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Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

ABSTRACT: Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B) converges faster than previously proposed algorithms accelerated proximal algorithm (APG) and alternating directional method of multipliers for balanced model (ADMM-B).

SUBMITTER: Liu Y

PROVIDER: S-EPMC4388626 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

Liu Yunsong Y Cai Jian-Feng JF Zhan Zhifang Z Guo Di D Ye Jing J Chen Zhong Z Qu Xiaobo X

PloS one 20150407 4

Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model tha ...[more]

PMID: 25849209

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Project description:Purpose To assess the effect of a combination of compressed sensing and SENSitivity Encoding (SENSE) acceleration techniques on radiation therapy magnetic resonance imaging (MRI) simulation workflows. Methods and Materials Thirty-seven acquisitions were performed with both SENSE-only (SENSE) and combined compressed sensing and SENSE (CS) techniques in 24 patients receiving radiation therapy MRI simulation for a wide range of disease sites. The anatomic field of view prescription and image resolution were identical for both SENSE and CS acquisitions to ensure fair comparison. The acquisition time of all images was recorded to assess time savings. For each image pair, image quality, and ability to contour were assessed by 2 radiation oncologists. Aside from direct image pair comparisons, the feasibility of using CS to improve MRI simulation protocols by increasing image resolution, field of view, and reducing motion artifacts was also evaluated. Results CS resulted in an average reduction of 27% in scan time with negligible changes in image quality and the ability to contour structures for RT treatment planning compared with SENSE. Physician scoring of image quality and ability to contour shows that while SENSE still has slightly better image quality compared with CS, this observed difference in image quality did not affect the ability to contour. In addition, the higher acceleration capability of CS enabled use of superior-inferior direction phase encoding in a sagittal 3-dimensional T2-weighted scan for substantially improved visibility of the prostatic urethra, which eliminated the need for a Foley catheter in most patients. Conclusions The combination of compressed sensing and parallel imaging resulted in marked improvements in the MRI Simulation workflow. The scan time was reduced without significantly affecting image quality in the context of ability to contour. The acceleration capabilities allowed for increased image resolution under similar scanning times as well as significantly improved urethra visualization in prostate simulations.

Project description:BackgroundMagnetic resonance imaging (MRI) is a powerful tool for predicting heart failure (HF) patient prognosis (including death), but it adversely affecting clinical diagnosis and work efficiency. Compressed sensing technology reconstructs and recovers signals using sampling points that are far below the requirement of traditional sampling laws, which can shorten the signal acquisition time without affecting the image quality of MRI. This study aimed to apply compressed sensing technology to the MRI images of patients with HF to evaluate its effectiveness in the diagnosis of HF. Although compressed sensing MRI technology has not yet been widely adopted in clinical practice, it has demonstrated favorable application prospects. Through continuous updating and optimization, it is expected to become a research hotspot in medical imaging, providing more valuable information for clinical work.MethodsIn this study, 66 patients with acute ischemic stroke admitted to hospital were selected for the experimental group, and 20 patients with normal cardiac function who underwent physical examinations during the same period were selected for the control group. An MRI image reconstruction algorithm based on compressed sensing was developed and used in the cardiac MRI image processing.ResultsThe results showed that the e' and heart rate of the experimental group were significantly higher than those of the control group, and the E/e' ratio was significantly lower than that of the control group (P<0.05). The early peak filling rate (PFR1), the PFR1/the late peak filling rate (PFR2), the early filling volume (FV1), and the FV1/the filling volume (FV) of the experimental group were significantly higher than those of the control group, and the PFR2 and the late filling volume (FV2) of the experimental group were significantly lower than those of the control group (P<0.05). The diagnostic sensitivity, specificity, and the area under the curve (AUC) for the concentration-time of the PFR2 were 0.891, 0.788, and 0.904, respectively. The diagnostic sensitivity, specificity, and AUC for the FV2 were 0.902, 0.878, and 0.925, respectively. The peak signal to noise ratio and structural similarity of the images reconstructed using the oral contraceptives algorithm were significantly higher than those determined by the sensitivity coding algorithm and the orthogonal matching pursuit algorithm (P<0.05).ConclusionsThe imaging algorithm based on compressed sensing had excellent processing effect on cardiac MRI and improved the image quality. Cardiac MRI imaging had good diagnostic performance for HF and had the value of clinical popularization.

Project description:BackgroundEndogenous contrast T1? cardiovascular magnetic resonance (CMR) can detect scar or infiltrative fibrosis in patients with ischemic or non-ischemic cardiomyopathy. Existing 2D T1? techniques have limited spatial coverage or require multiple breath-holds. The purpose of this project was to develop an accelerated, free-breathing 3D T1? mapping sequence with whole left ventricle coverage using a multicoil, compressed sensing (CS) reconstruction technique for rapid reconstruction of undersampled k-space data.MethodsWe developed a cardiac- and respiratory-gated, free-breathing 3D T1? sequence and acquired data using a variable-density k-space sampling pattern (A?=?3). The effect of the transient magnetization trajectory, incomplete recovery of magnetization between T1?-preparations (heart rate dependence), and k-space sampling pattern on T1? relaxation time error and edge blurring was analyzed using Bloch simulations for normal and chronically infarcted myocardium. Sequence accuracy and repeatability was evaluated using MnCl2 phantoms with different T1? relaxation times and compared to 2D measurements. We further assessed accuracy and repeatability in healthy subjects and compared these results to 2D breath-held measurements.ResultsThe error in T1? due to incomplete recovery of magnetization between T1?-preparations was T1?healthy =?6.1% and T1?infarct =?10.8% at 60?bpm and T1?healthy =?13.2% and T1?infarct =?19.6% at 90?bpm. At a heart rate of 60?bpm, error from the combined effects of readout-dependent magnetization transients, k-space undersampling and reordering was T1?healthy =?12.6% and T1?infarct =?5.8%. CS reconstructions had improved edge sharpness (blur metric?=?0.15) compared to inverse Fourier transform reconstructions (blur metric?=?0.48). There was strong agreement between the mean T1? estimated from the 2D and accelerated 3D data (R2 =?0.99; P <?0.05) acquired on the MnCl2 phantoms. The mean R1? estimated from the accelerated 3D sequence was highly correlated with MnCl2 concentration (R2 =?0.99; P?<?0.05). 3D T1? acquisitions were successful in all human subjects. There was no significant bias between undersampled 3D T1? and breath-held 2D T1? (mean bias?=?0.87) and the measurements had good repeatability (COV2D =?6.4% and COV3D =?7.1%).ConclusionsThis is the first report of an accelerated, free-breathing 3D T1? mapping of the left ventricle. This technique may improve non-contrast myocardial tissue characterization in patients with heart disease in a scan time appropriate for patients.

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Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

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Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging.

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