Information technology applications in biomedical functional imaging

Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringVersion of RecordPublishe

Fast and reliable estimation of multiple parametric images using an integrated method for dynamic SPECT

Author name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Adaptive fuzzy clustering in constructing parametric images for low SNR functional imaging

Author name used in this publication: Michael FulhamAuthor name used in this publication: Dagan FengRefereed conference paper2008-2009 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Constructing reliable parametric images using enhanced GLLS for dynamic SPECT

Author name used in this publication: (David) Dagan Feng2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Dynamic image data compression in spatial and temporal domains : theory and algorithm

Author name used in this publication: Dagan FengVersion of RecordPublishe

Adaptive fuzzy clustering in constructing parametric images for low SNR functional imaging

Functional imaging can provide quantitative functional parameters to aid early diagnosis. Low signal to noise ratio (SNR) in functional imaging, especially for single photon emission computed tomography, poses a challenge in generating voxel-wise parametric images due to unreliable or physiologically meaningless parameter estimates. Our aim was to systematically investigate the performance of our recently proposed adaptive fuzzy clustering (AFC) technique, which applies standard fuzzy clustering to sub-divided data. Monte Carlo simulations were performed to generate noisy dynamic SPECT data with quantitative analysis for the fitting using the general linear least square method (GLLS) and enhanced model-aided GLLS methods. The results show that AFC substantially improves computational efficiency and obtains improved reliability as standard fuzzy clustering in estimating parametric images but is prone to slight underestimation. Normalization of tissue time activity curves may lead to severe overestimation for small structures when AFC is applied.Department of Electronic and Information EngineeringAuthor name used in this publication: Michael FulhamAuthor name used in this publication: Dagan FengRefereed conference pape

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography

Introduction: Dynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition. Methods: A new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography. The phantom is based on high spatial and temporal information derived from real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. Results: The proposed phantom is comprised of three spatial and two temporal dimensions, including periodic physiological processes due to respiratory motion and non-periodic functional processes due to tracer kinetics. Example applications are shown in parametric [18F]FDG and [15O]H2O PET imaging, successfully generating realistic macro- and micro-parametric maps. Conclusions: The envisaged applications of this digital phantom include the development and evaluation of motion correction and 4D image reconstruction algorithms in PET and SPECT, development of protocols and methods for tracer and drug development as well as new pharmacokinetic parameter estimation algorithms, amongst others. Although the simulation platform is primarily developed for generating dynamic phantoms for emission tomography studies, it can easily be extended to accommodate dynamic MR and CT imaging simulation protocols