System and method for quantifying perfusion using a dictionary matching

Patent metadata

Field	Value
Publication number	US-11747421-B2
Application number	US-201916416707-A
Country	US
Kind code	B2
Filing date	May 20, 2019
Priority date	May 18, 2018
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

The present application provides a system and method for quantifying perfusion using a dictionary matching approach. In some aspects, the method comprises performing a predetermined pulse sequence using an MRI system to acquire MRI data from the subject after having delivered a dose of a contrast agent to the subject. The method also includes comparing the MRI data to a dictionary to determine perfusion information, and generating, using the perfusion information, a report indicative of perfusion within the subject.

First claim

The invention claimed is:

1. A method for generating images of a subject using a magnetic resonance imaging (MRI) system, the method including steps comprising:
performing a predetermined pulse sequence using an MRI system to acquire dynamic contrast-enhanced (DCE) MRI data from a liver of the subject after having delivered a dose of a contrast agent to the subject;
comparing the acquired DCE MRI data to a dictionary to determine perfusion information including at least one perfusion parameter including a distribution volume (DV), wherein the dictionary includes a plurality of entries and each entry includes one or more associated perfusion parameters and comparing the acquired DCE MRI data to the dictionary comprises identifying an entry in the dictionary based on the comparison and assigning the one or more associated perfusion parameters of the identified dictionary entry to the acquired DCE MRI data; and
generating, using the perfusion information including at least one perfusion parameter, a report indicative of perfusion within the liver of the subject;
wherein the dictionary is generated using a dual-input single compartment (DISC) pharmacokinetic model.

2. The method of claim 1 , wherein the at least one perfusion parameter further comprises at least one of an arterial fraction (AF), and a mean transit time (MTT).

3. The method of claim 1 , wherein the method further comprises comparing the DCE MRI data by executing a pattern matching algorithm that correlates patterns of signal evolutions associated with the DCE MRI data and corresponding entries in the dictionary.

4. The method of claim 3 , wherein the pattern matching algorithm includes at least one of an inner product, a template match, a matrix inversion, a correlation, a Bayesian estimation, basis pursuit, or simulated annealing.

5. The method of claim 3 , wherein the pattern matching is performed using at least one of a neural network or deep learning network.

6. The method of claim 3 , wherein the dictionary matching is performed on a voxel-by-voxel basis to determine perfusion parameters.

7. The method of claim 3 , wherein the method further comprises generating the dictionary by combining physical and hemodynamic properties with the predetermined pulse sequence and the pharmacokinetic model.

8. The method of claim 1 , wherein the method further comprises reconstructing at least one image depicting perfusion in the liver of the subject.

9. The method of claim 1 , wherein the dictionary is a compressed dictionary.

10. A magnetic resonance imaging (MRI) system comprising:
a magnet system configured to generate a polarizing magnetic field about a portion of the subject positioned in the MRI system;
a plurality of gradient coils configured to apply a gradient field to the polarizing magnetic field;
a radio frequency (RF) system configured to apply a RF excitation field to the subject, and acquire therefrom a set of magnetic resonance image (MRI) data;
at least one processor configured to:
control the plurality of gradient coils and RF system to perform a predetermined pulse sequence to acquire dynamic contrast-enhanced (DCE) MRI data from a liver of the subject after having delivered a dose of a contrast agent to the subject;
compare the acquired DCE MRI data to a dictionary to determine perfusion information including at least one perfusion parameter including a distribution volume (DV), wherein the dictionary includes a plurality of entries and each entry includes one or more associated perfusion parameters and comparing the acquired DCE Mill data to the dictionary comprises identifying an entry in the dictionary based on the comparison and assigning the one or more associated perfusion parameters of the identified dictionary entry to the acquired DCE MM data; and
generate, using the perfusion information including at least one perfusion parameter, a report indicative of perfusion within the liver of the subject;
wherein the dictionary is generated using a dual-input single compartment (DISC) pharmacokinetic model.

11. The system of claim 10 , wherein the at least one perfusion parameter further comprises at least one of an arterial fraction (AF), and a mean transit time (MTT).

12. The system of claim 10 , wherein the at least one processor is further configured to compare the DCE MRI data by executing a pattern matching algorithm that correlates patterns of signal evolutions associated with the DCE MRI data and corresponding entries in the dictionary.

13. The system of claim 10 , wherein the at least one processor is further configured to generate the dictionary by combining physical and hemodynamic properties with the predetermined pulse sequence and the pharmacokinetic model.

14. The system of claim 10 , wherein the at least one processor is further configured to reconstruct at least one an image depicting perfusion in the liver of the subject.

Assignees

• Univ Case Western Reserve

Inventors

• Gulani Vikas
• Ghodasara Satyam
• Wright Katherine
• Seiberlich Nicole
• Griswold Mark A

Classifications

• G01R33/5608 Primary

  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

• G01R33/5601

  involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent · CPC title

• G01R33/56366 Primary

  Perfusion imaging · CPC title

• G01R33/5635

  Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA] · CPC title