US 11,982,726 B2Granted

Method for joint arterial input function and tracer kinetic parameter estimation in accelerated DCE-MRI using a model consistency constraint

AssigneeUniversity of Southern California

InventorsKrishna S. Nayak, Yannick Bliesener, Yi Guo, Yinghua Zhu, Sajan Goud Lingala, Robert Marc Lebel

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Abstract

Tracer kinetic models are utilized as temporal constraints for highly under-sampled reconstruction of DCE-MRI data. In one embodiment, a method for improving dynamic contrast enhanced imaging. The method includes steps of administering a magnetic resonance contrast agent to a subject and then collecting magnetic resonance contrast agent from the subject. A tracer kinetic model (i.e. eTofts or Patlak) is selected to be applied to the magnetic resonance imaging data. The tracer kinetic model is applied to the magnetic resonance imaging data. Tracer kinetic maps and dynamic images are simultaneously reconstructed and a consistency constraint is applied. The proposed method allows for easy use of different tracer kinetic models in the formulation and estimation of patient-specific arterial input functions jointly with tracer kinetic maps.

Claims34

Dependent claims

A method for improving dynamic contrast enhanced imaging for joint arterial input function and tracer kinetic parameter estimation, the method comprising:

a) administering a contrast agent to a subject;
b) collecting imaging data from the subject for a tissue or organ;
c) selecting a tracer kinetic model to be applied to the imaging data, the tracer kinetic model being defined by a plurality of tracer kinetic parameters;
d) reconstructing dynamic images from the imaging data as reconstructed dynamic images, wherein a consistency constraint is applied to the reconstructed dynamic images;
the consistency constraint including a weighted sum of a data consistency component and a model consistency component;
e) applying the tracer kinetic model to the reconstructed dynamic images to estimate tracer kinetic parameter maps as estimated tracer kinetic parameter maps wherein a model consistency restraint is applied;
and
f) alternately performing steps d) and e) until converging on a set of dynamic images and tracer kinetic parameter maps, wherein the consistency constraint is estimated by a least square optimization formulated as:
(
C
,
θ
)
=
arg
⁢
⁢
min
C
,
θ
⁢

UFE
⁡
(
ψ
⁢
⁢
C
+
S
0
)
-
y

2
2
+
β
⁢

P
⁡
(
θ
)
-
C

2
2
Ψ is a signal operator which converts concentration-time-curves (per voxel) C of the contrast agent to an image intensity time series;
U is an under-sampling mask;
F is a Fourier transform;
E is a sensitivity encoding matrix providing spatial relative sensitivities of pickup coils;
S0 is image intensity for each voxel prior to contrast agent arrival;
y is under-sampled magnetic resonance imaging data:
C is a measured concentration-time curves of the contrast agent for each voxel in a Field Of View;
P(θ) is a predicted concentration distribution of the contrast agent from a selected tracer kinetic model;
β is a penalty or weight factor for the model consistency component;
and
θ are tracer kinetic parameters.

61 more elements

A system for improving dynamic contrast enhanced imaging for joint arterial input function and tracer kinetic parameter estimation, the system comprising:

a magnetic resonance imaging system that generates magnetic resonance imaging data from a subject for a tissue or organ;
and
a programmable computer configured to execute steps of:
a) collecting the magnetic resonance imaging data from the subject;
b) reconstructing dynamic images from the magnetic resonance imaging data as reconstructed dynamic images, wherein a consistency constraint is applied to the reconstructed dynamic images;
the consistency constraint including a weighted sum of a data consistency component and a model consistency component;
c) applying a tracer kinetic model to the reconstructed dynamic images to estimate tracer kinetic parameter maps as estimated tracer kinetic parameter maps, wherein a model consistency constraint is applied;
and
d) alternately performing steps b) and c) until converging on a set of dynamic images and tracer kinetic parameter maps, wherein the consistency constraint is estimated by a least square optimization formulated as:
(
C
,
θ
)
=
arg
⁢
⁢
min
C
,
θ
⁢

UFE
⁡
(
ψ
⁢
⁢
C
+
S
0
)
-
y

2
2
+
β
⁢

P
⁡
(
θ
)
-
C

2
2
Ψ is a signal operator which converts concentration-time-curves (per voxel) C of a contrast agent to an image intensity time series;
U is an under-sampling mask;
F is a Fourier transform;
E is a sensitivity encoding matrix providing spatial relative sensitivities of pickup coils;
S0 is image intensity for each voxel prior to contrast agent arrival;
y is under-sampled magnetic resonance imaging data:
C is a measured concentration-time curves of the contrast agent for each voxel in a Field Of View;
P(θ) is a predicted concentration distribution of the contrast agent from a selected tracer kinetic model;
β is a penalty or weight factor for the model consistency component;
and
θ are tracer kinetic parameters.

62 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/384,795Filed 15 Apr 2019 · us provisional application
Priority: 13 Apr 2018
Published: US 2019/0317171 A117 Oct 2019
Granted: 14 May 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification14 codes

G01R 33/56308
Of moving material, e.g. flow contrast angiography: characterization of motion or flow; Dynamic imaging
G01R 33/56366
Of moving material, e.g. flow contrast angiography: perfusion imaging
A61B 5/055
Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves:…
G01R 33/5601
Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution: involving…