System and method for accelerated, time-resolved imaging

1. 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 magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field;

a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array;

a computer system programmed to:

control the RF system and the magnetic gradient system to acquire medical image data from the subject in parallel using the coil array;

reconstruct a series of images of the subject from the acquired medical image data set by:

enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model; and

preserving information in the series of images that deviate from the non-patient-specific model by controlling a requirement of consistency with the non-patient-specific model.

2. The system of claim 1 wherein the computer system is further programmed to control the requirement of consistency with the non-patient-specific model by relaxing the requirement of consistency with the non-patient-specific model if a fit with the non-patient-specific model is undesirable for a given pixel in an image in the series of images.

3. A method for reconstructing a series of images of a subject, the steps of the method comprising:

a) acquiring medical image data from the subject with a medical imaging system;

b) reconstructing a series of images of the subject from the acquired medical image data set by:

i) enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model; and

ii) preserving information in the series of images that deviate from the non-patient-specific model by controlling a requirement of consistency with the non-patient-specific model.

4. The method of claim 3 wherein the non-patient-specific signal model includes a model for contrast bolus propagation dynamics.

5. The method of claim 3 wherein reconstructing the series of images includes performing an iterative reconstruction process.

6. The method of claim 5 wherein controlling the requirement of consistency with the non-patient-specific model is performed by relaxing the requirement of consistency with the non-patient-specific model if a fit with the non-patient-specific model is undesirable for a given pixel in an image in the series of images.

7. The method of claim 5 wherein reconstructing the series of images of the subject includes iteratively minimizing an optimization problem having a data-fidelity term and a non-patient-specific, model-based term.

8. The method of claim 7 wherein controlling the requirement of consistency with the non-patient-specific model includes precluding simultaneous minimization of the data fidelity term with relaxation of the non-patient-specific, model-based term.

9. The method of claim 7 wherein a balance is reached between the data-fidelity term and the non-patient-specific, model-based term using l p norms, wherein the l p norm for the data-fidelity term is p=1 and the l p norm for the data non-patient-specific, model-based term is p<=1.

10. The method of claim 3 wherein the non-patient-specific model relates to time resolved contrast-enhanced magnetic resonance angiography (CE MRA), dynamic susceptibility contrast perfusion-weighted imaging (DSC PWI), or dynamic contrast enhanced perfusion-weighted imaging (DCE-PWI.

11. The method of claim 3 wherein the medical imaging system includes a magnetic resonance imaging system or a computed tomography system.

12. The method of claim 3 wherein the non-patient-specific signal model describes signal evolution in a serial dimension of the series of images.

13. The method of claim 3 further comprising performing a motion correction process by incorporating a motion correction operator into reconstructing the series of images of the subject, that aligns individual images in the series of images before invoking the non-patient-specific, signal model.

14. The method of claim 13 further comprising estimating the motion correction operator by:

acquiring motion tracking data and estimating the motion correction operator from the motion tracking data;

estimating the motion correction operator by co-registering each image in the series of images another image in the series of images; or

adaptively estimating the motion correction operator during the reconstruction.

15. The method of claim 14 wherein the motion tracking data is acquired from one of separately-acquired navigator sampling, navigator data derived from the medical image data, or an external tracking device.

16. The method of claim 3 wherein step b) includes using at least one of a compressed sensing-based reconstruction, a model consistency condition (MOCCO) constrained image reconstruction, or a highly constrained projection reconstruction (HYPR) processes.

17. The method of claim 3 wherein step b) includes generating parameter map of the subject.