Qing-San Xiang

Correction for geometric distortion and N/2 ghosting in EPI by phase labeling for additional coordinate encoding (PLACE).

Echo‐planar imaging (EPI) is vulnerable to geometric distortion and N/2 ghosting. These artifacts can be analyzed with an intuitive k‐t space tool, and here we propose a simple method for their correction. In a slightly modified additional EPI acquisition, we sample the k‐t space with a shift in ky by adding a small area to the phase‐encoding (PE) gradient. Physically, the added gradient area creates a relative phase ramp across the object and directly encodes the undistorted original y‐coordinate of each voxel into a phase difference between two distorted complex images, in a method called “phase labeling for additional coordinate encoding” (PLACE). The phase information is then used to map the mismapped signals back to their original locations for geometric and intensity correction. Smoothing of expanded complex data matrix effectively reduces noise in the differential phase map and allows subpixel warping. The two acquired images can also be averaged to effectively suppress the N/2 ghost. Efficient correction for both artifacts can be achieved with three acquisitions. These acquisitions can also serve as reference scans to correct for geometric distortion and/or N/2 ghost artifacts on all images in a time series. The technique was successfully demonstrated in phantom and animal studies. Magn Reson Med 57:731–741, 2007.

Two-point water-fat imaging with partially-opposed- phase (POP) acquisition : An asymmetric dixon method

A novel two‐point water‐fat imaging method is introduced. In addition to the in‐phase acquisition, water and fat magnetization vectors are sampled at partially‐opposed‐phase (POP) rather than exactly antiparallel as in the original Dixon method. This asymmetric sampling encodes more valuable phase information for identifying water and fat. From the magnitudes of the two complex images, a big and a small chemical component are first robustly obtained pixel by pixel and then used to form two possible error phasor candidates. The true error phasor is extracted from the two error phasor candidates through a simple procedure of regional iterative phasor extraction (RIPE). Finally, least‐squares solutions of water and fat are obtained after the extracted error phasor is smoothed and removed from the complex images. For noise behavior, the effective number of signal averages NSA* is typically in the range of 1.87–1.96, very close to the maximum possible value of 2. Compared to earlier approaches, the proposed method is more efficient in data acquisition and straightforward in processing, and the final results are more robust. At both 1.5T and 0.3T, well separated and identified in vivo water and fat images covering a broad range of anatomical regions have been obtained, supporting the clinical utility of the method. Magn Reson Med, 2006.

Quantitative evaluation of metal artifact reduction techniques

To develop a technique to quantify artifact, and to use it to compare the effectiveness of several approaches to metal artifact reduction, including view angle tilting and increasing the slice select and image bandwidths (BWs), in terms of metal artifact reduction, noise, and blur.

Chemical shift imaging with spectrum modeling

A new chemical shift imaging technique was developed to efficiently obtain separate images for multiple chemical shift peaks from a set of spin‐echo acquisitions. Information from localized NMR spectroscopy was used to model the chemical shift spectrum as sharp peaks with known resonance frequencies but unknown amplitudes. Based on this model, a set of spin‐echo images with shifted 180° RF pulses were acquired, in which the magnetization vectors of different chemical components were put into different directions. The amplitudes of the chemical shift peaks were obtained by solving nonlinear equations in a region‐growing process. Experimental results on an ethanol phantom as well as a subject with silicone breast implants are presented. Magn Reson Med 46:126–130, 2001.

Accelerating MRI by skipped phase encoding and edge deghosting (SPEED)

A fast imaging method called skipped phase encoding and edge deghosting (SPEED) is introduced. The k‐space is sparsely sampled into three interleaved datasets, each with a skip‐size N and a relative shift in phase encoding (PE). These datasets are separately reconstructed by 2DFT and edge‐enhanced by a differential filter in the PE direction, resulting in edge maps with phase‐shifted aliasing ghosts. The sparseness of edges reduces the chance of ghost overlapping. Typical ghosted‐edge maps can be adequately modeled with only two dominating ghost layers that are resolved from a set of three equations using least‐square error minimization, yielding N ghost maps of different orders that can be registered and averaged into a single deghosted‐edge map for noise and artifact reduction. Finally, the deghosted‐edge map is transformed into a deghosted image by an inverse filter. A few central k‐space lines are collected without PE skip to aid the inverse filtering. SPEED has been demonstrated by in vivo data to reduce scan time considerably without noticeable artifacts. It has various potential applications, such as MR angiography (MRA), where the signal itself is sparse. As an independent method, SPEED can be combined with other fast imaging methods for further acceleration. Magn Reson Med 53:1112–1117, 2005.

