Magnetic Resonance in Medicine 71:1760–1770 (2014) Monte Carlo SURE-Based Parameter Selection for Parallel Magnetic Resonance Imaging Reconstruction
نویسندگان
چکیده
Purpose: Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein’s unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods that preserve the undersampled acquired data, which cannot be accomplished using existing techniques. Theory: We derive a weighted MSE criterion appropriate for data-preserving regularized parallel imaging reconstruction and the corresponding weighted Stein’s unbiased risk estimate. We describe a Monte Carlo approximation of the weighted Stein’s unbiased risk estimate that uses two evaluations of the reconstruction method per candidate parameter value. Methods: We reconstruct images using the denoising sparse images from GRAPPA using the nullspace method (DESIGN) and L1 iterative self-consistent parallel imaging (L1-SPIRiT). We validate Monte Carlo Stein’s unbiased risk estimate against the weighted MSE. We select the regularization parameter using these methods for various noise levels and undersampling factors and compare the results to those using MSE-optimal parameters. Results: Our method selects nearly MSE-optimal regularization parameters for both DESIGN and L1-SPIRiT over a range of noise levels and undersampling factors. Conclusion: The proposed method automatically provides nearly MSE-optimal choices of regularization parameters for data-preserving nonlinear parallel MRI reconstruction methods. Magn Reson Med 71:1760–1770, 2014. © 2013 Wiley Periodicals, Inc.
منابع مشابه
Monte Carlo SURE-based parameter selection for parallel magnetic resonance imaging reconstruction.
PURPOSE Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection. We propose a Monte Carlo method for automatic parameter selection based on Stein's unbiased risk estimate that minimizes the multichannel k-space mean squared error (MSE). We automatically tune parameters for image reconstruction methods tha...
متن کاملANALYTICAL STUDY OF EFFECT OF BILAYER INORGANIC AND ORGANIC COATING AROUND THE IRON OXIDE NANOPARTICLES ON MAGNETIC RESONANCE IMAGING CONTRAST
Background & Aims: In recent years, iron oxide nanoparticles have been used in contrast-enhanced magnetic resonance imaging for diagnosing a wide range of diseases. In order to provide biocompatibility and prevent the toxicity of the nanoparticles, using organic or inorganic coating around these nanoparticles is important for their application. The aim of this study is to investigate the effect...
متن کاملEffect of Bias in Contrast Agent Concentration Measurement on Estimated Pharmacokinetic Parameters in Brain Dynamic Contrast-Enhanced Magnetic Resonance Imaging Studies
Introduction: Pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely applied in tumor diagnosis and treatment evaluation. Precision analysis of the estimated PK parameters is essential when they are used as a measure for therapy evaluation or treatment planning. In this study, the accuracy of PK parameters in brain DCE...
متن کاملApplication of Magnetic Resonance Imaging (MRI) as a safe & Application of Magnetic Resonance Imaging (MRI) as a safe & non-destructive method for monitoring of fruit & vegetable in postharvest period
To investigate and control quality, one must be able to measure quality-related attributes. Quality of produce encompasses sensory attributes, nutritive values, chemical constituents, mechanical properties, functional properties and defects. MRI has great potential for evaluating the quality of fruits and vegetables. The equipment now available is not feasible for routine quality testing. The ...
متن کاملAccelerating Magnetic Resonance Imaging through Compressed Sensing Theory in the Direction space-k
Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI can be accelerated through several methods such as parallel imaging and compressed sensing, where a fraction of the K-space lines...
متن کامل