Systems and methods for designing magnetic resonance fingerprinting imaging parameters

Patent metadata

Field	Value
Publication number	US-10241173-B2
Application number	US-201715450127-A
Country	US
Kind code	B2
Filing date	Mar 6, 2017
Priority date	Mar 14, 2016
Publication date	Mar 26, 2019
Grant date	Mar 26, 2019

Abstract

Systems and methods for acquiring magnetic resonance fingerprinting (MRF) imaging data from a subject using a magnetic resonance imaging (MRI) system are provided. The method includes receiving an indication of an MRF imaging process to be performed by the MRI system and receiving a desired design objective for the MRF imaging process and a configuration metric associated with the MRF imaging process. The method further includes using the configuration metric to bound a variance of tissue parameter estimates associated with the MRF imaging process and determine imaging parameters that achieve the desired design objective. The method also includes performing the MRF imaging process using the determined imaging parameters to acquire MRF data using the MRI system.

First claim

The invention claimed is:

1. A method for acquiring magnetic resonance fingerprinting (MRF) imaging data from a subject using a magnetic resonance imaging (MRI) system, the steps of the method comprising:
receiving an indication of an MRF imaging process to be performed by the MRI system;
receiving a desired design objective for the MRF imaging process and a configuration metric associated with the MRF imaging process;
using the configuration metric to bound a variance of tissue parameter estimates associated with the MRF imaging process, determining imaging parameters that achieve the desired design objective; and
performing the MRF imaging process using the determined imaging parameters to acquire MRF data using the MRI system.

2. The method of claim 1 wherein the configuration metric includes a theoretical lower bound.

3. The method of claim 2 wherein the theoretical lower bound is a Cramer-Rao bound (CRB).

4. The method of claim 1 wherein the MRI system is used to acquire an MRI signal from a given area of tissue of the subject to determine an MRF tissue parameter that is a function of the constituent MR properties of the given area of tissue.

5. The method of claim 1 wherein the desired design object includes signal-to-noise ratio (SNR) efficiency of the MRF imaging process.

6. The method of claim 1 wherein determining imaging parameters includes evaluating:

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where α n min and α n max respectively denote upper and lower limits of an nth flip angle in the MRF imaging process, TR n min and TR n max respectively denote upper and lower limits on an nth repetition time (TR) of the MRF imaging process, T denotes a total acquisition time of the MRF imaging process, and ω i balances a relative importance of variance of tissue parameter estimates.

7. The method of claim 1 wherein determining imaging parameters that achieve the desired design objective includes evaluating the configuration metric over a set of representative tissue parameters.

8. The method of claim 1 further comprising applying a stochastic optimization to obtain a desirable local minimum indicating imaging parameters that achieve the desired design objective.

9. The method of claim 1 wherein the imaging parameters that achieve the desired design objective include radio frequency (RF) pulse phase, flip angles, or repetition time (TR), and k-space trajectories.

10. A magnetic resonance imaging (MRI) system comprising:
a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject arranged in the MRI system;
a plurality of gradient coils configured to apply

Assignees

• Massachusetts Gen Hospital
• Univ Southern California

Inventors

• Zhao Bo
• Haldar Justin P
• Wald Lawrence

Classifications

• G01R33/50 Primary

  based on the determination of relaxation times {, e.g. T1 measurement by IR sequences; T2 measurement by multiple-echo sequences} · CPC title

• G01R33/5608

  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 title