391 research outputs found
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous
quantification of multiple properties of biological tissues. It relies on a
pseudo-random acquisition and the matching of acquired signal evolutions to a
precomputed dictionary. However, the dictionary is not scalable to
higher-parametric spaces, limiting MRF to the simultaneous mapping of only a
small number of parameters (proton density, T1 and T2 in general). Inspired by
diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF
sequence with embedded diffusion-encoding gradients along all three axes to
efficiently encode orientational diffusion and T1 and T2 relaxation. We take
advantage of a convolutional neural network (CNN) to reconstruct multiple
quantitative maps from this single, highly undersampled acquisition. We bypass
expensive dictionary matching by learning the implicit physical relationships
between the spatiotemporal MRF data and the T1, T2 and diffusion tensor
parameters. The predicted parameter maps and the derived scalar diffusion
metrics agree well with state-of-the-art reference protocols. Orientational
diffusion information is captured as seen from the estimated primary diffusion
directions. In addition to this, the joint acquisition and reconstruction
framework proves capable of preserving tissue abnormalities in multiple
sclerosis lesions
Magnetic resonance fingerprinting review part 2: Technique and directions
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154317/1/jmri26877.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154317/2/jmri26877_am.pd
Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging
Novel methods for quantitative, transient-state multiparametric imaging are
increasingly being demonstrated for assessment of disease and treatment
efficacy. Here, we build on these by assessing the most common Non-Cartesian
readout trajectories (2D/3D radials and spirals), demonstrating efficient
anti-aliasing with a k-space view-sharing technique, and proposing novel
methods for parameter inference with neural networks that incorporate the
estimation of proton density. Our results show good agreement with gold
standard and phantom references for all readout trajectories at 1.5T and 3T.
Parameters inferred with the neural network were within 6.58% difference from
the parameters inferred with a high-resolution dictionary. Concordance
correlation coefficients were above 0.92 and the normalized root mean squared
error ranged between 4.2% - 12.7% with respect to gold-standard phantom
references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric
isotropic resolution in under five minutes with reconstruction and inference
times < 7 minutes. Our 3D quantitative transient-state imaging approach could
enable high-resolution multiparametric tissue quantification within clinically
acceptable acquisition and reconstruction times.Comment: 43 pages, 12 Figures, 5 Table
Deep Learning for the Acceleration of Magnetic Resonance Fingerprinting
Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of the human body. Although MRF has demonstrated improved scan efficiency compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this work is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with less sampling data. Most existing approaches use the MRF signal evolution at each individual pixel to estimate tissue properties without considering the spatial association among neighboring pixels. In this report, I propose a spatially-constrained quantification method that uses signals at multiple neighboring pixels to better estimate tissue properties at the central pixel. Specifically, I have designed a unique two-step deep learning model to estimate the tissue property (T1 or T2) maps from the observed MRF signals, which is comprised of 1) a feature extraction module to reduce the dimension of signals by extracting a low-dimensional feature vector from the high-dimensional signal evolution and 2) a spatially-constrained quantification module to exploit the spatial information from the extracted feature maps to generate the final tissue property map. A corresponding two-step training strategy has been developed for network training. The proposed method was tested on highly undersampled MRF data acquired from human brains. The experimental results demonstrated that the proposed method can achieve accurate quantification for T1 and T2 relaxation times using only 1/4 time points of the original sequence (i.e., four times of acceleration for MRF acquisition). Furthermore, a rapid 2D MRF technique with a sub-millimeter in-plane resolution was developed using deep-learning-based quantification approach for brain T1 and T2 quantification. Specifically, the 2D acquisition was performed using a FISP sequence and a spiral trajectory with 0.8 mm in-plane resolution. A novel network architecture, i.e., residual channel attention U-Net, was proposed to improve high resolution details in the estimated tissue maps. Quantitative brain imaging was performed with five adults and two pediatric subjects and the performance of the proposed approach was compared to several existing methods in the literature. In vivo measurements with both adult and pediatric subjects show that high quality T1 and T2 mapping with 0.8 mm in-plane resolution was achieved in 7.5 sec per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared to the standard U-Net, high resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. The experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside the reduced data acquisition time, five-fold acceleration in tissue property mapping was also achieved with the proposed method.Master of Scienc
Deep Unrolling for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising
quantitative MR imaging approach. Deep learning methods have been proposed for
MRF and demonstrated improved performance over classical compressed sensing
algorithms. However many of these end-to-end models are physics-free, while
consistency of the predictions with respect to the physical forward model is
crucial for reliably solving inverse problems. To address this, recently [1]
proposed a proximal gradient descent framework that directly incorporates the
forward acquisition and Bloch dynamic models within an unrolled learning
mechanism. However, [1] only evaluated the unrolled model on synthetic data
using Cartesian sampling trajectories. In this paper, as a complementary to
[1], we investigate other choices of encoders to build the proximal neural
network, and evaluate the deep unrolling algorithm on real accelerated MRF
scans with non-Cartesian k-space sampling trajectories.Comment: Tech report. arXiv admin note: substantial text overlap with
arXiv:2006.1527
Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact encoder-decoder network with residual blocks in order to embed Bloch manifold projections through multi-scale piecewise affine approximations, and to replace the non-scalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols
Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks
Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR
imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF
based on dictionary matching is slow and lacks scalability. To overcome these
limitations, neural network (NN) approaches estimating MR parameters from
fingerprints have been proposed recently. Here, we revisit NN-based MRF
reconstruction to jointly learn the forward process from MR parameters to
fingerprints and the backward process from fingerprints to MR parameters by
leveraging invertible neural networks (INNs). As a proof-of-concept, we perform
various experiments showing the benefit of learning the forward process, i.e.,
the Bloch simulations, for improved MR parameter estimation. The benefit
especially accentuates when MR parameter estimation is difficult due to MR
physical restrictions. Therefore, INNs might be a feasible alternative to the
current solely backward-based NNs for MRF reconstruction.Comment: Accepted at MICCAI MLMIR 202
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