Biomedical subjects

Cun-Hui Zhang

Publications and source records attributed to Cun-Hui Zhang.

3 recordsLinked to original sources

PubMed2006

A generalization of the two-dimensional prolate spheroidal wave function method for nonrectilinear MRI data acquisition methods.

The two-dimensional (2-D) prolate spheroidal wave function (2-D PSWF) method was previously introduced as an efficient method for trading off between spatial and temporal resolution in magnetic resonance imaging (MRI), with minimal penalty due to truncation and partial volume effects. In the 2-D PSWF method, the k-space sampling area and a matching 2-D PSWF filter, with optimal signal concentration and minimal truncation artifacts, are determined by the shape and size of a given convex region of interest (ROI). The spatial information in the reduced k-space data is used to calculate the total image intensity over a nonsquare ROI instead of producing a low-resolution image. This method can be used for tracking dynamic signals from non-square ROIs using a reduced k-space sampling area, while achieving minimal signal leakage. However, the previous theory is limited to the case of rectilinear sampling. In order to make the 2-D PSWF method more suitable for dynamic studies, this paper presents a generalized version of the 2-D PSWF theory that can be applied to nonrectilinear data acquisition methods. The method is applied to an fMRI study using a spiral trajectory, which illustrates the methods efficiency at tracking hemodynamic signals with high temporal resolution.

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PubMed2004

Mixed group ranks: preference and confidence in classifier combination.

Classifier combination holds the potential of improving performance by combining the results of multiple classifers. For domains with very large numbers of classes, such as biometrics, we present an axiomatic framework of desirable mathematical properties for combination functions of rank-based classifiers. This framework represents a continuum of combination rules, including the Borda Count, Logistic Regression, and Highest Rank combination methods as extreme cases [11], [23], [4], [13]. Intuitively, this framework captures how the two complementary concepts of general preference for specific classifiers and the confidence it has in any specific result (as indicated by ranks) can be balanced while maintaining consistent rank interpretation. Mixed Group Ranks (MGR) is a new combination function that balances preference and confidence by generalizing these other functions. We demonstrate that MGR is an effective combination approach by performing multiple experiments on data sets with large numbers of classes and classifiers from the FERET face recognition study.

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PubMedDate not supplied

Two dimensional prolate spheroidal wave functions for MRI.

The tradeoff between spatial and temporal resolution is often used to increase data acquisition speed for dynamic MR imaging. Reduction of the k-space sampling area, however, leads to stronger partial volume and truncation effects. A two dimensional prolate spheroidal wave function (2D-PSWF) method is developed to address these problems. Utilizing prior knowledge of a given region of interest (ROI) and the spatial resolution requirement as constraints, this method tailors the k-space sampling area with a matching 2D-PSWF filter so that optimal signal concentration and minimal truncation artifacts are achieved. The k-space sampling area is reduced because the shape and size of the sampling area match the resolution posed by the non-rectangular shape of a convex ROI. The 2D-PSWF method offers an efficient way for spatial and temporal tradeoff with minimal penalty due to truncation, and thus, it promises a wide range of applications in MRI research.

Adult↗