804 research outputs found
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy computations of a matched-filtering step due to the
growing size and complexity of the fingerprint dictionaries in multi-parametric
quantitative MRI applications. We address this shortcoming by arranging
dictionary atoms in the form of cover tree structures and adopt the
corresponding fast approximate nearest neighbour searches to accelerate
matched-filtering. For datasets belonging to smooth low-dimensional manifolds
cover trees offer search complexities logarithmic in terms of data population.
With this motivation we propose an iterative reconstruction algorithm, named
CoverBLIP, to address large-size MRF problems where the fingerprint dictionary
i.e. discrete manifold of Bloch responses, encodes several intrinsic NMR
parameters. We study different forms of convergence for this algorithm and we
show that provided with a notion of embedding, the inexact and non-convex
iterations of CoverBLIP linearly convergence toward a near-global solution with
the same order of accuracy as using exact brute-force searches. Our further
examinations on both synthetic and real-world datasets and using different
sampling strategies, indicates between 2 to 3 orders of magnitude reduction in
total search computations. Cover trees are robust against the
curse-of-dimensionality and therefore CoverBLIP provides a notion of
scalability -- a consistent gain in time-accuracy performance-- for searching
high-dimensional atoms which may not be easily preprocessed (i.e. for
dimensionality reduction) due to the increasing degrees of non-linearities
appearing in the emerging multi-parametric MRF dictionaries
CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery
Current proposed solutions for the high dimensionality of the MRF
reconstruction problem rely on a linear compression step to reduce the matching
computations and boost the efficiency of fast but non-scalable searching
schemes such as the KD-trees. However such methodologies often introduce an
unfavourable compromise in the estimation accuracy when applied to nonlinear
data structures such as the manifold of Bloch responses with possible increased
dynamic complexity and growth in data population. To address this shortcoming
we propose an inexact iterative reconstruction method, dubbed as the Cover
BLoch response Iterative Projection (CoverBLIP). Iterative methods improve the
accuracy of their non-iterative counterparts and are additionally robust
against certain accelerated approximate updates, without compromising their
final accuracy. Leveraging on these results, we accelerate matched-filtering
using an ANNS algorithm based on Cover trees with a robustness feature against
the curse of dimensionality.Comment: In Proceedings of Joint Annual Meeting ISMRM-ESMRMB 2018 - Pari
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
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
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