15 research outputs found
A Plug-and-Play Approach To Multiparametric Quantitative MRI:Image Reconstruction Using Pre-Trained Deep Denoisers
Current spatiotemporal deep learning approaches to Magnetic Resonance
Fingerprinting (MRF) build artefact-removal models customised to a particular
k-space subsampling pattern which is used for fast (compressed) acquisition.
This may not be useful when the acquisition process is unknown during training
of the deep learning model and/or changes during testing time. This paper
proposes an iterative deep learning plug-and-play reconstruction approach to
MRF which is adaptive to the forward acquisition process. Spatiotemporal image
priors are learned by an image denoiser i.e. a Convolutional Neural Network
(CNN), trained to remove generic white gaussian noise (not a particular
subsampling artefact) from data. This CNN denoiser is then used as a
data-driven shrinkage operator within the iterative reconstruction algorithm.
This algorithm with the same denoiser model is then tested on two simulated
acquisition processes with distinct subsampling patterns. The results show
consistent de-aliasing performance against both acquisition schemes and
accurate mapping of tissues' quantitative bio-properties. Software available:
https://github.com/ketanfatania/QMRI-PnP-Recon-PO
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
SRflow: Deep learning based super-resolution of 4D-flow MRI data
Exploiting 4D-flow magnetic resonance imaging (MRI) data to quantify hemodynamics requires an adequate spatio-temporal vector field resolution at a low noise level. To address this challenge, we provide a learned solution to super-resolve in vivo 4D-flow MRI data at a post-processing level. We propose a deep convolutional neural network (CNN) that learns the inter-scale relationship of the velocity vector map and leverages an efficient residual learning scheme to make it computationally feasible. A novel, direction-sensitive, and robust loss function is crucial to learning vector-field data. We present a detailed comparative study between the proposed super-resolution and the conventional cubic B-spline based vector-field super-resolution. Our method improves the peak-velocity to noise ratio of the flow field by 10 and 30% for in vivo cardiovascular and cerebrovascular data, respectively, for 4 × super-resolution over the state-of-the-art cubic B-spline. Significantly, our method offers 10x faster inference over the cubic B-spline. The proposed approach for super-resolution of 4D-flow data would potentially improve the subsequent calculation of hemodynamic quantities
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
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
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network
In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task network to produce correlated outputs as a framework to enable localisation of region of interest (RoI) via classification, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task. We demonstrate how the multi-task network with RoI focused loss offers an advantage over other configurations of the network to achieve higher accuracy of performance. This is relevant to sCT where failure to accurately estimate high Hounsfield Unit values of bone could lead to impaired accuracy in clinical applications. We compare the dose calculation maps from the proposed sCT and the real CT in a radiation therapy treatment planning setup
In vivo myelin water quantification using diffusion–relaxation correlation MRI: A comparison of 1D and 2D methods
Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion