37 research outputs found
Uncertainty quantification for sparse Fourier recovery
One of the most prominent methods for uncertainty quantification in
high-dimen-sional statistics is the desparsified LASSO that relies on
unconstrained -minimization. The majority of initial works focused on
real (sub-)Gaussian designs. However, in many applications, such as magnetic
resonance imaging (MRI), the measurement process possesses a certain structure
due to the nature of the problem. The measurement operator in MRI can be
described by a subsampled Fourier matrix. The purpose of this work is to extend
the uncertainty quantification process using the desparsified LASSO to design
matrices originating from a bounded orthonormal system, which naturally
generalizes the subsampled Fourier case and also allows for the treatment of
the case where the sparsity basis is not the standard basis. In particular we
construct honest confidence intervals for every pixel of an MR image that is
sparse in the standard basis provided the number of measurements satisfies or that is sparse with respect to
the Haar Wavelet basis provided a slightly larger number of measurements
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
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy storage and computation requirements of a
dictionary-matching (DM) step due to the growing size and complexity of the
fingerprint dictionaries in multi-parametric quantitative MRI applications. In
this paper we study a deep learning approach to address these shortcomings.
Coupled with a dimensionality reduction first layer, the proposed MRF-Net is
able to reconstruct quantitative maps by saving more than 60 times in memory
and computations required for a DM baseline. Fine-grid manifold enumeration
i.e. the MRF dictionary is only used for training the network and not during
image reconstruction. We show that the MRF-Net provides a piece-wise affine
approximation to the Bloch response manifold projection and that rather than
memorizing the dictionary, the network efficiently clusters this manifold and
learns a set of hierarchical matched-filters for affine regression of the NMR
characteristics in each segment
Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting.
PURPOSE: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. METHODS: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on two different phantoms and three healthy volunteer brains in vivo. RESULTS: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (<4% deviation for T1 and <6% deviation for T2). The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual qualities of fine brain structures in the multi-parametric maps generated by multi-shot EPI-MRF and Spiral-MRF implementations were comparable. CONCLUSION: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral-MRF. This multi-shot approach achieved considerable k-space subsampling and comparatively short TRs in a similar manner to spirals and therefore provides an alternative for performing MRF using an accelerated Cartesian readout; thereby increasing the potential usability of MRF.The research leading to these results has received funding from the European Commission H2020 Framework Programme (H2020- MSCAITN- 2014), number 642685 MacSeNet, the Engineering and Physical Sciences Research Council (EPSRC) platform Compressed Quantitative MRI grant, number EP/M019802/1 and the Scottish Research Partnership in Engineering (SRPe) award, number SRPe PECRE1718/ 17