Intra reconstruction motion correction

• US 10,304,219 B2
• Filed: 06/12/2015
• Issued: 05/28/2019
• Est. Priority Date: 06/13/2014
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Cachovan, Michal, Vija, Alexander Hans
• Primary Examiner(s): Grant, II, Jerome
• Application Number:
  US15/315,313
• Publication Number:
  US 20180033166A1
• Time in Patent Office: 1,446 Days
• Field of Search: 382107
• CPC Class Codes: G06T 11/005 Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• CPC Class Codes: G06T 11/006 Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10108 Single photon emission computed tomography [SPECT]
• CPC Class Codes: G06T 2207/20004 Adaptive image processing
• CPC Class Codes: G06T 2207/20182 Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 2211/412 Dynamic
• CPC Class Codes: G06T 2211/424 Iterative
• CPC Class Codes: G06T 2211/464 Dual or multimodal imaging, i.e. combining two or more imaging modalities
• CPC Class Codes: G06T 7/248 involving reference images or patches

Abstract

A set of first modality data is provided to an intra-reconstruction motion correction method. The set of first modality data includes a plurality of views. A set of second modality data is provided to the method. A motion estimate is generated for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data. A motion corrected model of the set of first modality data is generated by a forward projection including the motion estimate.

Claims

1. A method of processing data for medical imaging, the method comprising:

providing a set of first modality data including a plurality of views from a first imaging modality;

providing a set of second modality data from a second imaging modality;

generating a motion estimate from each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data;

generating a motion corrected model of the set of first modality data by forwarding projecting the set of first modality data including the motion estimate; and

wherein neither the first nor the second image modality comprises Magnetic Induction Tomography.

2. The method of claim 1, wherein the motion corrected model is generated by deflecting each projection in the first modality data based on the motion estimate.

3. The method of claim 2, wherein the motion estimate comprises an adapted system matrix.

4. The method of claim 3, wherein the adapted system matrix is represented by the equation:

A^T=PTK^PSFM

wherein P is a projector, T is a motion operator, K^PSF is the point spread function convolution operator, and M is the attenuation correction operator.

5. The method of claim 3, wherein the motion corrected model is generated according to the equation:

6. The method of claim 1, wherein the motion estimate is generated by a patient motion assessment tool.

7. The method of claim 6, wherein the patient motion assessment tool generates a motion estimation in a projection space.

8. The method of claim 6, wherein the patient motion assessment tool generates a motion estimation in an image space.

9. The method of claim 1, wherein the first imaging modality is the nuclear imaging modality comprising a SPECT imaging modality and the second imaging modality comprises a CT imaging modality.

10. The method of claim 1, comprising performing a point spread function convolution of the motion-corrected model to identify a depth dependent detector response.

11. The method of claim 1, wherein the motion-corrected model is iteratively updated by comparing the first modality data to an nth iteration-motion corrected model to generate updated first modality data, wherein the nth iteration motion-corrected model is a motion-corrected model generated by an nth iteration of the method of processing data.

12. A computer readable storage medium, tangibly embodying a program of instructions executable by a processor to cause the processor to perform the steps of:

generating a set of first modality data including a plurality of views, wherein the set of first modality data is generated by a first modality of an imaging system;

generating a set of second modality data including the plurality of views, wherein the set of second modality data is generated by a second modality of the imaging system;

calculating a motion matrix for each of the plurality of views in the first modality data by registering the set of first modality data to the set of second modality data; and

generating a motion corrected model of the set of first modality data based on the generated movement data,

wherein the computer readable medium is non-transitory.

13. The non-transitory, computer readable medium of claim 12, wherein the motion corrected model is generated by deflecting each projection in the first modality data based on the movement data.

14. The non-transitory, computer readable medium of claim 13, wherein the motion matrix is an adapted system matrix.

15. The non-transitory, computer readable medium of claim 14, wherein the adapted system matrix is represented by the equation:

A^T=PTK^PSFM

wherein P is a projector, T is a motion operator, K^PSF is the point spread function convolution operator, and M is the attenuation correction operator.

16. The non-transitory, computer readable medium of claim 14, wherein the motion corrected model is generated according to the equation:

17. The non-transitory, computer readable medium of claim 12, wherein the movement data is generated by a patient motion assessment tool.

18. The non-transitory, computer readable medium of claim 17, wherein the patient motion assessment tool comprises a motion estimation in a projection space.

19. The non-transitory, computer readable medium of claim 17, wherein the patient motion assessment tool comprises a motion estimation in an image space.

20. The non-transitory, computer readable medium of claim 12, wherein movement data is generated by estimating motion fields.

21. Method of patient motion assessment, comprising:

receiving a set of first modality data comprising a plurality of views from a first imaging modality;

receiving a set of second modality data comprising the plurality of views from a second imaging modality;

filtering the first modality of data to remove data points outside of a predetermined range;

registering the set of first modality data with the set of second modality data to reduce motion artifacts in the set of first modality data; and

wherein neither the first nor the second image modality comprises Magnetic Induction Tomography.

22. The method of claim 21, wherein the registering is completed by one of a rigid registration technique or a non-rigid registration technique.

23. A method of processing data for medical imaging, the method comprising:

providing a set of first modality data including a plurality of views;

providing a set of second modality data;

generating a motion estimate for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data; and

generating a motion corrected model of the set of first modality data by forwarding projecting the set of first modality data including the motion estimate, wherein the motion corrected model is generated by deflecting each projection in the first modality data based on the motion estimate and wherein the motion estimate comprises an adapted system matrix.

24. A method of processing data for medical imaging, the method comprising:

providing a set of first modality data including a plurality of views;

providing a set of second modality data;

generating a motion estimate for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data, wherein the motion estimate is generated by a patient motion assessment tool; and

generating a motion corrected model of the set of first modality data by forwarding projecting the set of first modality data including the motion estimate.

25. The method of claim 24, wherein the patient motion assessment tool generates a motion estimation in a projection space.

26. The method of claim 24, wherein the patient motion assessment tool generates a motion estimation in an image space.

27. A method of processing data for medical imaging, the method comprising:

providing a set of first modality data including a plurality of views;

providing a set of second modality data;

generating a motion estimate for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data; and

generating a motion corrected model of the set of first modality data by forwarding projecting the set of first modality data including the motion estimate; and

performing a point spread function convolution of the motion-corrected model to identify a depth dependent detector response.

28. A method of processing data for medical imaging, the method comprising:

providing a set of first modality data including a plurality of views;

providing a set of second modality data;

generating a motion estimate for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data; and

generating a motion corrected model of the set of first modality data by forwarding projecting the set of first modality data including the motion estimate, wherein the motion-corrected model is iteratively updated by comparing the first modality data to an nth iteration-motion corrected model to generate updated first modality data, wherein the nth iteration motion-corrected model is a motion-corrected model generated by an nth iteration of the method of processing data.