scholarly journals The Influence of Radial Undersampling Schemes on Compressed Sensing in Cardiac DTI

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2388
Author(s):  
Jianping Huang ◽  
Wenlong Song ◽  
Lihui Wang ◽  
Yuemin Zhu
Keyword(s):  
Compressed Sensing ◽  
Diffusion Tensor ◽  
Acquisition Time ◽  
Lower Sensitivity ◽  
Sampling Schemes ◽  
Radial Sampling ◽  
Golden Angle ◽  
Reconstruction Quality ◽  
Space Data ◽  
Random Angle

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction.

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 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 CS-GAN for High-Quality Diffusion Tensor Imaging

2020 ◽  
Author(s):  
Ying Cao ◽  
Lihui Wang ◽  
Jianping Huang ◽  
Xinyu Cheng ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Image Quality ◽  
Compressed Sensing ◽  
Spatial Information ◽  
Diffusion Tensor ◽  
High Quality ◽  
Image Domain ◽  
Low Snr ◽  
Sampling Ratio ◽  
Space Data

Abstract Background: Compressed sensing magnetic resonance imaging (CS-MRI) is a promising technique for accelerating MRI speed. However, image quality in CS-MRI is still a pertinent problem. In particular, there is little work on reducing aliasing artefacts in compressed sensing diffusion tensor imaging (CS-DTI), which constitute a serious obstacle to obtaining high-quality images. Method: We propose a CS-DTI de-aliasing method based on conditional generative adversarial (cGAN), called CS-GAN, to tackle de-aliasing problems in CS-DTI with highly undersampled k-space data. The method uses a nested-UNet based generator, a ResNet-based discriminator, and a content loss function defined in both image domain and frequency domain. Result and Concludions: Compared to existing state-of-the-art de-aliasing methods based on deep learning, our method achieves superior imaging quality in terms of both diffusion weighted (DW) image quality and DTI diffusion metrics. Moreover, even at extremely low sampling ratio and low SNR, our method can still reconstruct texture details and spatial information.

scholarly journals Compressed Sensing MRI Reconstruction with Multiple Sparsity Constraints on Radial Sampling

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jianping Huang ◽  
Lihui Wang ◽  
Yuemin Zhu
Keyword(s):  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Splitting Algorithm ◽  
Radial Sampling ◽  
Reconstruction Methods ◽  
Nonlocal Total Variation ◽  
L2 Norm ◽  
Mri Reconstruction ◽  
Space Data

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique for accelerating MRI acquisitions by using fewer k-space data. Exploiting more sparsity is an important approach to improving the CS-MRI reconstruction quality. We propose a novel CS-MRI framework based on multiple sparse priors to increase reconstruction accuracy. The wavelet sparsity, wavelet tree structured sparsity, and nonlocal total variation (NLTV) regularizations were integrated in the CS-MRI framework, and the optimization problem was solved using a fast composite splitting algorithm (FCSA). The proposed method was evaluated on different types of MR images with different radial sampling schemes and different sampling ratios and compared with the state-of-the-art CS-MRI reconstruction methods in terms of peak signal-to-noise ratio (PSNR), feature similarity (FSIM), relative l2 norm error (RLNE), and mean structural similarity (MSSIM). The results demonstrated that the proposed method outperforms the traditional CS-MRI algorithms in both visual and quantitative comparisons.

Compressed Sensing in Parallel MRI: A Review

2021 ◽  
pp. 2250038
Author(s):  
Rafiqul Islam ◽  
Md Shafiqul Islam ◽  
Muhammad Shahin Uddin
Keyword(s):  
Compressed Sensing ◽  
Imaging Technique ◽  
Reconstruction Algorithm ◽  
Acquisition Time ◽  
Reconstruction Algorithms ◽  
Parallel Mri ◽  
Image Domain ◽  
Conventional Mri ◽  
Magnetic Resonance Imaging Mri ◽  
Space Data

Magnetic resonance imaging (MRI) is a dynamic and safe imaging technique in medical imaging. Recently, parallel MRI (pMRI) is widely used for accelerating conventional MRI. Both frequency and image domain-based reconstructions are the most attractive methods for generating the image from multi-channel k-space data. Compressed sensing (CS) is a recently used procedure to reduce the acquisition time of conventional MRI. This reduction is achieved by taking fewer measurements from the fully sampled k-space data. Therefore, applying the CS technique in pMRI is the most emerging way for further improving the acquisition time that is a tremendous research interest. However, as the phase encoding plane may be perpendicular or parallel to the coil elements plane, finding the exact domain for CS in pMRI reconstruction is a major challenging issue. In this work, the application of the CS technique in pMRI in both domains is investigated. Later some widely used methodologies are presented as the nonlinear reconstruction algorithm of CS in pMRI. Finally, a discussion is performed based on CS in pMRI to perceive the reality of different reconstruction algorithms at a glance for finding preferred methodologies.

