Kinetic Characterization and Mapping for Whole-body Molecular Image Retrieval

Summary

In vivo kinetic behaviour characterization and dynamic holistic mapping for efficient dual-modality whole-body molecular imaging data retrieval.

Supervisor(s)

Professor David Feng, Associate Professor Tom Weidong Cai

Research Location

Computer Science

Program Type

N/A

Synopsis

Modern biomedical molecular imaging such as whole-body F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) imaging is being increasingly utilized to exploit in vivo specific physiological functions and biochemical pathways at the molecular level, with the expression and activity of specific molecules (e.g., proteases and protein kinases) and biological processes (e.g., apoptosis, angiogenesis, and metastasis), and has resulted in its use for characterizing lesions that are indeterminate by conventional anatomical imaging modalities, for staging the distribution of disease, and for determining the effects of therapy and prognosis.This project aims to develop molecular feature extraction approaches with PET-based kinetic behavior characterization in different activity levels and computed tomography (CT)-based dynamic mapping on entire body, separate organs, subunits of organs, or other functional units that are expected to exhibit similar physiological pattern and temporal variation, for efficient whole-body molecular imaging data retrieval via accurately quantify a variety of physiological events such as glucose metabolism, DNA synthesis and drug uptake, together with improved localization of abnormalities, and potentially paving the way for disease diagnosis, treatment selection / response, and future drug evaluation and development.

Keywords

Computer Applications, Life and medical sciences, Molecular imaging, Kinetic modeling and analysis, Physiological feature extraction, Positron emission tomography (PET), Computed tomography (CT), Content-based image retrieval, Computer assisted diagnosis (CAD)

Opportunity ID

The opportunity ID for this research opportunity is: 313

Other opportunities with Professor David Feng

• Advanced computer modelling of biological systems using insight knowledge
• Discovery of new image-derived features for computer-aided diagnosis
• Automated 3-Dimensional Biomedical Registration for Whole-body Images from Combined PET/CT Scanners - Automatic Registration for 3D Whole-body Images from Combined PET/CT Scanners
• Deformable Registration for Temporal Lung Volumes
• Multi-dimensional Biomedical Data Visualization
• Image Representation using Multi-dimensional Biomedical Functional and Anatomical Features
• Automatic Image Content Annotation
• Web Image Annotation
• Data Management for Automated Identification and Classification of Plant Images
• Novel Image Retouching Techniques
• Automatic Video Content Annotation
• Intelligent Access to Digital TV Content
• Semantic Multimedia Information Retrieval
• Multimedia Streaming with Peer-to-Peer Techniques
• Intelligent Multimodality Molecular Image Segmentation
• Multimodality Medical Image Segmentation
• Medical Image Mining for Computer-Aided Diagnosis
• Functional Brain Image Understanding for Differential Diagnosis of Dementia
• Disease Map - Big Data driven modelling and derivation of diseases and treatment response
• Machine learning in Multiscale Image-Omics
• Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data
• Object-based Volumetric Texture Feature Extraction for Biomedical Image Retrieval
• Semantic-driven Multi-modal Biomedical Data Visualisation
• Computerised image analysis of musculoskeletal diagnostics and surgical planning

Other opportunities with Associate Professor Tom Weidong Cai

• Content-based Retrieval and Management of Multi-dimensional Biomedical Imaging Data
• Object-based Volumetric Texture Feature Extraction for Biomedical Image Retrieval
• Semantic Feature Description and Similarity Measurement for Large-scale Image Search
• Neuroimaging Computing for Early Detection of Alzheimer’s Disease
• Feature-Centric Content Analysis for Bioimaging Informatics
• Visual Feature Extraction for Image Pattern Classification