scholarly journals Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zangen Zhu ◽  
Khan Wahid ◽  
Paul Babyn ◽  
Ran Yang
Keyword(s):  
Wavelet Transform ◽  
Compressed Sensing ◽  
Structural Similarity ◽  
Sampling Theorem ◽  
Acquisition Time ◽  
Discrete Wavelet ◽  
Signal Acquisition ◽  
Mathematical Framework ◽  
Reconstruction Method ◽  
Space Data

Undersamplingk-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.

scholarly journals Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Shanshan Chen ◽  
Bensheng Qiu ◽  
Feng Zhao ◽  
Chao Li ◽  
Hongwei Du
Keyword(s):  
Wavelet Transform ◽  
Compressed Sensing ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Signal To Noise ◽  
Reconstruction Methods ◽  
Noise Ratio

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.

scholarly journals Post mortem validation of DSI compressed sensing with optical imaging

2021 ◽  
Author(s):  
Robert Jones ◽  
Chiara Maffei ◽  
Jean Augustinack ◽  
Bruce Fischl ◽  
Hui Wang ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Ex Vivo ◽  
Ground Truth ◽  
Acceleration Factor ◽  
Training Data ◽  
Acquisition Time ◽  
Human Brain Tissue ◽  
Diffusion Spectrum Imaging ◽  
Reconstruction Methods ◽  
Space Data

AbstractCompressed sensing (CS) has been used to enhance the feasibility of diffusion spectrum imaging (DSI) by reducing the required acquisition time. CS applied to DSI (CS-DSI) attempts to reconstruct diffusion probability density functions (PDFs) from significantly undersampled q-space data. Dictionary-based CS-DSI using L2-regularized algorithms is an intriguing approach that has demonstrated high fidelity reconstructions, fast computation times and inter-subject generalizability when tested on in vivo data. CS-DSI reconstruction fidelity is typically evaluated using the fully sampled data as ground truth. However, it is difficult to gauge how great an error with respect to the fully sampled PDF we can tolerate, without knowing whether that error also translates to substantial loss of accuracy with respect to the true fiber orientations. Here, we obtain direct measurements of axonal orientations in ex vivo human brain tissue at microscopic resolution with polarization-sensitive optical coherence tomography (PSOCT). We employ dictionary-based CS reconstruction methods to DSI data from the same samples, acquired at high max b-value (40000 s/mm2) and with high spatial resolution. We compare the diffusion orientation estimates from both CS and fully sampled DSI to the ground-truth orientations from PSOCT. This allows us to investigate the conditions under which CS reconstruction preserves the accuracy of diffusion orientation estimates with respect to PSOCT. We find that, for a CS acceleration factor of R=3, CS-DSI preserves the accuracy of the fully sampled DSI data. That acceleration is sufficient to make the acquisition time of DSI comparable to that of state-of-the-art single- or multi-shell acquisitions. We also show that, as the acceleration factor increases further, different CS reconstruction methods degrade in different ways. Finally, we find that the signal-to-noise (SNR) of the training data used to construct the dictionary can have an impact on the accuracy of the CS-DSI, but that there is substantial robustness to loss of SNR in the test data.

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.

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

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 308 ◽  
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.

scholarly journals Compressed-Sensing Magnetic Resonance Image Reconstruction Using an Iterative Convolutional Neural Network Approach

2020 ◽  
Vol 10 (6) ◽  
pp. 1902
Author(s):  
Fumio Hashimoto ◽  
Kibo Ote ◽  
Takenori Oida ◽  
Atsushi Teramoto ◽  
Yasuomi Ouchi
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Similarity Index ◽  
Structural Similarity ◽  
Neural Network Approach ◽  
Priori Information ◽  
Space Data

Convolutional neural networks (CNNs) demonstrate excellent performance when employed to reconstruct the images obtained by compressed-sensing magnetic resonance imaging (CS-MRI). Our study aimed to enhance image quality by developing a novel iterative reconstruction approach that utilizes image-based CNNs and k-space correction to preserve original k-space data. In the proposed method, CNNs represent a priori information concerning image spaces. First, the CNNs are trained to map zero-filling images onto corresponding full-sampled images. Then, they recover the zero-filled part of the k-space data. Subsequently, k-space corrections, which involve the replacement of unfilled regions by original k-space data, are implemented to preserve the original k-space data. The above-mentioned processes are used iteratively. The performance of the proposed method was validated using a T2-weighted brain-image dataset, and experiments were conducted with several sampling masks. Finally, the proposed method was compared with other noniterative approaches to demonstrate its effectiveness. The aliasing artifacts in the reconstructed images obtained using the proposed approach were reduced compared to those using other state-of-the-art techniques. In addition, the quantitative results obtained in the form of the peak signal-to-noise ratio and structural similarity index demonstrated the effectiveness of the proposed method. The proposed CS-MRI method enhanced MR image quality with high-throughput examinations.

scholarly journals An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1559 ◽  
Author(s):  
Fan Zhang ◽  
Zhichao Xu ◽  
Wei Chen ◽  
Zizhe Zhang ◽  
Hao Zhong ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Discrete Wavelet Transform ◽  
Video Surveillance ◽  
Structural Similarity ◽  
Underground Mines ◽  
Noise Robustness ◽  
Discrete Wavelet ◽  
Surveillance Systems ◽  
Compression Method

Video surveillance systems play an important role in underground mines. Providing clear surveillance images is the fundamental basis for safe mining and disaster alarming. It is of significance to investigate image compression methods since the underground wireless channels only allow low transmission bandwidth. In this paper, we propose a new image compression method based on residual networks and discrete wavelet transform (DWT) to solve the image compression problem. The residual networks are used to compose the codec network. Further, we propose a novel loss function named discrete wavelet similarity (DW-SSIM) loss to train the network. Because the information of edges in the image is exposed through DWT coefficients, the proposed network can learn to preserve the edges better. Experiments show that the proposed method has an edge over the methods being compared in regards to the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), particularly at low compression ratios. Tests on noise-contaminated images also demonstrate the noise robustness of the proposed method. Our main contribution is that the proposed method is able to compress images at relatively low compression ratios while still preserving sharp edges, which suits the harsh wireless communication environment in underground mines.

scholarly journals Digital Watermarking for Image Authentication using Spatial-Scale Domain based Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 2334-2341
Keyword(s):  
Wavelet Transform ◽  
Spatial Scale ◽  
Digital Watermarking ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Authentication ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet

This paper aims in presenting a thorough comparison of performance and usefulness of concept of spatial-scale domain based techniques in digital watermarking in order to sustain the ownership, security and true authentication. Spatial-scale based image watermarking techniques provides the information of 2-Dimensional (2-D) signal at different scales and levels, which is desirable for image watermarking. Further, these techniques emerged as a powerful and efficient tool to overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques. The spatial-scale based watermarking techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT) have been selected for watermarking process. Further, the comparison of performance of the selected watermarking techniques have been carried out in terms of different metrics, such as Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Tamper Assessment Factor (TAF) and Mean Structural Similarity Index (MSSIM). Analysis of result shows that multi-directional and shift-invariant NSCT technique outperforms the SWT and DWT based image watermarking techniques in terms of authentication, better capture quality, and tampering resistance, subjective and objective evaluation.

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