Highly accelerated MRI by skipped phase encoding and edge deghosting with array coil enhancement (SPEED‐ACE)

The fast MRI method of skipped phase encoding and edge deghosting (SPEED) is further developed with array coil enhancement, and thus is termed SPEED-ACE. In SPEED-ACE, k space is sparsely sampled with skipped phase encoding at every Nth step using a set of receiver coils simultaneously, similar to SENSE, leading to sensitivity-weighted images with up to N layers of overlapping aliasing ghosts. The ghosted images are edge enhanced by a differential filter to yield ghosted edge maps, in which the ghost overlapping layers are greatly reduced since the sparseness of edges reduces the chance of ghost overlapping. Typical ghosted edge maps can be adequately modeled with a double-layer structure. By using data from at least three coils through least-square-error minimization, a deghosted edge map is obtained and inverse-filtered into a final deghosted image. In this way, SPEED-ACE partially samples k space with a skip size of N by using multiple receiver coils in parallel, and obtains a fairly good deghosted image with an undersampling factor of N. SPEED-ACE is not limited to the double-layer ghost model, but can be generalized to include more layers of ghosts for more flexible and improved performance. As a new parallel imaging method, SPEED-ACE was tested using in vivo data to demonstrate the possibility of achieving undersampling factors even greater than the number of receiver coils, which is so far not achievable by other parallel imaging methods.

Nonlinear phase correction with an extended statistical algorithm

This paper presents a new magnetic resonance imaging (MRI) phase correction method. The linear phase correction method using autocorrelation proposed by Ahn and Cho (AC method) is extended to handle nonlinear terms, which are often important for polynomial expansion of phase variation in MRI. The polynomial coefficients are statistically determined from a cascade series of n-pixel-shift rotational differential fields (RDFs). The n-pixel-shift RDF represents local vector rotations of a complex field relative to itself after being shifted by n pixels. We have found that increasing the shift enhances the signal significantly and extends the AC method to handle higher order nonlinear phase error terms. The n-pixel-shift RDF can also be applied to improve other methods such as the weighted least squares phase unwrapping method proposed by Liang. The feasibility of the method has been demonstrated with two-dimensional (2-D) in vivo inversion-recovery MRI data.

Accelerating non‐contrast‐enhanced MR angiography with inflow inversion recovery imaging by skipped phase encoding and edge deghosting (SPEED)

To accelerate non‐contrast‐enhanced MR angiography (MRA) with inflow inversion recovery (IFIR) with a fast imaging method, Skipped Phase Encoding and Edge Deghosting (SPEED).

Simplified skipped phase encoding and edge deghosting (SPEED) for imaging sparse objects with applications to MRA

The fast imaging method named skipped phase encoding and edge deghosting (SPEED) has been demonstrated to reduce scan time considerably with typical magnetic resonance imaging data. In this work, SPEED is simplified with improved efficiency to accelerate the scan of sparse objects; we refer to this method as S-SPEED. S-SPEED partially samples k-space into two interleaved data sets, each with the same skip size of N but a different relative shift in phase encoding. The sampled data are then Fourier transformed into two ghosted images with N aliasing ghosts. Given the sparseness of signal distribution, the ghosted images are simply modeled with a single-layer structure, analogous to that used in maximum-intensity projection. With an algorithm based on a least-square-error solution, a deghosted image is solved, and a residual map is output for quality control. S-SPEED can be generalized to include more layers with additional acquisitions for refined results. Without differential filtering and full central k-space sampling, S-SPEED reduces scan time further and achieves more straightforward reconstruction, as compared with SPEED. In this work, S-SPEED is applied to accelerate magnetic resonance angiography (MRA) by taking advantage of the sparse nature of MRA data. With sparse phantom data and in vivo phase contrast MRA data, S-SPEED is demonstrated to achieve satisfactory results with an acceleration factor of 5.5 using a single coil.

Efficient multiple acquisitions by skipped phase encoding and edge deghosting (SPEED) using shared spatial information

The fast MRI method of Skipped Phase Encoding and Edge Deghosting (SPEED) is further developed to accelerate multiple acquisitions. In a single acquisition, SPEED first acquires three sparse ghosted edge maps with an undersampling factor of N/3, which are modeled with a double‐layer structure and described by three equations with two unknown ghosts, each with a unique ghost order index. By minimizing least‐square‐error, a pair of ghost order indexes is determined. Based on them, the two corresponding ghosts are resolved, leading to a deghosted image. In this case, three equations are needed to determine the ghost order index, while only two equations are required to resolve the two ghosts. This shows both inefficiency and potential. Multiple acquisitions often contain similar spatial information. The similarities can be used to improve efficiency by sharing the ghost order index among different acquisitions, leading to acceleration factors greater than that achievable with single acquisition. Magn Reson Med 61:229–233, 2009.