scholarly journals Rank-One and Transformed Sparse Decomposition for Dynamic Cardiac MRI

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xianchao Xiu ◽  
Lingchen Kong
Keyword(s):  
Cardiac Mri ◽  
Low Rank ◽  
Cine Mri ◽  
Sparse Decomposition ◽  
Sparse Model ◽  
Cardiac Cine ◽  
Rank One ◽  
Magnetic Resonance Imaging Mri ◽  
Alternative Direction Method ◽  
Space Data

It is challenging and inspiring for us to achieve high spatiotemporal resolutions in dynamic cardiac magnetic resonance imaging (MRI). In this paper, we introduce two novel models and algorithms to reconstruct dynamic cardiac MRI data from under-sampledk-tspace data. In contrast to classical low-rank and sparse model, we use rank-one and transformed sparse model to exploit the correlations in the dataset. In addition, we propose projected alternative direction method (PADM) and alternative hard thresholding method (AHTM) to solve our proposed models. Numerical experiments of cardiac perfusion and cardiac cine MRI data demonstrate improvement in performance.

scholarly journals Joint Reconstruction Framework of Compressed Sensing and Nonlinear Parallel Imaging for Dynamic Cardiac Magnetic Resonance Imaging

2021 ◽  
Author(s):  
Zhanqi Hu ◽  
Cailei Zhao ◽  
Xia Zhao ◽  
Lingyu Kong ◽  
Jun Yang ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Parallel Imaging ◽  
Dynamic Imaging ◽  
Cine Mri ◽  
Reconstruction Process ◽  
Joint Reconstruction ◽  
Cardiac Cine ◽  
Nonlinear Technique ◽  
Space Data

Abstract Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we propose a new reconstruction framework for dynamic cardiac imaging that takes advantage of both CS-based dynamic imaging and one nonlinear parallel imaging technique. The method decouples the reconstruction process into two sequential steps: use CS to reconstruct a series of aliased dynamic images from the highly undersampled k-space data; use nonlinear GRAPPA method, one nonlinear technique of parallel imaging, to reconstruct the original dynamic images from the k-space data that has been reconstructed by CS. The sampling scheme of the proposed method is designed to simultaneously satisfy the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. Four in vivo experiments of dynamic cardiac cine MRI were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show the proposed method of dynamic cardiac cine MRI is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, when compared with k-t FOCUSS and k-t FOCUSS with sensitivity encoding, using the same numbers of measurements. The proposed joint reconstruction framework effectively combines the CS method and one nonlinear technique of parallel imaging, and improves the image quality of dynamic cardiac cine MRI reconstruction when comparing to the state-of-the-art methods.

Motion Adaptive Patch-Based Low-Rank Approach for Compressed Sensing Cardiac Cine MRI

2014 ◽  
Vol 33 (11) ◽  
pp. 2069-2085 ◽  
Author(s):  
Huisu Yoon ◽  
Kyung Sang Kim ◽  
Daniel Kim ◽  
Yoram Bresler ◽  
Jong Chul Ye
Keyword(s):  
Compressed Sensing ◽  
Low Rank ◽  
Cine Mri ◽  
Cardiac Cine

scholarly journals Accelerated cardiac cine MRI using locally low rank and finite difference constraints

2016 ◽  
Vol 34 (6) ◽  
pp. 707-714 ◽  
Author(s):  
Xin Miao ◽  
Sajan Goud Lingala ◽  
Yi Guo ◽  
Terrence Jao ◽  
Muhammad Usman ◽  
...  
Keyword(s):  
Finite Difference ◽  
Low Rank ◽  
Cine Mri ◽  
Cardiac Cine ◽  
Difference Constraints

A kernel method for higher temporal resolution MRI using the partial separability (PS) model

2016 ◽  
Vol 61 (4) ◽  
pp. 393-400
Author(s):  
Xiang Feng ◽  
Guoxi Xie ◽  
Xin Liu ◽  
Bensheng Qiu
Keyword(s):  
Temporal Resolution ◽  
Kernel Method ◽  
Dynamic Change ◽  
Dynamic Mri ◽  
Training Data ◽  
Cine Mri ◽  
Partial Separability ◽  
Cardiac Cine ◽  
Magnetic Resonance Imaging Mri

