Distortion-free diffusion and quantitative magnetic resonance imaging with blip up-down acquisition of spin- and gradient-echoes
Magnetic resonance imaging (“MRI”) using a spin- and gradient-echo (“SAGE”) pulse sequence with blip up-down acquisition (“BUDA”) encoding enables distortion-free, high-resolution diffusion-weighted imaging and/or quantitative parameter mapping. Phase-encoding polarities are alternated across shots during a multi-shot acquisition. In each shot, multi-contrast data are acquired at echo times associated with a gradient echo, a mixed gradient-and-spin echo, and a spin echo. High in-plane resolution and distortion-free quantitative parameter maps can be generated, such as T2 maps. T2* maps, paramagnetic susceptibility maps, and diamagnetic susceptibility maps. Diffusion-weighted data can be acquired using diffusion encoding gradients and BUDA encoding, where multi-contrast data are acquired in the b=0 acquisition. Diffusion parameter maps can be generated from the b=0 and diffusion-weighted data.
1 . A method for magnetic resonance imaging, the method comprising:
(a) acquiring, with a magnetic resonance imaging (MRI) system, multi-contrast data using a multi-shot echo planar imaging (msEPI) pulse sequence comprising multiple shots, wherein a phase-encoding polarity is changed across the multiple shots of the msEPI pulse sequence and wherein in each shot data are acquired at a first echo time associated with a gradient echo, a second echo time associated with a mixed gradient-and-spin echo, and a third echo time associated with a spin echo;
(b) computing field inhomogeneity data from the multi-contrast data; and
(c) reconstructing an image from the multi-contrast data using the field (c) inhomogeneity data in order to reduce geometric distortions in the reconstructed image;
wherein the phase-encoding polarity is alternated with each subsequent shot in the msEPI pulse sequence.
2 . The method of claim 1 , wherein computing the field inhomogeneity data comprises:
reconstructing intermediate blip-up images from the multi-contrast data acquired in shots using a positive polarity phase-encoding;
reconstructing intermediate blip-down images from the multi-contrast data acquired in shots using a negative polarity phase-encoding; and
estimating the field inhomogeneity data from the intermediate blip-up images and the intermediate blip-down images.
3 . The method of claim 2 , wherein the field inhomogeneity data comprise a field map.
4 . The method of claim 1 , wherein the image is reconstructed using a reconstruction framework that implements a Hankel structured low-rank regularization.
5 . The method of claim 4 , wherein the reconstruction framework incorporates the field inhomogeneity data to reduce geometric distortions while reconstructing the image.
6 . The method of claim 1 , wherein at least one of the first echo time, the second echo time, and the third echo time are adjusted in subsequent repetitions of the pulse sequence in order to provide different contrast weightings in the multi-contrast data.
7 . The method of claim 1 , wherein reconstructing the image from the multi-contrast data comprises reconstructing a plurality of images having different contrast weightings.
8 . The method of claim 7 , further comprising generating a quantitative parameter map from the plurality of images.
9 . The method of claim 8 , wherein the quantitative parameter map comprises at least one of a T2 map, a T2′ map, a T2* map, a paramagnetic susceptibility map, or a diamagnetic susceptibility map.
10 . The method of claim 1 , further comprising acquiring diffusion-weighted data using a second msEPI pulse sequence comprising multiple shots, wherein the phase-encoding polarity is changed across the multiple shots of the second msEPI pulse sequence and wherein in each shot data are acquired following application of diffusion encoding gradients.
11 . The method of claim 10 , wherein the multi-contrast data are acquired as b=0 acquisition data, and wherein reconstructing the image from the multi-contrast data comprises reconstructing a plurality of images having different contrast weightings.
12 . The method of claim 10 , further comprising reconstructing diffusion-weighted images from the diffusion-weighted data.
13 . The method of claim 12 , further comprising generating a diffusion parameter map using the image reconstructed from the multi-contrast data and the diffusion-weighted images reconstructed from the diffusion-weighted data, wherein the image reconstructed from the multi-contrast data comprises a b=0 image.
14 . The method of claim 1 , further comprising denoising the image reconstructed from the multi-contrast data.
15 . The method of claim 14 , wherein the image is denoised by inputting the image to a neural network, generating output as a denoised image.
16 . The method of claim 15 , wherein the neural network comprises an MR-Self2Self (MR-S2S) model architecture.
17 . The method of claim 16 , wherein the MR-S2S neural network is trained using self-supervised learning.
18 . A method for magnetic resonance imaging, the method comprising:
(a) acquiring, with a magnetic resonance imaging (MRI) system, multi-contrast data using a multi-shot echo planar imaging (msEPI) pulse sequence comprising multiple shots, wherein a phase-encoding polarity is changed across the multiple shots of the msEPI pulse sequence and wherein in each shot data are acquired at a first echo time associated with a gradient echo, a second echo time associated with a mixed gradient-and-spin echo, and a third echo time associated with a spin echo;
(b) computing field inhomogeneity data from the multi-contrast data; and
(c) reconstructing an image from the multi-contrast data using the field inhomogeneity data in order to reduce geometric distortions in the reconstructed image;
wherein computing the field inhomogeneity data comprises:
reconstructing intermediate blip-up images from the multi-contrast data acquired in shots using a positive polarity phase-encoding;
reconstructing intermediate blip-down images from the multi-contrast data acquired in shots using a negative polarity phase-encoding; and
estimating the field inhomogeneity data from the intermediate blip-up images and the intermediate blip-down images.
19 . A method for magnetic resonance imaging, the method comprising:
(a) acquiring, with a magnetic resonance imaging (MRI) system, multi-contrast data using a multi-shot echo planar imaging (msEPI) pulse sequence comprising multiple shots, wherein a phase-encoding polarity is changed across the multiple shots of the msEPI pulse sequence and wherein in each shot data are acquired at a first echo time associated with a gradient echo, a second echo time associated with a mixed gradient-and-spin echo, and a third echo time associated with a spin echo;
(b) computing field inhomogeneity data from the multi-contrast data;
(c) reconstructing an image from the multi-contrast data using the field inhomogeneity data in order to reduce geometric distortions in the reconstructed image; and
further comprising acquiring diffusion-weighted data using a second msEPI pulse sequence comprising multiple shots, wherein the phase-encoding polarity is changed across the multiple shots of the second msEPI pulse sequence and wherein in each shot data are acquired following application of diffusion encoding gradients.
20 . A method for magnetic resonance imaging, the method comprising:
(a) acquiring, with a magnetic resonance imaging (MRI) system, multi-contrast data using a multi-shot echo planar imaging (msEPI) pulse sequence comprising multiple shots, wherein a phase-encoding polarity is changed across the multiple shots of the msEPI pulse sequence and wherein in each shot data are acquired at a first echo time associated with a gradient echo, a second echo time associated with a mixed gradient-and-spin echo, and a third echo time associated with a spin echo;
(b) computing field inhomogeneity data from the multi-contrast data;
(c) reconstructing an image from the multi-contrast data using the field (c) inhomogeneity data in order to reduce geometric distortions in the reconstructed image and
(d) denoising the image reconstructed from the multi-contrast data.