IP Library Granted Patent US 11,412,948
Granted Patent B2
US 11,412,948 · App. 15/595,156 · Granted Aug 16, 2022

Method for improved dynamic contrast enhanced imaging using tracer-kinetic models as constraints

Inventors: Krishna Shrinivas Nayak (Long Beach, CA); Yi Guo (Los Angeles, CA); Robert Marc Lebel (Calgary AB, CA); Yinghua Zhu (San Jose, CA); Sajan Goud Lingala (Los Angeles, CA)
Assignee: University of Southern California
A61B5/055A61B5/0042A61B5/7257G01R33/5601G01R33/56366G01R33/5611
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,412,948
App. No.
15/595,156
Granted
Aug 16, 2022
Kind
B2
Abstract

Tracer kinetic models are utilized as temporal constraints for highly under-sampled reconstruction of DCE-MRI data. The method is flexible in handling any TK model, does not rely on tuning of regularization parameters, and in comparison to existing compressed sensing approaches, provides robust mapping of TK parameters at high under-sampling rates. In summary, the method greatly improves the robustness and ease-of-use while providing better quality of TK parameter maps than existing methods. In another embodiment, TK parameter maps are directly reconstructed from highly under-sampled DCE-MRI data. This method provides more accurate TK parameter values and higher under-sampling rates. It does not require tuning parameters and there are not additional intermediate steps. The proposed method greatly improves the robustness and ease-of-use while providing better quality of TK parameter maps than conventional indirect methods.

Claims (22)

1. A method for improving dynamic contrast enhanced imaging, the method comprising:

a) administering a magnetic resonance contrast agent to a subject;

b) collecting magnetic resonance imaging data from the subject, the magnetic resonance imaging data including under-sampled (k,t)-space data from a plurality of receiver coils;

c) selecting a tracer kinetic model to be applied to the magnetic resonance imaging data, the tracer kinetic model being defined by a plurality of tracer kinetic parameters;

d) applying the tracer kinetic model to estimate tracer kinetic parameter maps;

e) generating a library of simulated concentration time profiles for the magnetic resonance contrast agent by applying the tracer kinetic model;

f) creating a compact dictionary of temporal basis functions from the library of simulated concentration time profiles; and

g) determining estimated concentration time profiles for each spatial position from the under-sampled (k-t)-space data with an optimal projection onto the compact dictionary of temporal basis functions.

2. The method of claim 1 wherein step d) starts with an initial guess of the tracer kinetic parameter maps.

3. The method of claim 1 wherein the compact dictionary of temporal basis functions is formed by compressing the library of simulated concentration time profiles, using a dictionary learning algorithm.

4. The method of claim 3 wherein the dictionary learning algorithm is k-singular value decomposition (k-SVD).

5. The method of claim 1 wherein dictionary of temporal basis functions is a full library of simulated concentration time profiles.

6. The method of claim 1 wherein the tracer kinetic model is a Patlak model having kinetic parameters:

K trans which is a transfer constant from blood plasma into extracellular extravascular space (EES); and

V p which is a fractional plasma volume.

7. The method of claim 6 wherein the tracer kinetic model is an extended Tofts-Kety model further having kinetic parameter K ep which is a transfer constant from EES back to the blood plasma.

8. The method of claim 1 wherein the estimated concentration time profiles are modeled as a “k-sparse” linear combination of temporal basis functions in the compact dictionary of temporal basis functions such that a concentration time profile at each spatial position can be approximated using a linear combination of k temporal basis functions where k is a positive integer.

9. The method of claim 1 wherein the estimated concentration time profiles are estimated from fully sampled or under-sampled DCE-MRI measurements using minimization of an objective function that balances model-fitting and data consistency.

10. The method of claim 1 further comprising estimating kinetic parameters by fitting the estimated concentration time profiles to a Tofts model or an extended Tofts-Kety model having kinetic parameters:

K trans which is a transfer constant from blood plasma into extracellular extravascular space (EES); and

V p which is a fractional plasma volume.

11. The method of claim 10 wherein the tracer kinetic model is the extended Tofts-Kety model further having kinetic parameter K ep which is a transfer constant from EES back to the blood plasma.

Assignments (2)
CONFIRMATORY LICENSE Recorded Jul 31, 2024
From: UNIVERSITY OF SOUTHERN CALIFORNIA
To: NATIONAL INSTITUTES OF HEALTH
Reel/Frame 068218/0880 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 27, 2017
From: NAYAK, KRISHNA SHRINIVAS; GUO, YI; LEBEL, ROBERT MARC; ZHU, YINGHUA; LINGALA, SAJAN GOUD
To: UNIVERSITY OF SOUTHERN CALIFORNIA
Reel/Frame 043118/0606 →
Continuity (2)
Provisional Application 62336033 · May 13, 2016
Related Publication 20170325709A1 · Nov 16, 2017
Cited By (1)
US 12,201,413