A Study Of Artifacts Suppression Approaches For Parallel MRI

Magnetic resonance imaging (MRI) has been considered as one of the greatest inventions in the last century. Compared with computer tomography (CT), MRI can provide images with higher soft tissue contrast and spatial resolution and without harmful radiation. In the meantime, MR is imaging technique that provides many parameters to choose for doctors. It not only clearly display human's anatomic structure, but reflect all kinds of vital functions. Therefore, MRI has been widely applied in clinics, and maybe the most promising non-invasive diagnostic tool in medicine.Speed has always been a critical consideration in MR imaging. Thus, researchers keep devoting themselves to making improvements in acquisition speed over the past decade. Early clinical applications required hours for even the simplest examinations. During the 1990s, advances in field strength, gradient hardware, and pulse sequences brought tremendous improvements in imaging speed. At the same time, it became obvious that further increases in speed along these lines would be progressively difficult to achieve because of physiological limitations. Rapidly switched field gradients produce neuromuscular stimulation, and excessively dense RF pulse trains can lead to unacceptable levels of RF energy deposition and tissue heating. The evolution of MR imaging appeared to have encountered fundamental constraints in terms of speed.In the course of the past decade, a new approach to MR image acquisition has been developed, which can extend or circumvent the speed limits traditionally associated with gradient and RF hardware. This approach, known as "parallel MRI" has taken the world of MRI by storm in the last few years, promising dramatic gains in imaging speed, a reduction in motion and susceptibility artifacts. In this study, we also focused our mind on this new technique. The aim of this work is to resolve the problems of artifacts elimination through which we proposed different strategies based on parallel MRI. We dedicated our study to the service of clinical requirement.From the point of the mode of imaging, the main reason of time consuming is that, in conventional MR imaging, the phase-encoding steps are performed in sequential order by switching a magnetic field gradient step by step, and this determines the speed of the measurement. The number of phase-encoding steps also defines image resolution in the phase-encoding direction (e.g., 256 steps for a 256×256 matrix). In parallel imaging, spatial encoding using multiple coils partially replaces the spatial encoding normally accomplished using gradients, because additional information is obtained from the spatial variation in the coil sensitivity profiles. Simply put, parallel imaging techniques use the spatial information inherent in local coil arrays to replace time-consuming phase encoding steps. Nevertheless, as a new technique, it is inevitable that there are some disadvantages for parallel MRI. For example, the physics of MR dictate that the signal to-noise ratio (SNR) will be reduced when using parallel imaging techniques. However, acceleration factors of 2 or higher can be achieved with virtually no loss in diagnostic accuracy and utility in MR applications today.Great progress in the development of pMRI methods has taken place, thereby producing a multitude of different and somewhat related parallel imaging reconstructtion techniques and strategies. The SENSitivity Encoding (SENSE) is the earliest technique that was developed and was also applied successfully to in vivo imaging based on the parallel MRI. This technique are already implemented and provided as a standard equipment of the MRI scanners with multiple receiver channels. A number of medical companies offer the most robust and versatile parallel imaging solution on the market, for example, GE's ASSET (Array Spatial Sensitivity Encoding Technique) on EXCITEⅡMR scanner, Philips's SyncraScan and Siemens's iPAT (integrated Parallel Acquisition Techniques) et al.However, the appearance of artifacts and noise are inevitable in whole imaging process. In the current version of the algorithm so far, the effect of the noise is removed by use of the appropriate noise model. In this study, we analyze the advantages and disadvantages of present parallel MRI methods in clinical applications and propose the new reconstruction strategy based on robust estimator frame to satisfy clinical requirements.The research showed that the images from component array coil may have strong artifacts due to motion, which in turn have the significant effect on reconstructed composite image. The least squares solution can get arbitrarily wrong when high residual errors are used in the estimation. In this article, these corrupted data are regarded as outliers in observed datum. This paper presents a new algorithm for SENSE which is optimal in presence of artifacts or noise.Additionally, with the appearance of the parallel MRI, fast imaging of MR come into a new stage. How to combine parallel MR with conventional fast imaging methods is a hot subject of the ongoing research. Partial Fourier acquisition and parallel imaging are two effective fast imaging techniques. Both techniques permit decreased acquisition times by reducing the amount of phased encoding needed. The benefits of the combined technique compared with the individual techniques are large reduced imaging time. Unfortunately, during the partial Fourier reconstruction processing, restoration of missing uncollected k-space data rely on corrupt Hermitian conjugate symmetry. This leads to minor phase variations and noise introduced, which in turn corrupt data are amplified in SENSE process. In this study, a constrained reconstruction algorithm is presented to remove artifact in images. We apply robust Annealing M (AM) Estimator scheme to suppress the corrupt data points and make solutions insensitive to the influence caused by outliers.Parallel imaging gives us unprecedented flexibility in exploiting the MR signal, so that you can choose to "trade" speed for improved spatial resolution. In this way, higher resolution images can be obtained in the same amount of time as a conventional exam. Consequently, how to determine the optimum way to combine individual coil images is a key problem during reconstruction. Sum-of-Squares (SOS) is considered a common reconstruction method for phased array magnetic resonance imaging with unknown coil sensitivity. However, if data from one or some coils are corrupt due to motion, SOS results show obvious artifact in combined image. The reason for this is that coil images are combined by equal weights, so that the artifact is not suppressed due to corrupt data. In this work, an alternative technique that is Weighted-Sum-of-Squares (WSOS) method is presented which aim at decreasing the significance of corrupt samples in the data set and hence attenuating artifacts in reconstruction. The WSOS approach is significant valid and robust when there are certain artifacts such as sporadic motion or noise during data acquisition.In conclusion, a novel method for parallel MRI reconstruction was proposed and implemented this is the so-called "AM-SENSE" algorithm. This idea is based on SENSE. The purpose of this work is to deal with the problems of algorithm in present of noise and artifacts. It is shown to be of higher accuracy than the commercial method used in practice. In addition, the proposed algorithm similarly provides acceptable image quality utilizing the idea of applying both speed-up methods simultaneously to provide an almost unlimited reduction in acquisition time. Besides, in this study, we proposed an alternative image combination technique using phased array coils. The WSOS approach is significant valid and robust when there are certain artifacts, e.g. sporadic motion or noise.