Micro Insert: A Prototype Full-Ring PET Device for Improving the Image Resolution of a Small-Animal PET Scanner

A full-ring PET insert device should be able to enhance the image resolution of existing small-animal PET scanners. Methods: The device consists of 18 high-resolution PET detectors in a cylindric enclosure. Each detector contains a cerium-doped lutetium oxyorthosilicate array (12 x 12 crystals, 0.72 x 1.51 x 3.75 mm each) coupled to a position-sensitive photomultiplier tube via an optical fiber bundle made of 8 x 16 square multiclad fibers. Signals from the insert detectors are connected to the scanner through the electronics of the disabled first ring of detectors, which permits coincidence detection between the 2 systems. Energy resolution of a detector was measured using a Ge-68 point source, and a calibrated 68Ge point source stepped across the axial field of view (FOV) provided the sensitivity profile of the system. A Na-22 point source imaged at different offsets from the center characterized the in-plane resolution of the insert system. Imaging was then performed with a Derenzo phantom filled with 19.5 MBq of F-18-fluoride and imaged for 2 h; a 24.3-g mouse injected with 129.5 MBq of F-18-fluoride and imaged in 5 bed positions at 3.5 h after injection; and a 22.8-g mouse injected with 14.3 MBq of F-18-FDG and imaged for 2 h with electrocardiogram gating. Results: The energy resolution of a typical detector module at 511 keV is 19.0% +/- 3.1 %. The peak sensitivity of the system is approximately 2.67%. The image resolution of the system ranges from 1.0- to 1.8-mm full width at half maximum near the center of the FOV, depending on the type of coincidence events used for image reconstruction. Derenzo phantom and mouse bone images showed significant improvement in transaxial image resolution using the insert device. Mouse heart images demonstrated the gated imaging capability of the device. Conclusion: We have built a prototype full-ring insert device for a small-animal PET scanner to provide higher-resolution PET images within a reduced imaging FOV. Development of additional correction techniques are needed to achieve quantitative imaging with such an insert.

低能He~(2+)入射He原子转移电离实验中出射电子成像研究；Momentum image of emission electrons in transfer ionization process of slow He~(2+) colliding on He

利用反应显微谱仪对70keV He2+-He转移电离过程中的出射电子进行了成像,研究了出射电子的空间速度分布特征.结果表明:电子主要集中在散射平面内;在散射平面内,电子速度分布介于零与入射离子速度Vp之间(即前向出射)且在散射离子和靶核核间轴处有一极小值,呈现出典型的双峰结构.出射电子的上述分布特征可由出射电子波函数σ振幅和π振幅的干涉进行定性解释,σ振幅和π振幅对出射电子波函数的贡献与碰撞参数相关.在小碰撞参数下,π振幅的贡献更加明显;而在大碰撞参数下,σ振幅的贡献更加显著.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

DNA diagnosis by capillary electrophoresis and microfabricated electrophoretic devices

DNA diagnosis is experiencing an impressive progression towards the development of novel technology to identity various clinically relevant categories of genetic changes and to meet the exponential growth of genomics. The introduction of capillary electrophoresis has dramatically accelerated the completion of the first draft of the human DNA sequence in the Human Genome Project, and thus, has become the method of choice for analysis of various genetic variants. The recent development of microfabricated electrophoretic devices has led to the possibility of integrating multiple sample handling with the actual measurement steps required for automation of molecular diagnostics. This review highlights the most recent progress in capillary electrophoresis and electrophoretic microdevices for DNA-based diagnostics, including the important areas of genotyping for point mutation, single nucleotide polymorphisms, short tandem repeats and organism identification. The application of these techniques for infectious and genetic disease diagnosis, as well as forensic identification purpose, are covered. The promising development and the challenges for techinical problems are also discussed.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Local histogram based geometric invariant image watermarking

Compared with other existing methods, the feature point-based image watermarking schemes can resist to global geometric attacks and local geometric attacks, especially cropping and random bending attacks (RBAs), by binding watermark synchronization with salient image characteristics. However, the watermark detection rate remains low in the current feature point-based watermarking schemes. The main reason is that both of feature point extraction and watermark embedding are more or less related to the pixel position, which is seriously distorted by the interpolation error and the shift problem during geometric attacks. In view of these facts, this paper proposes a geometrically robust image watermarking scheme based on local histogram. Our scheme mainly consists of three components: (1) feature points extraction and local circular regions (LCRs) construction are conducted by using Harris-Laplace detector; (2) a mechanism of grapy theoretical clustering-based feature selection is used to choose a set of non-overlapped LCRs, then geometrically invariant LCRs are completely formed through dominant orientation normalization; and (3) the histogram and mean statistically independent of the pixel position are calculated over the selected LCRs and utilized to embed watermarks. Experimental results demonstrate that the proposed scheme can provide sufficient robustness against geometric attacks as well as common image processing operations. (C) 2010 Elsevier B.V. All rights reserved.

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.