Physiological model based non-rigid image registration

• US 7,117,026 B2
• Filed: 06/12/2002
• Issued: 10/03/2006
• Est. Priority Date: 06/12/2002
• Status: Expired due to Fees

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Ye, Jinghan, Shao, Lingxiong, Zhao, Zuo, Da Silva, Angela J.
• Primary Examiner(s): ROBINSON, DANIEL LEON
• Application Number:
  US10/167,621
• Publication Number:
  US 20030233039A1
• Time in Patent Office: 1,574 Days
• Field of Search: 600/411, 600/410, 600/407, 600/414, 600/415, 600/416, 600/424, 600/425, 600/426, 600/1, 600/2, 600/3, 600/4, 600/5, 600/6, 600/7, 600/8, 324/306, 324/307, 324/308, 324/309
• US Class Current: 600/411
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/583 using calibration phantoms
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/38 Registration of image sequences

Abstract

A method for non-rigid registration and fusion of images with physiological modeled organ motions resulting from respiratory motion and cardiac motion that are mathematically modeled with physiological constraints. A method of combining images comprises the steps of obtaining a first image dataset (24) of a region of interest of a subject and obtaining a second image dataset (34) of the region of interest of the subject. Next, a general model of physiological motion for the region of interest is provided (142). The general model of physiological motion is adapted with data derived from the first image data set (140) to provide a subject specific physiological model (154). The subject specific physiological model is applied (172) to the second image dataset (150) to provide a combined image (122).

Claims

1. A method of combining images, the method comprising the steps of:

obtaining a first image dataset from a subject;

obtaining a second image dataset from the subject;

segmenting the first image data set;

segmenting the second image data set;

providing a physiological model;

morphing the physiological model to the segmented first image data set to produce a subject specific physiological phantom;

registering the subject specific physiological phantom with the segmented second image data set to generate a transform; and

applying the transform to one of the first and second image data sets to provide a fused image.

2. A method of combining images, the method comprising the steps of:

obtaining a first image dataset of a region of interest of a subject;

obtaining a second image dataset of the region of interest of the subject;

providing a general model of physiological motion for the region of interest;

adapting the general model of physiological motion with data derived from the first image data set to provide a subject specific physiological model; and

applying the subject specific physiological model to the second image dataset to provide a combined image.

3. The method of combining images of claim 2 wherein the first image dataset is obtained from a first imaging modality and the second image dataset is obtained from a second imaging modality different than the first imaging modality.

4. The method of combining images of claim 3 wherein one of the first and second image datasets is primarily functional image data and the other image dataset is primarily anatomical image data.

5. The method of combining images of claim 2 wherein the first image dataset is obtained from a discrete imaging and the second image dataset is obtained from a discrete imaging system.

6. A method of combining images, the method comprising the steps of:

obtaining a computed tomography image dataset from a subject;

obtaining a nuclear medicine image dataset from the subject;

segmenting the computed tomography image data set;

segmenting the nuclear medicine image data set;

providing a general physiological model;

morphing the physiological model to the segmented computed tomography image data set to produce a subject specific physiological phantom;

registering the subject specific physiological phantom with the segmented nuclear medicine image data set to generate a transform; and

applying the transform to the computed tomography image data set to provide a fused image.

7. A method of combining images, the method comprising the steps of:

obtaining a computed tomography image dataset from a subject;

obtaining a nuclear medicine image dataset from the subject;

segmenting the computed tomography image data set;

segmenting the nuclear medicine image data set;

providing a general physiological model;

morphing the physiological model to the segmented computed tomography image data set to produce a subject specific physiological phantom;

registering the subject specific physiological phantom with the segmented nuclear medicine image data set to generate a transform; and

applying the inverse of the transform to the segmented nuclear medicine image data set to provide a fused image.

8. An image registration and fusion system, the system comprising:

a first image memory for storing a first image data set of a region of interest of a subject;

a second image memory for storing a second image data set of the region of interest of the subject;

a general physiological model for physiological activity relative to the region of interest; and

an image registration and fusion processor, the processor in operative relationship with the first image memory, the physiological model and the second image memory, the image registration and fusion processor being programmed to;

morph the general physiological model to the first image data set to produce a subject specific physiological phantom,

register the subject specific phantom to the second data set to generate a transform, and

apply the transform to one of the first and second image data sets.

9. The system of claim 8 wherein the physiological model is empirically obtained from image data from an imaging scan of a subject.

10. The system of claim 8 wherein the physiological model a compilation of features from image data from an imaging scans of a plurality of different subjects.

11. The system of claim 8 wherein the processor is further programmed to:

segment the first image data set prior to morphing the physiological model, such that the physiological model is morphed to the segmented first image data set.

12. The system of claim 11 wherein the processor is further programmed to:

segment the second image data set, prior to registering to the subject specific phantom, such that the segmented second image is registered to the subject specific transform.

13. The system of claim 12 wherein the subject specific physiological model models motion of the subject in response to physiological motion over time.

14. The system of claim 13 wherein the physiological motion is at least one of respiratory motion and cardiac motion.

15. The system of claim 12 further including:

a first imaging modality which generates the first image data set; and

a second imaging modality different than the first imaging modality which generates the second image data set.

16. The system of claim 15 wherein the first and second imaging modalities each include a different one of scintigraphy, functional MRI (fMRI), single photon emission computed tomography (SPECT), positron emission tomography (PET), perfusion MRI (pMRI), functional CT (fCT), electro impedance tomography (EIT), magnetic resonance elastography (MRE), X-Ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, laparoscopic and laryngoscopic images, magnetic resonance angiography (MRA), digital subtraction angiography (DSA) and computed tomography angiography (CTA).

17. The system of claim 15 wherein one of the first and second imaging modalities generate data that is primarily functional and the other imaging modality generates data that is primarily anatomical.

18. The system of claim 15 wherein the first imaging modality is a discrete imaging system and the second imaging modality is a discrete imaging system.

19. The system of claim 18 including wherein the processor is further programmed to perform the step of:

pre-registering the first and second image datasets obtained from the discrete imaging systems.

20. The system of claim 12 wherein the step of applying the transform is an inverse application of the transform.

21. The system of claim 12 wherein the general physiological model is a non-uniform rational B-spline cardio-torso phantom model.

22. An apparatus for combining images, the apparatus comprising:

a first memory storing a first image dataset of a region of interest of a subject;

a second memory storing a second image dataset of the region of interest of the subject;

a non-subject specific model of physiological motion for the region of interest;

means for adapting the non-subject specific model of physiological motion with data derived from the first image data set to provide a subject specific physiological model; and

means for applying the subject specific physiological model to the second image dataset to provide a combined image.

23. An apparatus for combining images, comprising:

means for obtaining a first image dataset from a subject;

means for obtaining a second image dataset from the subject;

means for segmenting the first image data set;

means for segmenting the second image data set;

means for providing a physiological model;

means for morphing the physiological model to the segmented first image data set to produce a subject specific physiological phantom;

means for registering the subject specific physiological phantom with the segmented second image data set to generate a transform; and

means for applying the transform to one of the first and second image data sets to provide a fused image.

24. An apparatus for combining images, comprising:

means for obtaining a first image dataset of a region of interest of a subject;

means for obtaining a second image dataset of the region of interest of the subject;

means for providing a general model of physiological motion for the region of interest;

means for adapting the general model of physiological motion with data derived from the first image data set to provide a subject specific physiological model; and

means for applying the subject specific physiological model to the second image dataset to provide a combined image.