MULTI-PARAMETRIC PET-MR IMAGING AND MULTI-MODALITY JOINT IMAGE RECONSTRUCTION

• US 20160291105A1
• Filed: 02/23/2016
• Published: 10/06/2016
• Est. Priority Date: 02/24/2015
• Status: Abandoned Application

• Current Assignee: New York University
• Original Assignee: New York University
• Inventors: Knoll, Florian, Cloos, Martijn Anton Hendrik, Sodickson, Daniel K.
• Application Number:
  US15/051,450
• Publication Number:
  US 20160291105A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0035 adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
• CPC Class Codes: A61B 5/0042 for the brain
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 6/037 Emission tomography
• CPC Class Codes: A61B 6/4417 related to combined acquisition of different diagnostic modalities
• CPC Class Codes: A61B 6/464 involving a plurality of displays
• CPC Class Codes: A61B 6/486 involving generating temporal series of image data
• CPC Class Codes: A61B 6/501 for diagnosis of the head, e.g. neuroimaging or craniography
• CPC Class Codes: A61B 6/5205 involving processing of raw data to produce diagnostic data
• CPC Class Codes: A61B 6/5211 involving processing of medical diagnostic data
• CPC Class Codes: A61B 6/5247 combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound
• CPC Class Codes: G01R 33/481 MR combined with positron emission tomography [PET] or single photon emission computed tomography [SPECT]
• CPC Class Codes: G01R 33/4824 using a non-Cartesian trajectory
• CPC Class Codes: G01R 33/50 based on the determination of relaxation times , e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences
• CPC Class Codes: G01R 33/5602 by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of acquiring PET and MR images simultaneously includes obtaining raw k-space data for continuous MR volumes and acquiring PET information. The method further includes performing a joint multi-modality image reconstruction and generating a set of PET and MR images. The method additionally includes generating a set of MR fingerprints from the reconstructed MR images and using the MR fingerprints to generate a set of parameter maps.

Claims

1. A method of acquiring PET and MR images simultaneously, comprising:

obtaining raw k-space data for continuous MR volumes,

acquiring PET information

performing a joint multi-modality image reconstruction, generating a set of PET and MR images;

generating a set of MR fingerprints from the reconstructed MR images;

using the MR fingerprints to generate a set of parameter maps.

2. The method of claim 1, further comprising:

compressing the at least one set of reconstructed fingerprints.

3. The method of claim 1, further comprising:

comparing the at least one set of reconstructed fingerprints to a plurality of signal evolutions.

4. The method of claim 2, further comprising:

obtaining quantitative MR maps after comparing the at least one set of reconstructed fingerprints to the plurality of signal evolutions, and

performing retrospective contrast generation.

5. The method of claim 1, further comprising:

producing synthetic contrasts, including at least one of a T₁ contrast, T₂ contrast, a FLAIR contrast and an MPRAGE contrast.

6. The method of claim 1, wherein:

the PET and MR images are acquired within 5-10 minutes.

7. The method of claim 1, further comprising:

automatically filtering RF-field non-uniformities.

8. The method of claim 2, wherein each of the MR fingerprints is fitted pixel-by-pixel to respective signal evolution of the plurality of signal evolutions.

9. A computer implemented system for multi-parametric PET-MR IMAGING and multi-modality joint image reconstruction comprising:

a PET-MR scanner;

a MR control unit in communication with the PET-MR scanner;

a PET control unit in communication with the PET-MR scanner;

non-transitory computer-readable memory in communication with the PET-MR scanner having instructions stored therein for;

obtaining raw k-space data for continuous MR volumes, and

acquiring PET information and performing a joint multi-modality image reconstruction to generate a set of PET images and at least one set of reconstructed MR fingerprints.

10. The system of claim 9, further wherein the memory includes instructions for using the MR fingerprints to generate a set of parameter maps.

11. The system of claim 9, further comprising:

compressing the at least one set of reconstructed fingerprints.

12. The system of claim 9, further wherein the memory includes instructions for comparing the at least one set of reconstructed fingerprints to a plurality of signal evolutions.

13. The system of claim 10, further wherein the memory includes instructions for obtaining quantitative MR maps after comparing the at least one set of reconstructed fingerprints to the plurality of signal evolutions, and

performing retrospective contrast generation.

14. The system of claim 9, further wherein the memory includes instructions for producing synthetic contrasts, including at least one of a T₁ contrast, T₂ contrast, a FLAIR contrast and an MPRAGE contrast.

15. The system of claim 9, wherein:

the PET and MR images are acquired within 5-10 minutes.

16. The system of claim 9, further wherein the memory includes instructions for automatically filtering RF-field non-uniformities.

17. The system of claim 10, wherein each of the MR fingerprints is fitted pixel-by-pixel to respective signal evolution of the plurality of signal evolutions.

18. A method of simultaneously performing PET and MR imaging, comprising:

obtaining raw PET and MR data, including a plurality of 3D MRF image volumes, and

reconstructing PET and MR images;

wherein E is a mapping parameter, X_MR is the plurality of 3D MRF image volumes, k is the number of undersampled MR k-space datasets, J is a total number of PET lines of response, j are indices corresponding to the PET lines of response, f is sinogram data, A is a PET projection operator, x_PET is a PET image, λ

is a regularization parameter, and ψ

is a sparsifying transform.

19. The method of claim 18, wherein the PET and MR images are reconstructed in accordance with the following equation: