Estimating diffusion metrics from diffusion-weighted magnetic resonance images using optimized k-q space sampling and deep learning
AssigneeThe General Hospital Corporation
InventorsQiuyun Fan, Susie Yi Huang, Qiyuan Tian, Chanon Ngamsombat
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Abstract
Diffusion metric maps are generated from a limited input of magnetic resonance data to a suitably trained machine learning algorithm, such as a suitably trained neural network. In general, a downsampling strategy is implemented in the joint k-q space to enable the simultaneous estimation of multiple different diffusion metrics from a more limited set of input diffusion-weighted images.
Claims19
A method for generating diffusion metric maps from magnetic resonance data, the method comprising:
- (a) accessing with a computer system, magnetic resonance data acquired from a subject, wherein the magnetic resonance data comprise:
- first data that sample k-space with a first k-space sampling and sample q-space with a first q-space sampling;
- second data that sample k-space with a second k-space sampling and sample q-space with a second q-space sampling;
- wherein the first k-space sampling is higher than the second k-space sampling and the first q-space sampling is lower than the second q-space sampling;
- (b) accessing a trained neural network with the computer system, wherein the trained neural network has been trained on training data in order to learn a mapping from magnetic resonance data to diffusion metrics corresponding to at least two different diffusion models;
- and
- (c) inputting the magnetic resonance data to the trained neural network using the computer system, generating output as a plurality of diffusion metric maps, wherein the plurality of diffusion metric maps depict a spatial distribution in the subject of the diffusion metrics corresponding to the at least two different diffusion models.
A method for generating diffusion metric maps from magnetic resonance data acquired with a magnetic resonance imaging (MRI) system, the method comprising:
- (a) acquiring diffusion-weighted magnetic resonance data from a subject with the MRI system, wherein the diffusion-weighted magnetic resonance data jointly sample k-q space such that k-space sampling is increased at lower diffusion weighting b-values and q-space sampling is increased at lower spatial resolutions;
- (b) reconstructing diffusion-weighted images from the diffusion-weighted magnetic resonance data using a computer system;
- (c) accessing a trained neural network with the computer system, wherein the trained neural network has been trained on training data in order to learn a mapping from diffusion-weighted images to diffusion metrics corresponding to at least two different diffusion models;
- and
- (d) inputting the diffusion-weighted images to the trained neural network using the computer system, generating output as a plurality of diffusion metric maps, wherein the plurality of diffusion metric maps depict a spatial distribution in the subject of the diffusion metrics corresponding to the at least two different diffusion models.
Dependencies are inferred from the claim text.