metal object in a volume model formed from a plurality of projection images of a body containing the metal object, the apparatus comprising:

a receiver configured to receive the plurality of projection images; and

a processor configured to generate a volume model of at least part of the body using the plurality of projection images, select a plurality of voxels that are part of a region encompassing the metal object and at least one metal artifact based on a segmentation criterion, each of the plurality of voxels having a voxel value, the processor further configured to combine the selected plurality of voxels and the at least one metal object to form a first volume mask, project the first volume mask onto each of the plurality of projection images to generate respective projection masks, alter each respective projection masks so that the region containing the projection mask is delineated in the respective projection image, calculate a metal allocation log-likelihood value for each pixel in the respective projection mask using a statistical mean and a statistical variance of the pixel values of the pixels encompassed by the projection mask, calculate a background allocation log-likelihood value for each pixel in the respective projection mask using a statistical mean and a statistical variance of the pixel values of the pixels excluded from the projection mask, define for each pixel of the region an affiliation to the projection mask based on the metal allocation and background log-likelihood values, determine a second volume mask from the altered projection masks, alter the second volume mask based on the plurality of voxel values of the volume model, and provide the altered second volume mask as a three-dimensional form of the metal object.

13. The apparatus of claim 12, wherein the segmentation criterion is based on threshold value detection.

14. The apparatus of claim 12, wherein metal allocation log-likelihood value and the background allocation log-likelihood value are further calculated based on an edge detection.