Parallel magnetic resonance imaging reconstruction (original) (raw)

A NEW METHOD FOR DATA ACQUISITION AND IMAGE RECONSTRUCTION IN PARALLEL MAGNETIC RESONANCE IMAGING

We propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisitions) method by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial inhomogeneity in the sum-of-squares image. The proposed method is tested using numerical and real MRI phantom data.

New approach for data acquisition and image reconstruction in parallel magnetic resonance imaging

2009

In this study, we propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA algorithm by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows greater uniformity over the field of view than the conventional sum-of-squares (SoS) reconstruction that is conventionally used with GRAPPA. The body coil image can also be used to correct for spatial inhomogeneity in the SoS image. The algorithm has been tested using numerical and real MRI phantom data.

Parallel magnetic resonance imaging

Physics in Medicine and Biology, 2007

Parallel imaging has been the single biggest innovation in magnetic resonance imaging in the last decade. The use of multiple receiver coils to augment the time consuming Fourier encoding has reduced acquisition times significantly. This increase in speed comes at a time when other approaches to acquisition time reduction were reaching engineering and human limits. A brief summary of spatial encoding in MRI is followed by an introduction to the problem parallel imaging is designed to solve. There are a large number of parallel reconstruction algorithms; this article reviews a cross-section, SENSE, SMASH, g-SMASH and GRAPPA, selected to demonstrate the different approaches. Theoretical (the g-factor) and practical (coil design) limits to acquisition speed are reviewed. The practical implementation of parallel imaging is also discussed, in particular coil calibration. How to recognize potential failure modes and their associated artefacts are shown. Well-established applications including angiography, cardiac imaging and applications using echo planar imaging are reviewed and we discuss what makes a good application for parallel imaging. Finally, active research areas where parallel imaging is being used to improve data quality by repairing artefacted images are also reviewed.

New algorithms for parallel MRI

Journal of Physics: Conference Series, 2008

Magnetic Resonance Imaging with parallel data acquisition requires algorithms for reconstructing the patient's image from a small number of measured lines of the Fourier domain (k-space).

Reconstruction of parallel MRI Images Using High Resolution Image Reconstruction Techniques

Magnetic resonance imaging (MRI) has been used extensively for clinical purposes to depict anatomy because of its non-invasiveness to human body. It is always desirable to enhance the resolution of MR images in order to confirm the presence of any suspicious behavior inside the body while keeping the imaging time short. In this paper, we change the setting of the k-space sampling which differ from the typical parallel MRI practice. Instead of aliased images, we obtain a number of low resolution coil images. We use two methods, the total variation (TV) inpainting and inpainting in the frequency domain using tight frame, to reconstruct a high resolution image based on these low resolution coil images.

A Parallel Software for the Reconstruction of Dynamic MRI Sequences

2003

In this paper we present a parallel version of an existing Matlab software for dynamic Magnetic Resonance Imaging which implements a reconstruction technique based on B-spline Reduced-encoding Imaging by Generalized series Reconstruction. The parallel primitives used are provided by MatlabMPI. The parallel Matlab application is tested on a network of Linux workstations.

A brief review of parallel magnetic resonance imaging

European Radiology, 2003

Since the 1980s, the implementation of fast imaging methods and dedicated hardware for MRI scanners has reduced the image acquisition time from nearly an hour down to several seconds and has therefore enabled a widespread use of MRI in clinical diagnosis. Since this development, the greatest incremental gain in imaging speed has been provided by the development of parallel MRI (pMRI) techniques in late 1990s. Within the past 2 years, parallel imaging methods have become commercially available, which means that pMRI is now available for broad clinical use. In the clinical routine, virtually any MRI method can be enhanced by pMRI, allowing faster image acquisitions without any increased gradient system performance. In some cases pMRI can even result in a significant gain in image quality due to this faster acquisition. In this review article, the advantages and the disadvantages of pMRI in clinical applications are discussed and examples from many different daily applications are given.

IIR GRAPPA for parallel MR image reconstruction

Magnetic Resonance in Medicine, 2010

Accelerated parallel MRI has advantage in imaging speed, and its image quality has been improved continuously in recent years. This paper introduces a two-dimensional infinite impulse response model of inverse filter to replace the finite impulse response model currently used in generalized autocalibrating partially parallel acquisitions class image reconstruction methods. The infinite impulse response model better characterizes the correlation of k-space data points and better approximates the perfect inversion of parallel imaging process, resulting in a novel generalized image reconstruction method for accelerated parallel MRI. This k-space-based reconstruction method includes the conventional generalized autocalibrating partially parallel acquisitions class methods as special cases and has a new infinite impulse response data estimation mechanism for effective improvement of image quality.

2000 IJCNN-IEEE_MRI Reconstruction Reg., pp. 336-341.pdf

This paper concems a novel application of Neural Networks to Magnetic Resonance Imaging (MRI) by considering regularized Neural Network models for the problem of image reconstruction from sparsely sampled k-space. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to reduce the measurement time by omitting as many scanning trajectories as possible. This approach, however, entails underdetermined equations and leads to poor image reconstruction. It is proposed here that significant improvements could be achieved concerning image reconstruction if a procedure, based on neural network function approximation methodology and involving regularization techniques, for estimating the missing samples of complex k-space were introduced. To this end, the viability of involving Neural Network algorithms with/without regularization for such a problem is considered and it is found that their image reconstruction results are very favorably compared to the ones obtained by the trivial zero-filled k-space approach or traditional more sophisticated interpolation approaches. Moreover, it is found that regularized Multilayer Perceptrons outperform the ones not involving regularization during their training.