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Zhi-Pei Liang

Publications and source records attributed to Zhi-Pei Liang.

7 recordsLinked to original sources
PubMed2006

Truncation effects in SENSE reconstruction.

Finite sampling is an important practical issue in Fourier imaging systems. Although data truncation effects are well understood in conventional Fourier imaging where a single uniform receiver channel is used for data acquisition, this issue is not yet fully addressed in parallel imaging where an array of nonuniform receiver channels is used for sensitivity encoding to enable sub-Nyquist sampling of k-space. This article presents a systematic analysis of the problem by comparing the truncation effects in parallel imaging with those in conventional Fourier imaging. Specifically, it derives a convolution kernel function to characterize the truncation effects, which is shown to be approximately equal to that associated with the conventional Fourier imaging scheme. This article also describes a set of conditions under which significant differences between the truncation effects in parallel imaging and conventional Fourier imaging occur. The results should provide useful insight into interpreting and reducing data truncation effects in parallel imaging.

Fourier Analysis↗
PubMed2006

Unwrapping of MR phase images using a Markov random field model.

Phase unwrapping is an important problem in many magnetic resonance imaging applications, such as field mapping and flow imaging. The challenge in two-dimensional phase unwrapping lies in distinguishing jumps due to phase wrapping from those due to noise and/or abrupt variations in the actual function. This paper addresses this problem using a Markov random field to model the true phase function, whose parameters are determined by maximizing the a posteriori probability. To reduce the computational complexity of the optimization procedure, an efficient algorithm is also proposed for parameter estimation using a series of dynamic programming connected by the iterated conditional modes. The proposed method has been tested with both simulated and experimental data, yielding better results than some of the state-of-the-art method (e.g., the popular least-squares method) in handling noisy phase images with rapid phase variations.

Algorithms↗
PubMed2005

Application of perceptual difference model on regularization techniques of parallel MR imaging.

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or to improve image SNR. The quality of reconstructed images is limited by the inaccurate estimation of the sensitivity map, noise in the acquired k-space data and the ill-conditioned nature of the coefficient matrix. Tikhonov regularization is a popular method to reduce or eliminate the ill-conditioned nature of the problem. In this approach, selection of the regularization map and the regularization parameter is very important. Perceptual difference model (PDM) is a quantitative image quality evaluation tool that has been successfully applied to varieties of MR applications. High correlation between the human rating and PDM score shows that PDM should be suitable to evaluate image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting the regularization parameter. We found that a regularization map obtained using generalized series (GS) together with a spatially adaptive regularization parameter gave the best reconstructions. PDM was also used as an objective function for optimizing two important parameters in the spatially adaptive method. We conclude that PDM enables one to do comprehensive experiments and that it is an effective tool for designing and optimizing reconstruction methods in parallel MR imaging.

Brain Mapping↗
PubMed2003

Improving spatiotemporal resolution of USPIO-enhanced dynamic imaging of rat kidneys.

This paper addresses the problem of enhancing spatiotemporal resolution of ultra-small superparamagnetic iron oxide (USPIO)-enhanced dynamic MRI of rat kidneys. To alleviate the limited resolution problem of conventional full-scan Fourier imaging methods, we use a generalized series-based imaging scheme to reduce coverage of kappa-space. Experimental results demonstrate that the generalized series imaging method with basis functions constructed using two references (pre- and post-contrast) can reduce the number of phase encodings measured during the dynamic contrast wash-in process by a factor of 4 with a negligible or minimal loss of image quality. The method is expected to make 3D studies possible using USPIO-enhanced dynamic imaging of rat kidneys, and prove valuable for early detection of renal rejection after kidney transplantation.

Animals↗
PubMed2003

Fast algorithms for GS-model-based image reconstruction in data-sharing Fourier imaging.

Many imaging experiments involve acquiring a time series of images. To improve imaging speed, several "data-sharing" methods have been proposed, which collect one (or a few) high-resolution reference(s) and a sequence of reduced data sets. In image reconstruction, two methods, known as "Keyhole" and reduced-encoding imaging by generalized-series reconstruction (RIGR), have been used. Keyhole fills in the unmeasured high-frequency data simply with those from the reference data set(s), whereas RIGR recovers the unmeasured data using a generalized series (GS) model, of which the basis functions are constructed based on the reference image(s). This correspondence presents a fast algorithm (and two extensions) for GS-based image reconstruction. The proposed algorithms have the same computational complexity as the Keyhole algorithm, but are more capable of capturing high-resolution dynamic signal changes.

Algorithms↗
PubMed2003

Further analysis of interpolation effects in mutual information-based image registration.

This paper presents an analysis of the mutual information (MI) metric in rigid-body registration of two digital images, in particular, local fluctuations of the MI value due to interpolation. In contrast to existing work in this area, this paper starts with two hypothetical continuous images, based on which both sampling and interpolation effects are analyzed. This analysis indicates that an "ideal" interpolator may not be able to completely suppress the undesirable local minima of the MI metric if the sampling effect is not negligible. Several preprocessing methods are discussed for reducing the interpolation effects.

Algorithms↗