Research Of New Phase Imaging Procedures For Parallel Multichannel Magnetic Resonance

The signals acquired by magnetic resonance imaging(MRI)are complex,and amplitude images are widely used in routine diagnosis,while the phase components are often discarded.Whereas,the phase images contain important information such as tissue magnetic sensitivity,transverse relaxation rate T2* and magnetic field inhomogeneity,which are widely used in Dixon water and fat separation,susceptibility weighted imaging,and quantitative susceptibility mapping.Early single-channel MRI has the disadvantages of long scanning time and low signal-to-noise ratio(SNR).In recent years,the developed parallel MRI technologies can effectively improve the imaging speed and obtain the phase image of higher SNR by keeping the scanning field of view and spatial resolution unchanged.However,for both the single-channel and the parallel multi-channel MRI,the phase calculated from the complex MR signal by using the angle function(such as arctangent)is generally wrapped into the range of(-?,?] radian.Therefore,the calculated phase can also be called the wrapped phase.The process of obtaining the underlying true phase image from the wrapped phase image is called phase unwrapping.Phase unwrapping is generally based on the assumption that the underlying true phase is smooth and the phase difference between adjacent pixels is less than ?.If this assumption is satisfied,then the true phase map can be easily obtained.In the presence of strong noise,rapid phase changes or disconnected region in region of interest,the true phase difference between adjacent pixels may be above ?.In this scenario,accurately recovering the underlying true phase is a non-trivial task,especially for two and higher dimensional data.To address the above issues,three new phase imaging methods were proposed in this paper,and were evaluated by using simulation and in vivo MR Dixon water-fat separation and quantitative susceptibility mapping data.The main research content is divided into the following three parts:(1)A novel two-dimensional phase imaging method based on pixel clustering and local surface fitting is proposed,which can work robustly in the presence of severe noise,rapid phase changes or disconnected region in region of interest.The MR phase map usually varies rapidly in regions adjacent to wraps.In contrast,the phasors can vary slowly,especially in regions distant from tissue boundaries.Based on this observation,by exploiting the different local variation characteristics between the phase and phasor data,the proposed approach classifies pixels into easy-to-unwrap blocks and difficult-to-unwrap residual pixels first,and then sequentially performs intra-block,inter-block,and residual-pixel phase unwrapping by a region-growing surface fitting method.In the simulation experiment,the mean error ratio by the proposed method was less than 1.50%,even in areas with SNR equal to 0.5,phase changes larger than ?,and disconnected regions.For 350 in vivo knee and ankle images,the water–fat swap ratio of the proposed method was 4.29%.(2)A three-dimensional phase imaging method based on pixel clustering and local polynomial fitting is proposed,and the proposed method is applied in quantitative susceptibility mapping to achieve reliable and accurate tissue susceptibility measurement.The algorithm based on pixel clustering and local surface fitting is extended from two-dimensional to three-dimensional.The extended method based on pixel clustering and local surface fitting calculates the local difference of wrapped phase and local difference of underlying true phase in a 26-neighborhood window,groups the voxels into three-dimensional(sub)blocks,and approximates the underlying true phase was by using a three-dimensional local polynomial function model.Simulation and in vivo quantitative susceptibility mapping experimental results show that the proposed method can accurately unwrap three-dimensional phase data,even in presence of serious noise,disconnected regions,rapid phase changes and open-end cutlines.(3)A novel phase imaging method by using phase jump detection and local surface fitting is presented,which works effectively in the presence of severe noise,rapid-varying phase and disconnected regions.The proposed method first segments the phase map into blocks by automatically detecting phase jumps,and then clusters the pixels near phase jumps into residual pixels.Thereafter,the proposed method sequentially performs intra-block,inter-block,and residual-pixel unwrapping using the local surface fitting approach.To address intra-block wraps,the proposed method segments each block into subblocks using the phase partition approach and then performs inter-subblock unwrapping using a block-growing approach.The phase derivative variance is used as the quality criterion to determine the region-growing path of residual pixels.The proposed method obtained accurate phase-unwrapping results in the simulation experiment with severe noise,rapid-varying phase and disconnected regions,and the mean and standard deviation of error ratio was 0.26 ± 0.07%.For 505 in vivo knee and ankle images,the total water–fat swap ratio by the proposed method was 1.78%.