Rapid Non-Cartesian Regularized SENSE Reconstruction using a Point Spread Function Model
نویسندگان
چکیده
Synopsis Iterative reconstructions of undersampled non-Cartesian data are computationally expensive because non-Cartesian Fourier transforms are much less e cient than Cartesian Fast Fourier Transforms. Here, we introduce an algorithm that does not require non-uniform Fourier transforms during optimization iterations, resulting in large reductions in computation times with no impairment of image quality.
منابع مشابه
HYR2PICS: Hybrid regularized reconstruction for combined parallel imaging and compressive sensing in MRI
Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data in the k-space. So far, first attempts to combine sensitivity encoding (SENSE) imaging in pMRI with CS have been proposed in the context of Cartesian trajectories. Here, we extend these approaches to non-Cartesian trajectories...
متن کاملSelection of image support region and of an improved regularization for non-Cartesian SENSE
INTRODUCTION Compared to the well-behaved, highly localized and equi-spaced aliasing pattern that results from undersampling with Cartesian k-space trajectories, undersampling in non-Cartesian k-space results in a more complex and widespread aliasing pattern, in which all pixels in the reduced sampling image interact with the point spread function of all other pixels in the image. Typically, it...
متن کاملAugmented Lagrangian with Variable Splitting for Faster Non-Cartesian 𝕃1-SPIRiT MR Image Reconstruction
SPIRiT (iterative self-consistent parallel imaging reconstruction), and its sparsity-regularized variant L1-SPIRiT, are compatible with both Cartesian and non-Cartesian magnetic resonance imaging sampling trajectories. However, the non-Cartesian framework is more expensive computationally, involving a nonuniform Fourier transform with a nontrivial Gram matrix. We propose a novel implementation ...
متن کاملEdge Artifacts in Point Spread Function-based PET Reconstruction in Relation to Object Size and Reconstruction Parameters
Objective(s): We evaluated edge artifacts in relation to phantom diameter and reconstruction parameters in point spread function (PSF)-based positron emission tomography (PET) image reconstruction.Methods: PET data were acquired from an original cone-shaped phantom filled with 18F solution (21.9 kBq/mL) for 10 min using a Biograph mCT scanner. The images were reconstructed using the baseline or...
متن کاملA Sparse Spectral Deconvolution Algorithm for Non-cartesian Mrsi
Purpose: To minimize line shape distortions and spectral leakage artifacts in MR spectroscopic imaging. Methods: A spatially and spectrally regularized non-Cartesian MRSI algorithm that uses the line shape distortion priors, estimated from water reference data, to deconvolve the spectra is introduced. Sparse spectral regularization is used to minimize noise amplification associated with deconvo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017