Processing and displaying dynamic contrast-enhanced magnetic resonance imaging information

• US 20080125643A1
• Filed: 11/24/2006
• Published: 05/29/2008
• Est. Priority Date: 11/24/2006
• Status: Active Grant

• Current Assignee: Stichting Katholieke Universiteit
• Original Assignee: Qview, Inc.
• Inventors: Huisman, Henkjan J., Karssemeijer, Nico
• Granted Patent: US 8,280,488 B2
• Time in Patent Office: Days
• US Class Current: 600/420
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/708 Breast positioning means
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01R 33/281 Means for the use of in vitro contrast agents G01R33/282 takes precedence; involving use of a contrast agent in MR imaging G01R33/5601; in vivo contrast agents A61K49/0002
• 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 33/56308 Characterization of motion or flow; Dynamic imaging
• CPC Class Codes: G01R 33/56366 Perfusion imaging

Abstract

A method, system, and related computer program products for processing and displaying dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) information are described. A plurality of instances of an MRI volume of the body part acquired at a respective plurality of sample times subsequent to an administration of a tracer is processed to determine a plurality of pharmacokinetic (PK) parameters characterizing a mathematical model-based relationship between a plasma tracer concentration and a total tracer concentration within the body part. For one preferred embodiment, computation of the PK parameters is performed according to a generalized signal model such that computation can be carried out in real time during an interactive viewer session, with required reference regions being selectable and optionally re-selectable by the viewer without requiring extensive waiting times for PK parameter computation. Associated user interfaces and computer-aided detection (CAD) algorithms are also provided.

Claims

1. A method for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of a body part of a patient to determine a plurality of pharmacokinetic (PK) parameters characterizing a mathematical model-based relationship between a plasma tracer concentration and a total tracer concentration within the body part, comprising:

receiving a plurality of instances of an MRI volume of the body part acquired at a respective plurality of sample times subsequent to an administration of a tracer, said instances comprising voxels of MRI readings timewise variable according to the total tracer concentration but scalewise uncalibrated therewith;

for each of said voxels, processing said scalewise uncalibrated MRI readings to compute a first parameter set characteristic of said timewise variations thereof;

receiving an identification of a calibration region within said MRI volume;

for each of said voxels, processing said scalewise uncalibrated MRI readings against information derived from said identified calibration region to compute a second parameter set characteristic of a scale of said total tracer concentration; and

computing said plurality of PK parameters for each of said voxels using said first and second parameter sets.

2. The method of claim 1, said first and second parameter sets forming a total concentration parameter set, the method further comprising;

receiving an identification of at least one plasma reference region having known PK parameters;

modifying said total concentration parameter set for voxels within said at least one plasma reference region using substantially arithmetic computations to generate a plasma concentration parameter set; and

computing said PK parameters for each of said voxels using substantially arithmetic operations on said total concentration parameter set and said plasma concentration parameter set.

3. The method of claim 2, wherein said at least one plasma reference region comprises one of pectoral muscle tissue and left ventricular volume.

4. The method of claim 3, wherein said at least one plasma reference region comprises at least three separate plasma reference regions.

5. The method of claim 3, wherein said body part comprises of one of a breast and a prostate.

6. The method of claim 1, wherein said calibration region and said reference plasma region are automatically segmented by computer processing of the MRI volume.

7. The method of claim 1, wherein said calibration region and said reference plasma region are manually identified by a viewer.

8. The method of claim 1, wherein said plurality of pharmacokinetic parameters comprises an extra-cellular tissue volume ratio (v_e), a bolus arrival time (t₀), and a tracer transfer constant (K^trans).

9. The method of claim 1, further comprising displaying a map of at least one of said plurality of pharmacokinetic parameters as an overlay to a viewable MRI volume corresponding to said MRI volume.

10. A method for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of a body part of a patient to determine a plurality of pharmacokinetic (PK) parameters characterizing a mathematical model-based relationship between a plasma tracer concentration and a total tracer concentration within the body part, comprising:

receiving a plurality of instances of an MRI volume of the body part acquired at a respective plurality of sample times subsequent to an administration of a tracer, said instances comprising voxels of MRI readings variable according to the total tracer concentration but scalewise uncalibrated therewith;

prior to an interactive display session, processing said scalewise uncalibrated MRI readings to compute a first parameter set characteristic of said timewise variations thereof;

during an interactive display session, receiving a user identification of a calibration region; and

computing said PK parameters for all voxels in said MRI volume using said first parameter set, said uncalibrated MRI readings, and information derived from said identified calibration region within a real time interval after receiving said user identification.

11. The method of claim 10, further comprising;

computing a total concentration parameter set using said first parameter set, said uncalibrated MRI readings, and information derived from said identified calibration region;

receiving an identification of at least one plasma reference region having known PK parameters;

modifying said total concentration parameter set for voxels within said at least one plasma reference region using substantially arithmetic computations to generate a plasma concentration parameter set; and

computing said PK parameters for each of said voxels using substantially arithmetic operations on said total concentration parameter set and said plasma concentration parameter set.

12. The method of claim 11, wherein said at least one plasma reference region comprises one of pectoral muscle tissue and left ventricular volume.

13. The method of claim 11, wherein said at least one plasma reference region comprises at least three separate plasma reference regions.

14. The method of claim 10, wherein said body part comprises of one of a breast and a prostate.

15. The method of claim 10, wherein said plurality of pharmacokinetic parameters comprises an extra-cellular tissue volume ratio (v_e), a bolus arrival time (t₀), and a tracer transfer constant (K^trans).

16. The method of claim 10, further comprising displaying a map of at least one of said plurality of pharmacokinetic parameters as an overlay to a viewable MRI volume corresponding to said MRI volume.

18. The method of claim 10, wherein said body part comprises of one of a breast and a prostate.

19. The method of claim 10, wherein said plurality of pharmacokinetic parameters comprises an extra-cellular tissue volume ratio (v_e), a bolus arrival time (t₀), and a tracer transfer constant (K_trans).

20. The method of claim 10, further comprising displaying a map of at least one of said plurality of pharmacokinetic parameters as an overlay to a viewable MRI volume corresponding to said MRI volume.

17. A method for interactive processing and display of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) information, comprising:

(i) prior to an interactive user session;

receiving a plurality of instances of an MRI volume of a body part acquired at a respective plurality of sample times subsequent to an administration of a tracer, said instances comprising voxels of MRI readings that are timewise variable according to the total tracer concentration but scalewise uncalibrated therewith; and

for each of said voxels, processing said scalewise uncalibrated MRI readings to compute a first parameter set;

(ii) receiving an identification of the voxels of the MRI volume corresponding to at least one reference region;

(iii) during an interactive user session;

receiving a first user input determinative of information from which scalewise calibration of the MRI readings with the total tracer concentration can be performed for each voxel;

for each of said voxels, performing said scalewise calibration for at least two of said MRI readings using said first user input;

for each of said voxels, computing a second parameter set using said at least two scalewise calibrated MRI readings; and

for each of said voxels lying outside the at least one reference region, computing each of said plurality of PK parameters using (a) said first and second parameter sets for that voxel, and (b) representative first and second parameter sets for said identified voxels in said at least one reference region;

wherein said performing said scalewise calibration, said computing the second parameter set, and said computing each of said plurality of PK parameters are of minor computational intensity compared to said computing the first parameter set and are performed in a real-time interval across all of said voxels during said interactive user session.