Non-Contrast-Enhanced 4D MRA Using Compressed Sensing Reconstruction

• US 20130121550A1
• Filed: 10/28/2011
• Published: 05/16/2013
• Est. Priority Date: 11/10/2010
• Status: Active Grant

• Current Assignee: Siemens Healthineers AG (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Chang, Ti-chiun, Nadar, Mariappan S., Ghring, Jens, Zenge, Michael, Block, Kai Tobias, Schmitt, Peter, Mueller, Edgar
• Granted Patent: US 8,879,852 B2
• Time in Patent Office: Days
• US Class Current: 382/130
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: G01R 33/561 by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
• CPC Class Codes: G01R 33/5635 Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA]
• CPC Class Codes: G01R 33/56545 caused by finite or discrete sampling, e.g. Gibbs ringing, truncation artefacts, phase aliasing artefacts
• CPC Class Codes: G06T 11/003 Reconstruction from projections, e.g. tomography

Abstract

A reconstructed image is rendered of a patient by a processor from a set of undersampled MRI data by first subtracting two repetitions of the acquired data in k-space to create a third dataset. The processor reconstructs the image by minimizing an objective function under a constraint related to the third dataset, wherein the objective function includes applying a Karhunen-Loeve Transform (KLT) to a temporal dimension of data. The objective function under the constraint is expressed as arg min_f{∥φ(f)∥₁ subject to ∥Af−y∥₂≦ε}. The reconstructed image is an angiogram which may be a 4D angiogram. The angiogram is used to diagnose a vascular disease.

Claims

1. A method for reconstructing an image of an object from image data, comprising:

a processor receiving non-contrast-enhanced magnetic resonance image data of the object containing a first and a second dataset in k-space obtained with a medical imaging device which is acquired in an undersampled manner;

the processor generating a sparsified third dataset by subtracting the first from the second dataset in k-space; and

the processor iteratively minimizing an objective function under a constraint related to the third dataset to reconstruct the image of the object.

2. The method of claim 1, wherein the objective function includes applying a Karhunen-Loeve Transform (KLT).

3. The method of claim 1, wherein the minimizing of the objective function under the constraint is expressed as

arg min_f{∥

φ

(f)∥

₁ subject to ∥

Af−

y∥

₂≦

ε

}, wherein

f represents an iterative image;

φ

(f) represents the Karhunen-Loeve Transform of f;

y represents the third dataset;

A represents a Fourier operator that maps the trial image into k-space; and

ε

represents a parameter that accounts for a deviation between a measurement and ideal data.

4. The method of claim 1, wherein the Karhunen-Loeve Transform is applied to data defined in a temporal dimension.

5. The method of claim 2, wherein the processor suppresses an undersampling caused coefficient in a principal component in the KLT transformed data.

6. The method of claim 1, wherein the object is a patient.

7. The method of claim 1, wherein the reconstructed image is an angiogram.

8. The method of claim 6, wherein the angiogram is a 4D angiogram.

9. The method of claim 1, wherein the method is used in diagnosing a vascular disease.

10. A system to reconstruct an image of an object from image data, comprising:

a memory enabled to store data;

a processor enabled to execute instruction to perform the steps;

receiving non-contrast-enhanced magnetic resonance image data of the object containing a first and a second dataset in k-space obtained with a medical imaging device which is acquired in an undersampled manner;

generating a sparsified third dataset by subtracting the first from the second dataset ink-space; and

iteratively minimizing an objective function under a constraint related to the third dataset to reconstruct the image of the object.

11. The system of claim 10, wherein the objective function includes applying a Karhunen-Loeve Transform (KLT).

12. The system of claim 10, wherein the minimizing of the objective function under the constraint is expressed as

arg min_f{∥

φ

(f)∥

₁ subject to ∥

Af−

y∥

₂≦

ε

}, wherein

f represents an iterative image;

φ

(f) represents the Karhunen-Loeve Transform of f,

y represents the third dataset;

A represents a Fourier operator that maps the trial image into k-space; and

ε

represents a parameter that accounts for a deviation between a measurement and ideal data.

13. The system of claim 10, wherein the Karhunen-Loeve Transform is applied to data defined in a temporal dimension.

14. The system of claim 11, wherein the processor suppresses an undersampling caused coefficient in a principal component in the KLT transformed data.

15. The system of claim 10, wherein the object is a patient.

16. The system of claim 10, wherein the reconstructed image is an angiogram.

17. The system of claim 16, wherein the angiogram is a 4D angiogram.

18. The system of claim 10, wherein the system is used in diagnosing a vascular disease.