An Efficient Light-Weight Network for Fast Reconstruction on MR Images

Background: In recent years, deep learning (DL) algorithms have emerged in endlessly and achieved impressive performance, which makes it possible to accelerate magnetic resonance (MR) image reconstruction with DL instead of compressed sensing (CS) methods. However, a DL-based MR image reconstruction method has always suffered from its heavy learning parameters and poor generalization ability so far. Therefore, an efficient light-weight network is still in desperate need of fast MR image reconstruction. Methods: We propose an efficient and light-weight MR reconstruction network (named RecNet) that uses a Convolutional Neural Network (CNN) to fast reconstruct high-quality MR images. Specifically, the network is composed of cascade modules, and each cascade module is further divided into feature extraction blocks and a data consistency layer. The feature extraction block can not only effectively extract the features of MR images, but also do not introduce too many parameters for the whole network. To stabilize the training procedure, the correction information of image frequency is adopted in the data consistency (DC) layer. Results: We have evaluated RecNet on a public dataset and the results show that the image quality reconstructed by RecNet is the best on the peak a signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation standards. In addition, the pre-trained RecNet can also reconstruct high-quality MR images on an unseen dataset. Conclusion: The results demonstrate that the RecNet has superior reconstruction ability in various metrics than comparative methods. The RecNet can quickly generate high-quality MR images in fewer parameters. Furthermore, the RecNet has an excellent generalization ability on pathological images and different sampling rates data.

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Improved least squares MR image reconstruction using estimates of k-Space data consistency

Magnetic Resonance in Medicine ◽

10.1002/mrm.23144 ◽

2011 ◽

Vol 67 (6) ◽

pp. 1600-1608 ◽

Cited By ~ 34

Author(s):

Kevin M. Johnson ◽

Walter F. Block ◽

Scott. B. Reeder ◽

Alexey Samsonov

Keyword(s):

Image Reconstruction ◽

Least Squares ◽

Data Consistency ◽

Mr Image ◽

Space Data ◽

Mr Image Reconstruction

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Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/8865582 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Di Zhao ◽

Yanhu Huang ◽

Feng Zhao ◽

Binyi Qin ◽

Jincun Zheng

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Structural Information ◽

Reference Image ◽

Training Procedure ◽

Mr Image ◽

Image Prior ◽

Clinical Scenarios ◽

Deep Image ◽

Mr Image Reconstruction

Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled k -space measurements.

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Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training

Sensors ◽

10.3390/s20010308 ◽

2020 ◽

Vol 20 (1) ◽

pp. 308 ◽

Cited By ~ 2

Author(s):

Di Zhao ◽

Feng Zhao ◽

Yongjin Gan

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Compressed Sensing ◽

Training Dataset ◽

Reconstruction Method ◽

Training Procedure ◽

Deep Convolutional Neural Networks ◽

Mr Image ◽

Space Data ◽

Mr Image Reconstruction

Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based datasets for network training procedures is always a challenge in clinical applications. In this paper, we propose a novel deep learning based compressed sensing MR image reconstruction method that does not require any pre-training procedure or training dataset, thereby largely reducing clinician dependence on patient-based datasets. The proposed method is based on the Deep Image Prior (DIP) framework and uses a high-resolution reference MR image as the input of the convolutional neural network in order to induce the structural prior in the learning procedure. This reference-driven strategy improves the efficiency and effect of network learning. We then add the k-space data correction step to enforce the consistency of the k-space data with the measurements, which further improve the image reconstruction accuracy. Experiments on in vivo MR datasets showed that the proposed method can achieve more accurate reconstruction results from undersampled k-space data.

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Median Filter Based Compressed Sensing Model with Application to MR Image Reconstruction

Mathematical Problems in Engineering ◽

10.1155/2018/8316194 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Yunyun Yang ◽

Xuxu Qin ◽

Boying Wu

Keyword(s):

Magnetic Resonance ◽

Image Reconstruction ◽

Compressed Sensing ◽

Total Variation ◽

Median Filter ◽

Mr Images ◽

Mr Image ◽

Reconstruction Process ◽

Sensing Model ◽

Mr Image Reconstruction

Magnetic resonance imaging (MRI) has become a helpful technique and developed rapidly in clinical medicine and diagnosis. Magnetic resonance (MR) images can display more clearly soft tissue structures and are important for doctors to diagnose diseases. However, the long acquisition and transformation time of MR images may limit their application in clinical diagnosis. Compressed sensing methods have been widely used in faithfully reconstructing MR images and greatly shorten the scanning and transforming time. In this paper we present a compressed sensing model based on median filter for MR image reconstruction. By combining a total variation term, a median filter term, and a data fitting term together, we first propose a minimization problem for image reconstruction. The median filter term makes our method eliminate additional noise from the reconstruction process and obtain much clearer reconstruction results. One key point of the proposed method lies in the fact that both the total variation term and the median filter term are presented in the L1 norm formulation. We then apply the split Bregman technique for fast minimization and give an efficient algorithm. Finally, we apply our method to numbers of MR images and compare it with a related method. Reconstruction results and comparisons demonstrate the accuracy and efficiency of the proposed model.

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