Image fusion with automated compensation for brain deformation

• US 9,269,140 B2
• Filed: 12/10/2013
• Issued: 02/23/2016
• Est. Priority Date: 12/10/2012
• Status: Active Grant

• Current Assignee: Cleveland Clinic Foundation
• Original Assignee: Cleveland Clinic Foundation
• Inventors: Machado, Andre G.
• Primary Examiner(s): Liew, Alex
• Application Number:
  US14/102,262
• Publication Number:
  US 20140161338A1
• Time in Patent Office: 805 Days
• Field of Search: 382128-134
• US Class Current: 1/1
• CPC Class Codes: A61B 2576/026 for the brain
• CPC Class Codes: A61B 5/0042 for the brain
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 3/14 Transformations for image registration, e.g. adjusting or mapping for alignment of images
• CPC Class Codes: G06T 3/18 Image warping, e.g. rearranging pixels individually
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/0016 involving temporal comparison
• CPC Class Codes: G06T 7/344 involving models
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing

Abstract

A system can include a model to represent a volumetric deformation of a brain corresponding to brain tissue that has been displaced by at least one of disease, surgery or anatomical changes. A fusion engine can perform a coarse and/or fine fusion to align a first image of the brain with respect to a second image of the brain after a region of the brain has been displaced and to employ the deformation model to adjust one or more points on a displacement vector extending through a displaced region of the brain to compensate for spatial deformations that occur between the first and second image of the brain.

Claims

1. A system, comprising:

a model to represent a volumetric deformation of a brain, stored in a memory, corresponding to brain tissue that has been displaced or shifted by at least one of disease, surgery or anatomical changes; and

a fusion engine, executed by a processor, to employ the model to perform a coarse fusion to align a first three-dimensional image of a patient'"'"'s brain with respect to a second three-dimensional image of the patient'"'"'s brain after a region of the brain has been displaced or shifted,

wherein the coarse function comprises;

determining an initial volume of at least one selected anatomic region in the first three-dimensional image of the patient'"'"'s brain and another volume of the at least one selected anatomic region in the second three-dimensional image of the patient'"'"'s brain;

computing shrinkage or growth factors from the first three-dimensional image of the patient'"'"'s brain based on a difference between the initial volume and the another volume for the at least one selected anatomic region;

generating a mapping between the first and second three-dimensional images of the patient'"'"'s brain based on the computed shrinkage or growth factors and at least one volume parameter of the model to provide feedback that compensates for distortion between the first and second three-dimensional images of the patient'"'"'s brain; and

adjusting one or more points on a displacement vector of the model extending through the at least one selected anatomic region to compensate for spatial deformations that occur between the first and second image of the patient'"'"'s brain based on the mapping.

2. The system of claim 1, further comprising a fiducial marker in the first three-dimensional image of the patient'"'"'s brain, the fusion engine being programmed to employ the fiducial marker with the volumetric analysis of the model and apply to the first three-dimensional image of the patient'"'"'s brain to compensate for distortion in the first three-dimensional image of the patient'"'"'s brain for at least one spatial region of interest selected in response to a user input.

3. The system of claim 2, wherein the fiducial marker is selected in the first three-dimensional image of the patient'"'"'s brain in response to a user input or an automated system.

4. The system of claim 1, wherein the brain image includes at least one landmark or fiducial marker, the fusion engine is programmed to apply the volumetric analysis of the model and the at least one landmark or fiducial marker to the first three-dimensional image of the patient'"'"'s brain to compensate further for distortion in the first three-dimensional image of the patient'"'"'s brain in relation to at least one other image.

5. The system of claim 1, wherein the fusion engine is programmed to fuse or co-register the first three-dimensional image of the patient'"'"'s brain with the second three-dimensional image of the patient'"'"'s brain based on the model.

6. The system of claim 5, wherein the model and the fusion engine are employed to fuse or co-register the second three-dimensional image of the patient'"'"'s brain with a brain atlas to provide an adjusted brain atlas.

7. The system of claim 6, further comprising a vector calculator configured to determine a displacement vector for each pixel or voxel in the second three-dimensional image of the patient'"'"'s brain and transform each pixel or voxel into a coordinate system of a stereotactic system.

8. The system claim of 7, wherein the fusion engine processes at least one image event that includes at least one of a location of a fiducial marker, a foreign body inserted in the patient'"'"'s brain, and an anatomical structure after anatomical displacement.

9. The system of claim 8, wherein the image event is correlated or co-registered with a corresponding location in images acquired before the anatomical displacement occurred or after the anatomical displacement has resolved partially or completely.

10. The system claim of 9, wherein the fusion engine compensates for a structural shift that includes a brain shift associated with surgical procedures.

11. A method comprising:

performing a non-rigid alignment between the first three-dimensional image data and the second three-dimensional image data comprising;

determining an initial volume for at least one selected anatomic region in a first three-dimensional brain image for a patient;

determining another volume for the at least one selected anatomic region in a second three-dimensional brain image for the patient;

computing shrinkage or growth factors from the first three-dimensional brain image based on a difference between the initial volume and the another volume for the at least one selected anatomic region; and

generating a mapping between the first and second three-dimensional brain images based on the computed shrinkage or growth factors and at least one volume parameter for the brain to provide feedback that compensates for distortion between the first and second three-dimensional brain images; and

generating a vector field mapping based on the non-rigid alignment to provide displacement data to a user regarding displacement between the first image data and the second image data.

12. The method of claim 11, wherein the at least one selected anatomic region comprises at least one of a ventricular region, a subarachnoid region, a cortical region and a subcortical region.

13. The method of claim 11, further comprising utilizing one or more fiducial markers or landmarks to characterize inhomogeneities in anatomy or inhomogenities in a vector of displacement and align images from a digitized atlas and a brain image.

14. A method, comprising:

accessing a first three-dimensional image data of an anatomical feature for a patient and second three-dimensional image data of the anatomical feature for the patient;

performing a non-rigid alignment between the first three-dimensional image data and the second three-dimensional image data comprising;

determining an initial volume for at least one selected anatomic region in the first three-dimensional image data and determining another volume for the at least one selected anatomic region in the second three-dimensional image data;

computing a shrinkage or growth factor from the first three-dimensional brain image based on a difference between the initial volume and the another volume for the at least one selected anatomic region; and

generating a mapping between the first and second three-dimensional image data based on the computed shrinkage or growth factors and at least one volume parameter for the brain to provide feedback that compensates for distortion between the first and second three-dimensional image data; and

generating a vector field mapping based on the non-rigid alignment to provide displacement data to a user regarding displacement between the first image data and the second image data.

15. The method of claim 14, further comprising performing a rigid alignment between the first image data and the second image data to generate a rigidly aligned image.

16. The method of claim 15, wherein the rigid alignment or the non-rigid alignment includes at least one of a normalized mutual information correlation metric, a gradient descent optimization, and a linear interpolation between images.

17. The method of claim 16, wherein the non-rigid registration divides the images into a lattice and iteratively evaluates and optimizes correlation metrics for each cell in the lattice.

18. The method of claim 16, wherein the non-rigid registration employs intermediate results to adjust a continuous nonlinear mapping between the first image data and the second image data which is used to generate the correlation metrics.

19. The method of claim 14, further comprising generating a model which describes how the anatomical feature shifts over time and employing the model to update the vector field mapping over time.