SU-F-J-164: A Simulation Study On Anovel 4D MRI Reconstruction Method Based On Probability-Driven Sorting

2016 ◽  
Vol 43 (6Part11) ◽  
pp. 3445-3445
Author(s):  
X Liang ◽  
F Yin ◽  
Y Liu ◽  
J Cai
Keyword(s):  
Simulation Study ◽  
Reconstruction Method ◽  
4D Mri ◽  
Mri Reconstruction

scholarly journals SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

2020 ◽  
Vol 14 ◽  
Author(s):  
Zhenmou Yuan ◽  
Mingfeng Jiang ◽  
Yaming Wang ◽  
Bo Wei ◽  
Yongming Li ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Reconstruction Method ◽  
Mri Imaging ◽  
Adversarial Networks ◽  
Reconstruction Methods ◽  
Mri Reconstruction

Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

scholarly journals Motion robust 4D-MRI sorting based on anatomic feature matching: A digital phantom simulation study

2020 ◽  
Vol 1 (1) ◽  
pp. 41-47
Author(s):  
Zi Yang ◽  
Lei Ren ◽  
Fang-Fang Yin ◽  
Xiao Liang ◽  
Jing Cai
Keyword(s):  
Simulation Study ◽  
Feature Matching ◽  
4D Mri ◽  
Digital Phantom ◽  
Anatomic Feature

scholarly journals Improving Non-Cartesian MRI Reconstruction through Discontinuity Subtraction

2006 ◽  
Vol 2006 ◽  
pp. 1-9 ◽  
Author(s):  
Jiayu Song ◽  
Qing Huo Liu
Keyword(s):  
Reconstruction Method ◽  
Reconstruction Methods ◽  
Spread Function ◽  
Data Fidelity ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Space Data ◽  
2D And 3D ◽  
Undersampled Data

Non-Cartesian sampling is widely used for fast magnetic resonance imaging (MRI). Accurate and fast image reconstruction from non-Cartesiank-space data becomes a challenge and gains a lot of attention. Images provided by conventional direct reconstruction methods usually bear ringing, streaking, and other leakage artifacts caused by discontinuous structures. In this paper, we tackle these problems by analyzing the principal point spread function (PSF) of non-Cartesian reconstruction and propose a leakage reduction reconstruction scheme based on discontinuity subtraction. Data fidelity ink-space is enforced during each iteration. Multidimensional nonuniform fast Fourier transform (NUFFT) algorithms are utilized to simulate thek-space samples as well as to reconstruct images. The proposed method is compared to the direct reconstruction method on computer-simulated phantoms and physical scans. Non-Cartesian sampling trajectories including 2D spiral, 2D and 3D radial trajectories are studied. The proposed method is found useful on reducing artifacts due to high image discontinuities. It also improves the quality of images reconstructed from undersampled data.

A reconstruction method of intra-ventricular blood flow using color flow ultrasound: a simulation study

2015 ◽  
Author(s):  
Jaeseong Jang ◽  
Chi Young Ahn ◽  
Kiwan Jeon ◽  
Jung-il Choi ◽  
Changhoon Lee ◽  
...  
Keyword(s):  
Blood Flow ◽  
Simulation Study ◽  
Reconstruction Method ◽  
Color Flow

scholarly journals An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

2021 ◽  
Vol 7 (11) ◽  
pp. 231
Author(s):  
Wanyu Bian ◽  
Yunmei Chen ◽  
Xiaojing Ye ◽  
Qingchao Zhang
Keyword(s):  
Bilevel Optimization ◽  
Variational Model ◽  
Data Sets ◽  
Reconstruction Method ◽  
Learning Framework ◽  
Network Parameters ◽  
Meta Learning ◽  
Invariant Part ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri

This work aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the meta-learning framework. Specifically, we develop a deep reconstruction network induced by a learnable optimization algorithm (LOA) to solve the nonconvex nonsmooth variational model of MRI image reconstruction. In this model, the nonconvex nonsmooth regularization term is parameterized as a structured deep network where the network parameters can be learned from data. We partition these network parameters into two parts: a task-invariant part for the common feature encoder component of the regularization, and a task-specific part to account for the variations in the heterogeneous training and testing data. We train the regularization parameters in a bilevel optimization framework which significantly improves the robustness of the training process and the generalization ability of the network. We conduct a series of numerical experiments using heterogeneous MRI data sets with various undersampling patterns, ratios, and acquisition settings. The experimental results show that our network yields greatly improved reconstruction quality over existing methods and can generalize well to new reconstruction problems whose undersampling patterns/trajectories are not present during training.

scholarly journals Compressed Sensing MRI Reconstruction from Highly Undersampledk-Space Data Using Nonsubsampled Shearlet Transform Sparsity Prior

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Min Yuan ◽  
Bingxin Yang ◽  
Yide Ma ◽  
Jiuwen Zhang ◽  
Runpu Zhang ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Directional Sensitivity ◽  
Reconstruction Method ◽  
Approximation Properties ◽  
Mr Images ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Reconstruction Methods ◽  
Mri Reconstruction ◽  
Space Data

Compressed sensing has shown great potential in speeding up MR imaging by undersamplingk-space data. Generally sparsity is used as a priori knowledge to improve the quality of reconstructed image. Compressed sensing MR image (CS-MRI) reconstruction methods have employed widely used sparsifying transforms such as wavelet or total variation, which are not preeminent in dealing with MR images containing distributed discontinuities and cannot provide a sufficient sparse representation and the decomposition at any direction. In this paper, we propose a novel CS-MRI reconstruction method from highly undersampledk-space data using nonsubsampled shearlet transform (NSST) sparsity prior. In particular, we have implemented a flexible decomposition with an arbitrary even number of directional subbands at each level using NSST for MR images. The highly directional sensitivity of NSST and its optimal approximation properties lead to improvement in CS-MRI reconstruction applications. The experimental results demonstrate that the proposed method results in the high quality reconstruction, which is highly effective at preserving the intrinsic anisotropic features of MRI meanwhile suppressing the artifacts and added noise. The objective evaluation indices outperform all compared CS-MRI methods. In summary, NSST with even number directional decomposition is very competitive in CS-MRI applications as sparsity prior in terms of performance and computational efficiency.

SU-F-303-01: 4D-MRI Reconstruction Using Group-Wise Registration

2015 ◽  
Vol 42 (6Part26) ◽  
pp. 3537-3537
Author(s):  
R Farah ◽  
S Shea ◽  
E Tryggestad ◽  
R Teboh Forbang ◽  
J Wong ◽  
...  
Keyword(s):  
4D Mri ◽  
Mri Reconstruction

scholarly journals Compressed Sensing MRI Reconstruction Algorithm Based on Contourlet Transform and Alternating Direction Method

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Zhenyu Hu ◽  
Qiuye Wang ◽  
Congcong Ming ◽  
Lai Wang ◽  
Yuanqing Hu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Contourlet Transform ◽  
Superior Performance ◽  
Reconstruction Method ◽  
Mr Images ◽  
Alternating Direction ◽  
Mri Reconstruction

Compressed sensing (CS) based methods have recently been used to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. In traditional CS-MRI, wavelet transform can hardly capture the information of image curves and edges. In this paper, we present a new CS-MRI reconstruction algorithm based on contourlet transform and alternating direction method (ADM). The MR images are firstly represented by contourlet transform, which can describe the images’ curves and edges fully and accurately. Then the MR images are reconstructed by ADM, which is an effective CS reconstruction method. Numerical results validate the superior performance of the proposed algorithm in terms of reconstruction accuracy and computation time.

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