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US 11,694,373 B2Granted
Methods for scan-specific k-space interpolation reconstruction in magnetic resonance imaging using machine learning
AssigneeRegents of the University of Minnesota
InventorsMehmet Akcakaya, Steen Moeller
Life of this patent
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Abstract
Methods for reconstructing images from undersampled k-space data using a machine learning approach to learn non-linear mapping functions from acquired k-space lines to generate unacquired target points across multiple coils are described.
Claims25
A method for reconstructing an image from undersampled k-space data acquired with a magnetic resonance imaging (MRI) system, the steps of the method comprising:
- (a) providing to a computer system, undersampled k-space data and calibration data acquired with an MRI system;
- (b) processing the calibration data with a computer system to learn parameters for a machine learning algorithm implemented with a hardware processor and memory of the computer system;
- (c) estimating missing k-space data by inputting the undersampled k-space data to the machine learning algorithm;
- and
- (d) reconstructing an image from the undersampled k-space data and the estimated missing k-space data.
2 more elements
A method for reconstructing an image from undersampled k-space data acquired with a magnetic resonance imaging (MRI) system, the steps of the method comprising:
- (a) providing to a computer system, undersampled k-space data and calibration data acquired with an MRI system;
- (b) processing the calibration data in a k-space domain to learn a non-linear mapping from the calibration data;
- (c) estimating missing k-space data by directly applying the non-linear mapping to the undersampled k-space data;
- and
- (d) reconstructing an image from the undersampled k-space data and the estimated missing k-space data.
2 more elements
Dependencies are inferred from the claim text.