Abstract The partial separability (PS) model for spatiotemporal signals has been exploited effectively for sparse (k, t)-space sampling in dynamic magnetic resonance imaging (MRI). However, the training data for defining the temporal subspace is reordered by using a projection strategy in the conventional PS model-based method, which results in a suboptimal temporal resolution imaging. To address this issue, a kernel method was presented in this work to reorder the training data to realize a higher temporal resolution MRI. Numerical simulation results show that the MRI temporal resolution could be further improved and the dynamic change of motion object could be accurately captured by the proposed method. In vivo cardiac cine MRI results demonstrate that the proposed method can reconstruct better MR images with higher temporal resolution (up to 8.4 ms per snapshot). This study may find use in ultra-high resolution dynamic MRI.

scholarly journals Haemodynamic changes in cirrhosis following terlipressin and induction of sepsis—a preclinical study using caval subtraction phase-contrast and cardiac MRI

2020 ◽  
Author(s):  
Manil D. Chouhan ◽  
Stuart A. Taylor ◽  
Alan Bainbridge ◽  
Simon Walker-Samuel ◽  
Nathan Davies ◽  
...  
Keyword(s):  
Heart Rate ◽  
Blood Flow ◽  
Cardiac Mri ◽  
Phase Contrast ◽  
Systolic Function ◽  
Cine Mri ◽  
Chronic Liver Failure ◽  
Cardiac Cine ◽  
Haemodynamic Changes ◽  
Cardiac Systolic Function

Abstract Objectives Effects of liver disease on portal venous (PV), hepatic arterial (HA), total liver blood flow (TLBF), and cardiac function are poorly understood. Terlipressin modulates PV flow but effects on HA, TLBF, and sepsis/acute-on-chronic liver failure (ACLF)-induced haemodynamic changes are poorly characterised. In this study, we investigated the effects of terlipressin and sepsis/ACLF on hepatic haemodynamics and cardiac function in a rodent cirrhosis model using caval subtraction phase-contrast (PC) MRI and cardiac cine MRI. Methods Sprague-Dawley rats (n = 18 bile duct–ligated (BDL), n = 16 sham surgery controls) underwent caval subtraction PCMRI to estimate TLBF and HA flow and short-axis cardiac cine MRI for systolic function at baseline, following terlipressin and lipopolysaccharide (LPS) infusion, to model ACLF. Results All baseline hepatic haemodynamic/cardiac systolic function parameters (except heart rate and LV mass) were significantly different in BDL rats. Following terlipressin, baseline PV flow (sham 181.4 ± 12.1 ml/min/100 g; BDL 68.5 ± 10.1 ml/min/100 g) reduced (sham − 90.3 ± 11.1 ml/min/100 g, p < 0.0001; BDL − 31.0 ± 8.0 ml/min/100 g, p = 0.02), sham baseline HA flow (33.0 ± 11.3 ml/min/100 g) increased (+ 92.8 ± 21.3 ml/min/100 g, p = 0.0003), but BDL baseline HA flow (83.8 ml/min/100 g) decreased (− 34.4 ± 7.5 ml/min/100 g, p = 0.11). Sham baseline TLBF (214.3 ± 16.7 ml/min/100 g) was maintained (+ 2.5 ± 14.0 ml/min/100 g, p > 0.99) but BDL baseline TLBF (152.3 ± 18.7 ml/min/100 g) declined (− 65.5 ± 8.5 ml/min/100 g, p = 0.0004). Following LPS, there were significant differences between cohort and change in HA fraction (p = 0.03) and TLBF (p = 0.01) with BDL baseline HA fraction (46.2 ± 4.6%) reducing (− 20.9 ± 7.5%, p = 0.03) but sham baseline HA fraction (38.2 ± 2.0%) remaining unchanged (+ 2.9 ± 6.1%, p > 0.99). Animal cohort and change in systolic function interactions were significant only for heart rate (p = 0.01) and end-diastolic volume (p = 0.03). Conclusions Caval subtraction PCMRI and cardiac MRI in a rodent model of cirrhosis demonstrate significant baseline hepatic haemodynamic/cardiac differences, failure of the HA buffer response post-terlipressin and an altered HA fraction response in sepsis, informing potential translation to ACLF patients. Key Points Caval subtraction phase-contrast and cardiac MRI demonstrate: • Significant differences between cirrhotic/non-cirrhotic rodent hepatic blood flow and cardiac systolic function at baseline. • Failure of the hepatic arterial buffer response in cirrhotic rodents in response to terlipressin. • Reductions in hepatic arterial flow fraction in the setting of acute-on-chronic liver failure.

Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model

2016 ◽  
Vol 77 (4) ◽  
pp. 1505-1515 ◽  
Author(s):  
Samuel T. Ting ◽  
Rizwan Ahmad ◽  
Ning Jin ◽  
Jason Craft ◽  
Juliana Serafim da Silveira ◽  
...  
Keyword(s):  
Cine Mri ◽  
Sparse Model ◽  
Fast Implementation ◽  
Cardiac Cine

Segmentation of Biventricle in Cardiac Cine MRI via Nested Capsule Dense Network

2021 ◽  
Author(s):  
Yuhang Hu ◽  
Yajuan Zhang ◽  
Hongyang Zhang ◽  
Weihao Shen ◽  
Shoujun Zhou ◽  
...  
Keyword(s):  
Cardiac Mri ◽  
Spatial Information ◽  
Cine Mri ◽  
Cardiac Magnetic Resonance Image ◽  
Dense Network ◽  
Evaluation Standard ◽  
Cardiac Cine ◽  
Proposed Model ◽  
Physiological Indexes ◽  
Cardiac Diagnosis

Abstract Cardiac magnetic resonance image (MRI) has been widely used in diagnosis of cardiovascular diseases because of its noninvasive nature and high image quality. The evaluation standard of physiological indexes in cardiac diagnosis is essentially the accuracy of segmentation of left ventricle (LV) and right ventricle (RV) in cardiac MRI. In this paper, we propose a novel Nested Capsule Dense Network (NCDN) structure based on the FC-DenseNet model and capsule convolution-capsule deconvolution. Different from the traditional symmetric single codec network structure such as U-net, NCDN uses multiple codecs instead of a single codec to achieve multi-resolution, which makes it possible to save more spatial information and improve the robustness of the model. The proposed model is tested on three datasets that includes York University Cardiac MRI dataset, Automated Cardiac Diagnosis Challenge (ACDC-2017), and local dataset. The results show that the proposed NCDN outperforms the state-of-the-art methods.

scholarly journals Low-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Junbo Chen ◽  
Shouyin Liu ◽  
Min Huang
Keyword(s):  
Inverse Problem ◽  
Convex Optimization ◽  
Principal Component ◽  
Dynamic Mri ◽  
Low Rank ◽  
Cine Image ◽  
Sparse Decomposition ◽  
Decomposition Model ◽  
Reconstruction Methods ◽  
Space Data

The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse problem can be solved by convex optimization method. We propose a scalable and fast algorithm based on the inexact augmented Lagrange multipliers (IALM) to carry out the convex optimization. The experimental results demonstrate that our proposed algorithm can achieve superior reconstruction quality and faster reconstruction speed in cardiac cine image compared to existing state-of-art reconstruction methods.

An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

2019 ◽  
Vol 15 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Mei Sun ◽  
Jinxu Tao ◽  
Zhongfu Ye ◽  
Bensheng Qiu ◽  
Jinzhang Xu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Low Rank ◽  
Mr Images ◽  
Scanning Time ◽  
Previous State ◽  
Rank Approximation ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Rank Constraints

Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction. </P><P> Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction. Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation. </P><P> Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

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