The effect of microvascular obstruction on the myocardial microstructure: a diffusion tensor imaging study

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
A Das ◽  
K Kelly ◽  
M Aldred ◽  
I Teh ◽  
CK Stoeck ◽  
...  
Keyword(s):  
Microvascular Obstruction ◽  
Diffusion Tensor ◽  
Spin Echo ◽  
Mean Diffusivity ◽  
Imaging Study ◽  
Free Breathing ◽  
Helix Angle ◽  
Acquisition Time ◽  
Heart Research

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Heart Research UK Background Diffusion tensor cardiac magnetic resonance (DT-CMR) imaging allows for characterising myocardial microstructure in-vivo using mean diffusivity (MD), fractional anisotropy (FA), secondary eigenvector angle (E2A) and helix angle (HA) maps. Following myocardial infarction (MI), alterations in MD, FA and HA proportions have previously been reported. E2A depicts the contractile state of myocardial sheetlets, however the behaviour of E2A in infarct segments, and all DTI markers in areas of microvascular obstruction (MVO) is also not fully understood.  Purpose We performed spin echo DTI in patients following ST-elevation MI (STEMI) in order to investigate acute changes in DTI parameters in remote and infarct segments both with and without MVO. Method Twenty STEMI patients (16 men, 4 women, mean age 59) had acute (5 ± 2d) 3T CMR scans. CMR protocol included: second order motion compensated (M012) free-breathing spin echo DTI (3 slices, 18 diffusion directions at b-values 100s/mm2[3], 200s/mm2[3] and 500s/mm2[12], reconstructed resolution was 1.66x1.66x8mm); cine and late gadolinium enhancement (LGE) imaging. Average MD, FA, E2A HA parameters were calculated on a  16 AHA segmental level. HA maps were described by dividing values into left-handed HA (LHM, -90° < HA < -30°), circumferential HA (CM, -30° < HA < 30°), and right-handed HA (RHM, 30° < HA < 90°) and reported as relative proportions. Segments were defined as infarct (positive for LGE) and remote (opposite to the infarct).  Results DTI acquisition was successful in all patients (acquisition time 13 ± 5mins). Ten patients had evidence of MVO on LGE images. MD was significantly higher in infarct regions in comparison to remote; MVO-ve infarct segments had significantly higher MD than MVO + ve infarct segments (MD remote= 1.46 ± 0.12x10-3mm2/s, MD MVO + ve = 1.59 ± 0.12x10-3mm2/s, MD MVO-ve  = 1.75 ± 0.12x10-3mm2/s, ANOVA p < 0.01). FA was reduced in infarct segments in comparison to remote; MVO-ve infarct segments had significantly lower FA than MVO + ve infarct segments (FAremote= 0.37 ± 0.02, FA MVO + ve = 0.31 ± 0.02 x 10-3mm2/s, MD MVO-ve =0.25 ± 0.02, ANOVA p < 0.01). E2A values were significantly lower in infarct segments compared to remote; MVO + ve infarct segments had significantly lower values than MVO-ve. (E2A remote= 57.4 ± 5.2°, E2A MVO-ve = 46.8 ± 2.5°, E2A MVO + ve = 36.8 ± 3.1°, ANOVA p < 0.001). RHM% (corresponding to subendocardium) was significantly lower in infarct segments compared to remote; MVO + ve infarct segments had significantly lower RHM% than MVO-ve. (RHM remote= 37 ± 3%, RHM RHM MVO-ve= 28 ± 7%, MVO + ve= 8 ± 5%, ANOVA p < 0.001). Conclusion The presence of MVO results in a decrease in MD and increase in FA in comparison to surrounding infarct segments. However, the reduction in E2A and right-handed myocytes on HA in infarct segments is further exacerbated by the presence of MVO. Further study is required to investigate the underlying mechanisms for such alterations in signal intensity. Abstract Figure. A case of transmural septal MI with MVO

Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction

2014 ◽  
Vol 989-994 ◽  
pp. 3946-3951
Author(s):  
Xin Jin ◽  
Ming Feng Jiang ◽  
Jie Feng
Keyword(s):  
Compressed Sensing ◽  
Group Structure ◽  
Direct Method ◽  
Group Sparsity ◽  
Mr Image ◽  
Assignment Method ◽  
Group Assignment ◽  
Reconstruction Methods ◽  
Reconstruction Performance ◽  
Space Data

Exploiting the sparsity of MR signals, Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct a MR image with good quality from highly under-sampled k-space data. The group sparse method, which exploits additional sparse representation of the spatial group structure, can promote the overall sparsity degree, thereby leading to better reconstruction performance. In this work, an efficient superpixel/group assignment method, simple linear iterative clustering (SLIC), is incorporated to CS-MRI studies. A variable splitting strategy and classic alternating direct method is employed to solve the group sparse problem. The results indicate that the proposed method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction methods.

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