Magnetic resonance spatial risk map for tissue outcome prediction
Diffusion weighted images and perfusion weighted images are acquired with an MRI system and used to produce low b, DWI, ADC, CBV, CBF, and MTT images of brain tissues following an ischemic event. These MRI physiological measurements are input along with a spatial location measurement to a generalized linear model that predicts the outcome of tissues surrounding a lesion.
1. A method for predicting tissue fate, the steps comprising:
a) imaging tissues with a magnetic resonance imaging (MRI) system;
b) acquiring data from the tissues with the system that contains information indicative of a physiological parameter related to tissue health;
c) reconstructing an image of the tissues form the acquired data;
d) calculating from the acquired data a physiological parameter at tissue locations in the reconstructed image;
e) selecting a core region in the reconstructed image;
f) calculating a location parameter related to the distance of each of said tissue locations from the core region, the location parameter being equal to the distance of the tissue location form the core region; and
g) predicting the fate of tissues at each of said tissue locations using a predetermined model that employs as inputs the physiological parameter and the location parameter.
2. The method as recited in claim 1 in which the predetermined model is a generalized linear model of the form:
P
=
1
1
+
ⅇ
-
α
+
β
x
+
γ
r
in which P is the predicted outcome, x is the physiological parameter, r is the location parameter, β is a coefficient that weights the physiological parameter, γ is a coefficient that weights the location parameter, and α is a bias term.
3. The method as recited in claim 1 in which the tissues are located in a mammalian brain.
4. The method as recited in claim 1 in which step a) includes acquiring a diffusion weighted image.
5. The method as recited in claim 4 in which a plurality of diffusion weighted images are acquired with motion encoding gradients oriented in respective different directions.
6. The method as recited in claim 5 in which the physiological parameter calculated in step d) includes apparent diffusion coefficient (ADC) calculated from the plurality of diffusion weighted images.
7. The method as recited in claim 1 in which step b) includes acquiring a set of perfusion weighted image frames.
8. The method as recited in claim 7 in which step d) includes calculating a plurality of physiological parameters from said set of perfusion weighted image frames, and in which the predetermined model accepts said plurality of physiological parameters as inputs to the prediction.
9. The method as recited in claim 8 in which step b) includes acquiring a diffusion weighted image, and in which step d) includes calculating a diffusion coefficient at each tissue location from the acquired diffusion weighted image.
10. The method as recited in claim 1 in which step e) is performed by manually selecting the core region in the image reconstructed in step c).
11. The method as recited in claim 1 in which step e) is performed by selecting tissue locations based on the physiological parameter values calculated in step d).
12. The method as recited in claim 1 wherein step g) further includes employing an additional location parameter indicative of the region of the subject being imaged in which the tissues lie when predicting the fate of tissues using the predetermined mode.