IP Library Granted Patent US 9,398,855
Granted Patent B2
US 9,398,855 · App. 14/291,568 · Granted Jul 26, 2016

System and method for magnetic resonance imaging based respiratory motion correction for PET/MRI

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Quick Facts
Patent No.
US 9,398,855
App. No.
14/291,568
Granted
Jul 26, 2016
Kind
B2
Abstract

A method and system for magnetic resonance imaging (MRI) based motion correction in position emission tomography (PET) images is disclosed. A static 3D magnetic resonance (MR) image of a patient is received. PET image data of the patient and a series of 2D MR images of the patient acquired simultaneous to the acquisition of the PET image data are received. A 3D+t motion field is estimated by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image. A motion corrected PET image is generated based on the estimated 3D+t motion field using motion corrected PET reconstruction.

Claims (49)

1. A method for magnetic resonance imaging (MRI) based motion correction in position emission tomography (PET) images, comprising:

receiving a static 3D magnetic resonance (MR) image of a patient;

receiving PET image data of the patient;

receiving a series of 2D MR images of the patient acquired at a plurality of time points simultaneous to acquisition of the PET image data;

estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image, wherein estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image comprises:

estimating the 3D+t motion field using the registration of the series of 2D MR images and the static 3D MR image and a predictive respiratory motion model; and

generating a motion corrected PET image based on the estimated 3D+t motion field using motion corrected PET reconstruction.

2. The method of claim 1 , wherein estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image comprises:

estimating a 3D+t motion field that minimizes an energy function including an image energy term and a model energy term.

3. The method of claim 2 , wherein the image energy term compares each 2D MR image to a corresponding slice of a transformed volume resulting from transforming the static 3D volume based on the 3D+t motion field at the respective one of the plurality of time points, and the model energy term compares the 3D+t motion field and a predicted motion field calculated using the predictive respiratory motion model.

4. The method of claim 3 , wherein the image energy term is a negative intensity-based similarity measure between each 2D MR image and the corresponding slice of the transformed volume.

5. The method of claim 3 , wherein the model energy term measures a difference between the 3D+t motion field at each of the plurality of time steps and the predictive respiratory motion model at a breathing phase corresponding to each of the plurality of time steps.

6. The method of claim 5 , wherein the breathing phase corresponding to each of the plurality of time steps is detected using a navigation signal.

7. The method of claim 3 , further comprising:

registering the static 3D image to an atlas used to train a standard respiratory motion model to obtain a warping function; and

applying an inverse of the warping function to the standard respiratory motion model to obtain a patient-specific respiratory motion model, wherein the model energy term compares the 3D+t motion field and the patient-specific respiratory motion model.

8. The method of claim 1 wherein the series of 2D MR images are slices in the transverse plane acquired in a sweep along a head-foot direction of the patient.

9. An apparatus for magnetic resonance imaging (MRI) based motion correction in position emission tomography (PET) images, comprising:

a processor; and

a memory storing computer program instructions, which when executed on the processor, cause the processor to perform operations comprising:

receiving a static 3D magnetic resonance (MR) image of a patient;

receiving PET image data of the patient;

receiving a series of 2D MR images of the patient acquired at a plurality of time points simultaneous to acquisition of the PET image data;

estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image, wherein estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image comprises:

estimating the 3D+t motion field using the registration of the series of 2D MR images and the static 3D MR image and a predictive respiratory motion model; and

generating a motion corrected PET image based on the estimated 3D+t motion field using motion corrected PET reconstruction.

10. The apparatus of claim 9 , wherein estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image comprises:

estimating a 3D+t motion field that minimizes an energy function including an image energy term and a model energy term.

11. The apparatus of claim 10 , wherein the image energy term compares each 2D MR image to a corresponding slice of a transformed volume resulting from transforming the static 3D volume based on the 3D+t motion field at the respective one of the plurality of time points, and the model energy term compares the 3D+t motion field and a predicted motion field calculated using the predictive respiratory motion model.

12. The apparatus of claim 11 , wherein the image energy term is a negative intensity-based similarity measure between each 2D MR image and the corresponding slice of the transformed volume.

13. The apparatus of claim 11 , wherein the model energy term measures a difference between the 3D+t motion field at each of the plurality of time steps and the predictive respiratory motion model at a breathing phase corresponding to each of the plurality of time steps.

14. The apparatus of claim 11 , the operations further comprising:

registering the static 3D image to an atlas used to train a standard respiratory motion model to obtain a warping function; and

applying an inverse of the warping function to the standard respiratory motion model to obtain a patient-specific respiratory motion model, wherein the model energy term compares the 3D+t motion field and the patient-specific respiratory motion model.

15. A non-transitory computer readable medium storing computer program instructions for magnetic resonance imaging (MRI) based motion correction in position emission tomography (PET) images, the computer program instructions when executed on a processor cause the processor to perform operations comprising:

receiving a static 3D magnetic resonance (MR) image of a patient;

receiving PET image data of the patient;

receiving a series of 2D MR images of the patient acquired at a plurality of time points simultaneous to acquisition of the PET image data;

estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image, wherein estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image comprises:

estimating the 3D+t motion field using the registration of the series of 2D MR images and the static 3D MR image and a predictive respiratory motion model; and

generating a motion corrected PET image based on the estimated 3D+t motion field using motion corrected PET reconstruction.

16. The non-transitory computer readable medium of claim 15 , wherein estimating a 3D+t motion field by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image comprises:

estimating a 3D+t motion field that minimizes an energy function including an image energy term and a model energy term.

17. The non-transitory computer readable medium of claim 16 , wherein the image energy term compares each 2D MR image to a corresponding slice of a transformed volume resulting from transforming the static 3D volume based on the 3D+t motion field at the respective one of the plurality of time points, and the model energy term compares the 3D+t motion field and a predicted motion field calculated using the predictive respiratory motion model.

18. The non-transitory computer readable medium of claim 17 , wherein the image energy term is a negative intensity-based similarity measure between each 2D MR image and the corresponding slice of the transformed volume.

19. The non-transitory computer readable medium of claim 17 , wherein the model energy term measures a difference between the 3D+t motion field at each of the plurality of time steps and the predictive respiratory motion model at a breathing phase corresponding to each of the plurality of time steps.

20. The non-transitory computer readable medium of claim 17 , the operations further comprising:

registering the static 3D image to an atlas used to train a standard respiratory motion model to obtain a warping function; and

applying an inverse of the warping function to the standard respiratory motion model to obtain a patient-specific respiratory motion model, wherein the model energy term compares the 3D+t motion field and the patient-specific respiratory motion model.

Assignments (5)
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNEE PREVIOUSLY RECORDED AT REEL: 066088 FRAME: 0256. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Jan 17, 2024
From: SIEMENS HEALTHCARE GMBH
To: SIEMENS HEALTHINEERS AG
Reel/Frame 071178/0246 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 20, 2023
From: SIEMENS HEALTHCARE GMBH
To: SIEMENS HEALTHINEERS AG
Reel/Frame 066088/0256 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 31, 2017
From: SIEMENS AKTIENGESELLSCHAFT
To: SIEMENS HEALTHCARE GMBH
Reel/Frame 042535/0623 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 31, 2014
From: SIEMENS CORPORATION
To: SIEMENS AKTIENGESELLSCHAFT
Reel/Frame 033433/0871 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 18, 2014
From: CHEFD'HOTEL, CHRISTOPHE; LIAO, RUI; MIAO, SHUN
To: SIEMENS CORPORATION
Reel/Frame 033128/0528 →