Methods, apparatuses, systems, and computer-readable mediums for biomarker quantification using free-breathing stack-of-radial imaging
A method of generating at least one image using a magnetic resonance imaging (MRI) system includes generating, for each contrast of a plurality of contrasts, an image by forming a multi-dimensional data matrix by dividing MRI imaging data for a contrast into a plurality of bins and generating the image based on at least the MRI imaging data and the multi-dimensional data matrix. Each bin of the plurality of bins corresponds to one of a plurality of respiratory motion states.
1 . A method of generating at least one image using a magnetic resonance imaging (MRI) system, the method comprising:
generating, for each contrast of a plurality of contrasts, an image by
forming a first multi-dimensional data matrix by dividing MRI imaging data for a contrast into a plurality of bins, each bin of the plurality of bins corresponding to one of a plurality of respiratory motion states and the first multi-dimensional data matrix including acquired k-space data assigned to k-space and motion state dimensions, and
generating the image by evaluating an optimization function based on the MRI imaging data, the first multi-dimensional data matrix, a transformation matrix, and an image regularization.
2 . The method of claim 1 , further comprising:
generating the transformation matrix for each contrast of the plurality of contrasts.
3 . The method of claim 2 , wherein the transformation matrix includes at least one of an undersampling pattern, a coil sensitivity map, or a non-uniform Fourier transform.
4 . The method of claim 2 , further comprising:
determining the image regularization involving a linear transformation for each contrast of the plurality of contrasts.
5 . The method of claim 4 , wherein the image regularization includes at least one regularization factor.
6 . The method of claim 5 , wherein the at least one regularization factor is at least one of a regularization factor for spatial regularization or motion-state regularization.
7 . The method of claim 2 , further comprising, for each contrast of the plurality of contrasts:
applying the transformation matrix to the first multi-dimensional data matrix for the contrast to obtain a transformed multi-dimensional data matrix;
wherein the generating generates the image based on the MRI imaging data and the transformed multi-dimensional data matrix.
8 . The method of claim 1 , wherein the optimization function is based on an optimization equation given by
1
2
Ax
-
y
2
2
+
W
(
α
,
β
)
x
1
,
wherein
A is the transformation matrix,
x is a second multi-dimensional data matrix of the MRI imaging data to be reconstructed, the second multi-dimensional data matrix including at least a spatial dimension and a motion-state dimension,
y is the first multi-dimensional data matrix of the acquired k-space data assigned to k-space and motion-state dimensions, and
W is the image regularization.
9 . The method of claim 1 , further comprising:
acquiring the MRI imaging data from an MRI system, wherein the MRI imaging data is acquired for each contrast of the plurality of contrasts resulting from application of a pulse of the MRI system.
10 . The method of claim 9 , further comprising:
extracting a self-gating signal from the MRI imaging data; and
determining, based on the self-gating signal, the plurality of respiratory motion states associated with acquisition of the MRI imaging data,
wherein a separate self-gating signal is extracted for each contrast included in the plurality of contrasts.
11 . The method of claim 9 , wherein the MRI imaging data is acquired using a stack-of-star trajectory and a self-gating signal is extracted from the MRI imaging data by sampling a plurality of radial views included in the MRI imaging data.
12 . The method of claim 9 , further comprising:
determining the plurality of respiratory motion states associated with acquisition of the MRI imaging data by measuring one or more physiological signals with the MRI system.
13 . The method of claim 1 , further comprising:
calculating one or more biomarker parameter maps based on the images generated for the plurality of contrasts.
14 . The method of claim 13 , wherein the one or more biomarker parameter maps comprise at least one of a proton density fat fraction (PDFF) parameter map or a transverse relaxation rate (R 2 *) parameter map.
15 . A system for generating at least one image, the system comprising:
at least one processor; and
at least one memory including instructions that, when executed by the at least one processor, cause the system to
generate, for each contrast of a plurality of contrasts, an image by
forming a first multi-dimensional data matrix by dividing MRI imaging data for a contrast into a plurality of bins, each bin of the plurality of bins corresponding to one of a plurality of respiratory motion states and the first multi-dimensional data matrix including acquired k-space data assigned to k-space and motion state dimensions, and
generating the image by evaluating an optimization function based on the MRI imaging data, the first multi-dimensional data matrix, a transformation matrix, and an image regularization.
16 . The system of claim 15 , wherein the system is further caused to:
calculate one or more biomarker parameter maps based on the images generated for the plurality of contrasts.
17 . A non-transitory computer readable medium system storing computer readable instruction that, when executed by one or more processors of a system, cause the system to perform a method of generating at least one image, the method comprising:
generating, for each contrast of a plurality of contrasts, an image by
forming a first multi-dimensional data matrix by dividing MRI imaging data for a contrast into a plurality of bins, each bin of the plurality of bins corresponding to one of a plurality of respiratory motion states and the first multi-dimensional data matrix including acquired k-space data assigned to k-space and motion state dimensions, and
generating the image by evaluating an optimization function based on the MRI imaging data, the first multi-dimensional data matrix, a transformation matrix, and an image regularization.
18 . The non-transitory computer readable medium of claim 17 , wherein the method further comprises:
calculating one or more biomarker parameter maps based on the images generated for the plurality of contrasts.