Tool for accurate quantification in molecular MRI

• US 8,781,197 B2
• Filed: 04/28/2009
• Issued: 07/15/2014
• Est. Priority Date: 04/28/2008
• Status: Active Grant

• Current Assignee: Cornell University
• Original Assignee: Cornell University
• Inventors: Wang, Yi, de Rochefort, Ludovic, Kressler, Bryan, Liu, Tian, Spincemaille, Pascal
• Primary Examiner(s): Bitar, Nancy
• Application Number:
  US12/990,148
• Publication Number:
  US 20110044524A1
• Time in Patent Office: 1,904 Days
• Field of Search: None
• US Class Current: 382/131
• CPC Class Codes: G01R 33/54 Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
• CPC Class Codes: G01R 33/5601 involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G01R 33/563 of moving material, e.g. flow contrast angiography

Abstract

A method and apparatus is provided for magnetic source magnetic resonance imaging. The method includes collecting energy signals from an object, providing additional information of characteristics of the object, and generating the image of the object from the energy signals and from the additional information such that the image includes a representation of a quantitative estimation of the characteristics, e.g a quantitative estimation of magnetic susceptibility. The additional information may comprise predetermined characteristics of the object, a magnitude image generated from the object, or magnetic signals collected from different relative orientations between the object and the imaging system. The image is generated by an inversion operation based on the collected signals and the additional information. The inversion operation minimizes a cost function obtained by combining the data extracted from the collected signals and the additional information of the object. Additionally, the image is used to detect a number of diagnostic features including microbleeds, contract agents and the like.

Claims

1. A method for generating an image representing a magnetic susceptibility distribution for an object using magnetic resonance imaging (MRI), the method comprising:

determining, by a processing device, phase information corresponding to magnetic signals emitted by the object after the object is excited by a magnetic field;

determining, by the processing device, additional information corresponding to the object independent from the phase information, wherein the additional information is one of;

(a) magnitude information corresponding to the emitted magnetic signals and (b) prior information regarding the object; and

generating, by the processing device, the image representing the magnetic susceptibility distribution for the object based on the determined phase information and the determined additional information, wherein the generating comprises;

performing a direct inversion of a convolution operation using a system matrix, the determined phase information, and the additional information, wherein the additional information is used to improve the system matrix.

2. The method of claim 1, wherein performing the direct inversion comprises applying a regularization approach using an L1 norm to promote image sparsity and solving a minimization problem.

3. The method of claim 1, wherein performing the direct inversion comprises applying a regularization approach using an L2 norm and solving a minimization problem.

4. A non-transitory processor-readable medium having processor-executable instructions stored thereon for generating an image representing a magnetic susceptibility distribution for an object using magnetic resonance imaging (MRI), the processor-executable instructions, when executed by a processor, causing the following steps to be performed:

determining phase information corresponding to magnetic signals emitted by the object after the object is excited by a magnetic field;

determining additional information corresponding to the object independent from the phase information, wherein the additional information is one of;

(a) magnitude information corresponding to the emitted magnetic signals and (b) prior information regarding the object; and

generating the image representing the magnetic susceptibility distribution for the object based on the determined phase information and the determined additional information, wherein the generating comprises;

performing a direct inversion of a convolution operation using a system matrix, the determined phase information, and the additional information, wherein the additional information is used to improve the system matrix.

5. The non-transitory computer-readable medium of claim 4, wherein performing the direct inversion comprises applying a regularization approach using an L1 norm to promote image sparsity and solving a minimization problem.

6. The method of claim 4, wherein performing the direct inversion comprises applying a regularization approach using an L2 norm and solving a minimization problem.

7. A method for generating an image representing a magnetic susceptibility distribution for an object using magnetic resonance imaging (MRI), the method comprising:

determining, by a processing device, phase information corresponding to magnetic signals emitted by the object after the object is excited by a magnetic field;

determining, by the processing device, additional information corresponding to the object independent from the phase information, wherein the additional information is one of;

(a) magnitude information corresponding to the emitted magnetic signals and (b) prior information regarding the object;

generating, by the processing device, the image representing the magnetic susceptibility distribution for the object based on the determined phase information and the determined additional information, wherein the generating comprises;

performing an iterative inversion of a convolution operation using a system matrix, the determined phase information, the additional information, and an initial estimation of the object, including comparison of convolution results with the determined phase information and updating the initial estimation of the object based on the comparison and the additional information.

8. A non-transitory processor-readable medium having processor-executable instructions stored thereon for generating an image representing a magnetic susceptibility distribution for an object using magnetic resonance imaging (MRI), the processor-executable instructions, when executed by a processor, causing the following steps to he performed:

determining phase information corresponding to magnetic signals emitted by the object after the object is excited by a magnetic field;

determining additional information corresponding to the object independent from the phase information, wherein the additional information is one of;

(a) magnitude information corresponding to the emitted magnetic signals and (b) prior information regarding the object;

generating the image representing the magnetic susceptibility distribution for the object based on the determined phase information and the determined additional information, wherein the generating comprises;

performing an iterative inversion of a convolution operation using a system matrix, the determined phase information, the additional information, and an initial estimation of the object, including comparison of convolution results with the determined phase information and updating the initial estimation of the object based on the comparison and the additional information.