Research On MRI Reconstruction And Artifacts Removal

Magnetic Resonance Imaging (MRI) techniques are used to obtain anatomical, functional, and pathological information of certain part of human bodies or animals based on the principles of nuclear magnetic resonance. MRI is a non-invasive technique, and especially an important tool for medical research. The problem, that the reconstructed images using MRI have low quality, still exists, so more efficient methods are expected.Functional MRI is a new development of MRI technology in the early 1990s. Cerebral cortex functional activation can be imaged real-time based on the contrast mechanisms of blood oxygenation level dependent (BOLD). And the advent of functional MRI leads to a new method for the clinical MR diagnosis, which can transform single morphological research to systematic research that combines morphology with functional information. So functional MRI has become a significant tool to study brain function with high spatial resolution, high temporal resolution, and absolutely non-invasive characteristics.Compared with other imaging techniques, EPI has been an extensively used method. Due to its short acquiring time of EPI, the whole brain can be imaged in several seconds, so functional imaging is of high spatial resolution. Functional activity can be determined by analyzing the task-related signal change. But in 1.5 Tesla magnetic filed, the change of functional signal is only 1～2%. To obtain high functional signal detecting rate, high steady signal is needed. During image acquisition, lots of factors can lead to artifacts and therefore interfere with the functional activated area. Though the problem can be mitigated by improving the hardware property, we can't removal them satisfactorily. K-space based correction methods are used mostly, and post-processing strategy is a considerable method.Physiological artifact is an important interference of functional signal detection, including respiratory and cardiac artifact. The advent of these artifacts interfere with the detection offunctional signal, so efficient methods are expected to be presented.Aiming at problems mentioned above, there are two purposes in this thesis. Firstly, we want to reconstruct high quality image and make trade-off between time resolution and spatial resolution by presenting new MRI reconstruction methods. Secondly, we want to present efficient methods to remove physiological artifacts, thus functional signal can be detected more efficiently.The research achievements include five following methods:1) For non-cartesian acquisition, previous methods use regridding method and then obtain reconstruction results using FFT mostly. Based on non-uniform FFT (NUFFT) method, a new method for non-cartesian acquisition reconstruction is presented. This method gives weighted coefficients through many suitable functions, but previous methods give the weighted coefficients directly. The results show that our method can give high quality reconstruction images.2) For dynamic sequence images, a new high time and spatial resolution image reconstruction method is presented using information relativity. Using prior information from two reference image, high temporal and spatial resolution images can be reconstructed by reducing encoding. In the method, an ill-conditioned system is solved using modified TSVD method. At the same time, L-curve method is used to determine optimal parameter k. Some results using previous methods are given to be compared with our results. According to the comparison, our method is superior to others.3) Physiological artifacts are induced by concrete physiological activity and correspond to certain frequency, so they show obvious character. A method using power spectrum subtraction to removal physiological artifacts is presented. We estimate noise power spectrum from time series with selected voxel because CSF contains physiological noise and random noise without any activated signal. Using power spectrum of interested voxel, spectrum with physiological artifact removal can be obtained by subtracting estimated noise power spectrum. Experimental results show that the method can remove physiological artifacts efficiently.4) A spatial ICA based physiological artifacts removal method is presented. Using spatial ICA method, fMRI data can be decomposed and independent components are obtained. Power spectrum can be calculated using time series corresponding to independent components. we decide that which independent components contain physiological artifacts and reconstruct data by removal these independent components. Results show that the method can remove physiological artifacts efficiently.5) An image-space data based physiological artifacts removal method is given. In acquired multi-slices data research, if typical imaging parameter is used, physiological artifactscan't be critically sampling for each slice. Resulting time series artifacts are contaminated by aliased spectral components from the high-frequency physiological artifacts, and thus previous methods are not efficient anymore. We reorder the data from original slice ordering to time ordering and obtain time series by calculating mean value of every image. Thus physiological frequency is estimated by calculating power spectrum of time series. According to the property of aliasing, we can decide the aliasing position and then remove physiological artifacts using digital filter. Experimental results illustrate that the method can remove physiological artifacts preferably.At the end of the paper, prospects of future research are given. The topics on how to reconstruct high quality MR image and how to remove various artifacts efficiently are our future research keystone.