Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist–Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

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DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

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Magnetic Resonance Imaging Physical Principles And Sequence Design

Compressive sensing (CS) is an effective technique for reconstructing image from a small amount of sampled data. It has been widely applied in medical imaging, remote sensing, image compression, etc. In this paper, we propose two versions of a novel deep learning architecture, dubbed as ADMM-CSNet, by combining the traditional model-based CS method and data-driven deep learning method for image reconstruction from sparsely sampled measurements. We first consider a generalized CS model for image reconstruction with undetermined regularizations in undetermined transform domains, and then two efficient solvers using Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the model are proposed. We further unroll and generalize the ADMM algorithm to be two deep architectures, in which all parameters of the CS model and the ADMM algorithm are discriminatively learned by end-to-end training. For both applications of fast CS complex-valued MR imaging and CS imaging of real-valued natural images, the proposed ADMM-CSNet achieved favorable reconstruction accuracy in fast computational speed compared with the traditional and the other deep learning methods.

ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing

Objective: Improve the reconstructed image with fast and multiclass dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to provide adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multiclass dictionaries is proposed and solved using a fast alternating direction method of multipliers. Results: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. Conclusion: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction. Significance: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality.

Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction

Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.

Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B) converges faster than previously proposed algorithms accelerated proximal algorithm (APG) and alternating directional method of multipliers for balanced model (ADMM-B).

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Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

An index signal de-noising method relates to a signal de-noising method, and is easy to operate and exhibits excellent effects. The method includes the step of modeling index signals: filling the index signals in a Hankel matrix according to a set sequence, establishing a Hankel matrix low-rank reconstruction model, and solving the model to de-noise the signals. The method is fast in speed and high in precision, and parameters can be set on the basis of measured noise variances. During practical applications, the optimized model can be adopted to de-noise signals such as time-domain signals of nuclear magnetic resonance wave spectrums and signals that accord with the index characteristics, so the sampling time is reduced and the spectra resolution is increased. The index signal de-noising method, provided by the invention, exhibits excellent effects and is easy to operate.

Index signal de-noising method

Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI.

The invention provides an exponential signal denoising method achieved by means of prior information and relates to a denoising method for exponential signals.The prior exponential signals form a Hankel matrix according to a set sequence, the Hankel matrix is subjected to singular value decomposition, and a prior signal space and a prior singular value are obtained; then a Hankel matrix with the same size as the former Hankel matrix is established for the target exponential signals, and the matrix of the target signals is decomposed through the prior information space; after a weighted threshold is obtained from the prior singular value, a Hankel matrix of the denoised target signals is obtained from the singular value of the target exponential signals according to the weighted threshold; then the Hankel matrix of the target signals is solved, and finally the denoised signals are obtained.Through the prior information of the reference signals, the speed is high, the effect is good, and operation is easy.Denoising of a magnetic resonance spectrum can be achieved for signals with exponential features such as time domain signals of the magnetic resonance spectrum through the method, and the purposes of shortening the sampling time and increasing the signal to noise ratio of the spectrum are achieved.

Jing Ye

Papers

Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.

Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

Index signal de-noising method

Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI.

Exponential signal denoising method achieved by means of prior information