Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) method that can provide direct and quantitative measurements of cerebral blood flow (CBF) of scanned patients. ASL can be utilized as an imaging modality to detect Alzheimer's disease (AD), as brain atrophy of AD patients can be revealed by low CBF values in certain brain regions. However, partial volume effects (PVE), which is mainly caused by signal cross-contamination due to voxel heterogeneity and limited spatial resolution of ASL images, often prevents CBF in ASL from being precisely measured. In this study, a novel PVE correction method is proposed based on pixel-wise voxels in ASL images; it can well handle with the existing problems of blurring and loss of brain details in conventional PVE correction methods. Dozens of comparison experiments and statistical analysis also suggest that the proposed method is superior to other PVE correction methods in AD diagnosis based on real patients data.

A fast algorithm to speed up the search process of vector quantization encoding is presented. Using the sum and the partial norms of a vector, some eliminating inequalities are constructeded. First the inequality based on the sum is used for determining the bounds of searching candidate codeword. Then, using an inequality based on subvector norm and another inequality combining the partial distance with subvector norm, more unnecessary codewords are eliminated without the full distance calculation. The proposed algorithm can reject a lot of codewords, while introducing no extra distortion compared to the conventional full search algorithm. Experimental results show that the proposed algorithm outperforms the existing state-of-the-art search algorithms in reducing the computational complexity and the number of distortion calculation.

A simple and successful design of initial codebook of vector quantization (VQ) is presented. For existing initial codebook algorithms, such as random method, the initial codebook is strongly influenced by selection of initial codewords and difficult to match with the features of the training vectors. In the proposed method, training vectors are sorted according to the norm of training vectors. Then, the ordered vectors are partitioned into N groups where N is the size of codebook. The initial codewords are obtained from calculating the centroid of each group. This initializtion method has a robust performance and can be combined with the VQ algorithm to further improve the quality of codebook.