Molecular Imaging in Tumor Angiogenesis and Relevant Drug Research

Molecular imaging, including fluorescence imaging (FMI), bioluminescence imaging (BLI), positron emission tomography (PET), single-photon emission-computed tomography (SPECT), and computed tomography (CT), has a pivotal role in the process of tumor and relevant drug research. CT, especially Micro-CT, can provide the anatomic information for a region of interest (ROI); PET and SPECT can provide functional information for the ROI. BLI and FMI can provide optical information for an ROI. Tumor angiogenesis and relevant drug development is a lengthy, high-risk, and costly process, in which a novel drug needs about 10–15 years of testing to obtain Federal Drug Association (FDA) approval. Molecular imaging can enhance the development process by understanding the tumor mechanisms and drug activity. In this paper, we focus on tumor angiogenesis, and we review the characteristics of molecular imaging modalities and their applications in tumor angiogenesis and relevant drug research

Cone Beam Micro-CT System for Small Animal Imaging and Performance Evaluation

A prototype cone-beam micro-CT system for small animal imaging has been developed by our group recently, which consists of a microfocus X-ray source, a three-dimensional programmable stage with object holder, and a flat-panel X-ray detector. It has a large field of view (FOV), which can acquire the whole body imaging of a normal-size mouse in a single scan which usually takes about several minutes or tens of minutes. FDK method is adopted for 3D reconstruction with Graphics Processing Unit (GPU) acceleration. In order to reconstruct images with high spatial resolution and low artifacts, raw data preprocessing and geometry calibration are implemented before reconstruction. A method which utilizes a wire phantom to estimate the residual horizontal offset of the detector is proposed, and 1D point spread function is used to assess the performance of geometric calibration quantitatively. System spatial resolution, image uniformity and noise, and low contrast resolution have been studied. Mouse images with and without contrast agent are illuminated in this paper. Experimental results show that the system is suitable for small animal imaging and is adequate to provide high-resolution anatomic information for bioluminescence tomography to build a dual modality system

Automatic mapping aquaculture in coastal zone from TM imagery with OBIA approach

IEEE GRSS; The Geographical Society of China<span class="MedBlackText">Aquaculture area monitoring is of great importance for coastal zone sustainable management and planning. This paper focuses on the development and assessment of an automatic approach for aquaculture mapping in coastal zone from TM imagery. The contribution mainly consists of three aspects: first, utilizes the Multi-scale segmentation/object relationship modeling (MSS/ORM) strategy on the object based image analysis (OBIA) of TM imagery; second, evaluates the effectiveness GLCM homogeneity texture feature on pond aquaculture area information extraction; third, compares the analysis results from three different approaches, namely pixelbased maximum likelihood classifier (MLC), One-step supervised OBIA with stand nearest neighbor (SNN) and MSS/ORM OBIA strategy. The final result shows that the MSS/ORM OBIA approach greatly improves the classification accuracy and has good potential for automatic pond aquaculture land mapping in coastal zone from TM imagery.</span

Cerenkov Luminescence Tomography for In Vivo Radiopharmaceutical Imaging

Cerenkov luminescence imaging (CLI) is a cost-effective molecular imaging tool for biomedical applications of radiotracers. The introduction of Cerenkov luminescence tomography (CLT) relative to planar CLI can be compared to the development of X-ray CT based on radiography. With CLT, quantitative and localized analysis of a radiopharmaceutical distribution becomes feasible. In this contribution, a feasibility study of in vivo radiopharmaceutical imaging in heterogeneous medium is presented. Coupled with a multimodal in vivo imaging system, this CLT reconstruction method allows precise anatomical registration of the positron probe in heterogeneous tissues and facilitates the more widespread application of radiotracers. Source distribution inside the small animal is obtained from CLT reconstruction. The experimental results demonstrated that CLT can be employed as an available in vivo tomographic imaging of charged particle emitters in a heterogeneous medium

Differentiation of Soil Conditions over Low Relief Areas Using Feedback Dynamic Patterns

In many areas, such as plains and gently undulating terrain, easy-to-measure soil-forming factors such as landform and vegetation do not co-vary with soil conditions across space to the level that they can be effectively used in digital soil mapping. A challenging problem is how to develop a new environmental variable that co-varies with soil spatial variation under these situations. This study examined the idea that change patterns (dynamic feedback patterns) of the land surface, such as those captured daily by remote sensing images during a short period (6-7 d) after a major rain event, can be used to differentiate soil types. To examine this idea, we selected two study areas with different climates: one in northeastern China and the other in northwestern China. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to capture land surface feedback. To measure feedback dynamics, we used spectral information divergence (SID). Results of an independent-samples t-test showed that there was a significant difference in SID values between pixel pairs of the same soil subgroup and those of different subgroups. This indicated that areas with different soil types (subgroup level) exhibited significantly different dynamic feedback patterns, and areas within the same soil type have similar dynamic feedback patterns. It was also found that the more similar the soil types, the more similar the feedback patterns. These findings could lead to the development of a new environmental covariate that could be used to improve the accuracy of soil snapping in low-relief areas

Differentiation of Soil Conditions over Low Relief Areas Using Feedback Dynamic Patterns

In many areas, such as plains and gently undulating terrain, easy-to-measure soil-firming factors such as landform and vegetation do not co-vary with soil conditions across space to the level that they can be effectively used in digital soil mapping. A challenging problem is how to develop a new environmental variable that co-varies with soil spatial variation under these situations. This study examined the idea that change patterns (dynamic feedback patterns) of the land surface, such as those captured daily by remote sensing images during a short period (6-7 d) after a major rain event, can be used to differentiate soil types. To examine this idea, we selected two study areas with different climates: one in northeastern China and the other in northwestern China. Images from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to capture land surface feedback, To measure feedback dynamics, we used spectral information divergence (SID). Results of an independent-samples t-test showed that there was a significant difference in SID values between pixel pairs of the same soil subgroup and those of different subgroups. This indicated that areas with different soil types (subgroup level) exhibited significantly different dynamic feedback patterns, and areas within the same soil type have similar dynamic feedback patterns. It was also found that the more similar the soil types, the more similar the feedback patterns. These findings could lead to the development of a new environmental covariate that could be used to improve the accuracy of soil mapping in low-relief areas. © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved

Detecting feature from spatial point processes using collective nearest-neighbor

a b s t r a c t In a spatial point set, clustering patterns (features) are difficult to locate due to the presence of noise. Previous methods, either using grid-based method or distance-based method to separate feature from noise, suffer from the parameter choice problem, which may produce different point patterns in terms of shape and area. This paper presents the Collective Nearest Neighbor method (CLNN) to identify features. CLNN assumes that in spatial data clustered points and noise can be viewed as two homogenous point processes. The one with the higher intensity is considered as a feature and the one with the lower intensity is treated as noise. As a result, they can be separated according to the difference in intensity between them. With CLNN, points are first classified into feature and noise based on the kth nearest distance (the distance between a point and its kth nearest neighbor) at various values of k. Then, CLNN selects those classifications in which the separated classes (i.e. features and noise) are homogenous Poisson processes and cannot be further divided. Finally, CLNN identifies clustered points by averaging the selected classifications. Evaluation of CLNN using simulated data shows that CLNN reduces the number of false points significantly. The comparison between CLNN, the shared nearest neighbor, the spatial scan and the classification entropy method shows that CLNN produced the fewest false points. A case study using seismic data in southwestern China showed that CLNN is able to identify foreshocks of the Songpan earthquake (M = 7.2), which may help to locate the epicenter of the Songpan earthquake