K-T sparse: high frame-rate dynamic magnetic resonance imaging exploiting spatio-temporal sparsity
View Patent ↗A method of dynamic resonance imaging is provided. A magnetic resonance imaging excitation is applied. Data in 2 or 3 spatial frequency dimensions, and time is acquired, where an acquisition order in at least one spatial frequency dimension and the time dimension are in a pseudo-random order. The pseudo-random order and enforced sparsity constraints are used to reconstruct a time series of dynamic magnetic resonance images.
1. A method of dynamic resonance imaging, comprising:
a) applying a magnetic resonance imaging excitation;
b) acquiring data in 2 or 3 spatial frequency dimensions, and time, where an acquisition order in at least one spatial frequency dimension and the time dimension are in a pseudo-random order, and
c) using the pseudo-random order and enforced sparsity constraints to reconstruct a time series of dynamic magnetic resonance images.
2. The method, as recited in claim 1 , wherein the using the pseudo-random order and enforced sparsity constraints to construct dynamic magnetic resonance images, comprises performing an optimization that enforces sparcity of the time-series images in a transform domain, which uses a sparcifying transform.
3. The method, as recited in claim 2 , wherein the number of pseudo-random acquisitions is less than half of the total number of acquisitions for a full data set that would completely define an image.
4. The method, as recited in claim 3 , wherein a defined number n of k space values would completely define an image, wherein no more than ⅓ n k-space acquisitions are collected.
5. The method, as recited in claim 4 , wherein the using the pseudo-random order and enforced sparsity constraints to reconstruct dynamic magnetic resonance images uses a solution for the following non-linear convex optimization:
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6. The method of claim 5 , wherein the sparcifying transform is an image compression transform.
7. The method, as recited in claim 6 , wherein the dynamic magnetic resonance images are created in real time.
8. The method, as recited in claim 6 , further comprising displaying the dynamic magnetic resonance images, wherein the dynamic magnetic resonance images are displayed in real time.
9. The method, as recited in claim 6 , wherein the sparcifying transform is a wavelet transform in the spatial dimension and a Fourier transform in a temporal direction.
10. The method of claim 2 , wherein the sparcifying transform from claim 2 is an image compression transform.
11. The method, as recited in claim 1 , wherein the using the pseudo-random order to reconstruct dynamic magnetic resonance images uses a solution for the following non-linear convex optimization:
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12. The method, as recited in claim 1 , wherein the number of pseudo-random acquisitions is less than half of the total number of acquisitions for a full data set that would completely define an image.
13. The method, as recited in claim 1 , wherein a defined number n of k space values would completely define an image, wherein no more than ⅓ n k-space acquisitions are collected.
14. The method, as recited in claim 1 , further comprising displaying the series of dynamic magnetic resonance images.
15. A magnetic resonance imaging apparatus, comprising:
a magnetic resonance imaging excitation and detection system; and
a controller electrically connected to the magnetic resonance imaging excitation and detection system, comprising:
a display;
at least one processor; and
computer readable media, comprising:
computer readable code for applying a magnetic resonance imaging excitation;
computer readable code for acquiring data in 2 or 3 spatial frequency dimensions, and time, where an acquisition order in at least one spatial frequency dimension and the time dimension are in a pseudo-random order;
computer readable code for using the pseudo-random order and enforced sparsity constraints to reconstruct a time series of dynamic magnetic resonance images; and
computer readable code for displaying the time series of dynamic magnetic resonance images on the display.
16. The magnetic resonance imaging apparatus, as recited in claim 15 , wherein the computer readable code for using the pseudo-random order and enforced sparsity constraints to construct dynamic magnetic resonance images, comprises computer readable code for performing an optimization that enforces sparcity of the time-series images in a transform domain, which uses a sparcifying transform.
17. The magnetic resonance imaging apparatus, as recited in claim 16 , wherein the number of pseudo-random acquisitions is less than half of the total number of acquisitions for a full data set that would completely define an image and wherein a defined number n of k space values would completely define an image, wherein no more than ⅓ n k-space acquisitions are collected.
18. The magnetic resonance imaging apparatus, as recited in claim 17 , wherein the computer readable code for using the pseudo-random order and enforced sparsity constraints to reconstruct dynamic magnetic resonance images uses the solution for the following non-linear convex optimization:
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19. The magnetic resonance imaging apparatus of claim 18 , wherein the sparcifying transform is an image compression transform.
20. The magnetic resonance imaging apparatus, as recited in claim 16 , wherein the sparcifying transform is a wavelet transform in the spatial dimension and a Fourier transform in a temporal direction.