11,409 research outputs found
Efficient MR Image Reconstruction for Compressed MR Imaging
Abstract. In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original prob-lem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Fi-nally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the recon-struction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for com-pressed MR image reconstruction.
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction
Recent works have demonstrated that deep learning (DL) based compressed
sensing (CS) implementation can accelerate Magnetic Resonance (MR) Imaging by
reconstructing MR images from sub-sampled k-space data. However, network
architectures adopted in previous methods are all designed by handcraft. Neural
Architecture Search (NAS) algorithms can automatically build neural network
architectures which have outperformed human designed ones in several vision
tasks. Inspired by this, here we proposed a novel and efficient network for the
MR image reconstruction problem via NAS instead of manual attempts.
Particularly, a specific cell structure, which was integrated into the
model-driven MR reconstruction pipeline, was automatically searched from a
flexible pre-defined operation search space in a differentiable manner.
Experimental results show that our searched network can produce better
reconstruction results compared to previous state-of-the-art methods in terms
of PSNR and SSIM with 4-6 times fewer computation resources. Extensive
experiments were conducted to analyze how hyper-parameters affect
reconstruction performance and the searched structures. The generalizability of
the searched architecture was also evaluated on different organ MR datasets.
Our proposed method can reach a better trade-off between computation cost and
reconstruction performance for MR reconstruction problem with good
generalizability and offer insights to design neural networks for other medical
image applications. The evaluation code will be available at
https://github.com/yjump/NAS-for-CSMRI.Comment: To be appear in Computerized Medical Imaging and Graphic
Dictionaries for fast and informative dynamic MRI acquisition
Magnetic resonance (MR) imaging is an invaluable tool for medical research and diagnosis but suffers from inefficiencies. The speed of its acquisition mechanism, based on sequentially probing the interactions between nuclear atom spins and a changing magnetic field, is limited by atomic properties and scanner physics. Modern sampling techniques termed compressed sensing have nevertheless demonstrated how near perfect reconstructions are possible from undersampled, accelerated acquisitions, showing promise for more efficient MR acquisition paradigms. At the same time, information extraction from MR images through image analysis implies a considerable dimensionality reduction, in which an image is processed for the extraction of a few clinically useful parameters. This signals an inefficient handling of information in the separated treatment of acquisition and analysis that could be tackled by joining these two essential stages of the imaging pipeline.
In this thesis, we explore the use of adaptive sparse modelling for novel acquisition strategies of cardiac cine MR data. Conventional compressed sensing MR acquisition relies on fixed basis transforms for sparse modelling, which are only able to guarantee suboptimal sparse modelling. We introduce spatio-temporal dictionaries that are able to optimally adapt sparse modelling by absorbing salient features of cardiac cine data, and demonstrate how they can outperform sampling methods based on fixed basis transforms. Additionally, we extend the framework introduced to handle parallel data acquisition. Given the flexibility of the formulation, we show how it can be combined with a labelling model that provides a segmentation of the image as a by-product of the reconstruction, hence performing joint reconstruction and analysis.Open Acces
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
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