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A Journal on Nuclear Medicine and Molecular Imaging
Affiliated to the and to the International Research Group of Immunoscintigraphy
Indexed/Abstracted in: Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index (SciSearch), Scopus
Impact Factor 2,413
Online ISSN 1827-1936
TECHNOLOGIES AND METHODS IN NUCLEAR MEDICINE
Guest Editors: Todd-Pokropek A., Gilardi M. C.
From the Department of Medical Physics University College London, London, UK
INSERM U494, Paris, France
The continuing advances in hardware performance had made many previously computationally unattractive methods feasible, an example being iterative reconstruction in tomography, which is now routine. Dynamic SPECT can also be performed. However the aim of image processing is not just to produce pretty pictures, but to extract good clinical information. The methods also need to incorporate clinical knowledge and be defined using clinical constraints. In general data in nuclear medicine are n-D, often 3-D plus time. Data reduction for example by the extraction of physiological information, is important. Such data are in any case hard to visualise without compression, for example some kind of dimensionality reduction, going from n-D to a 2-D “functional” image. Both linear and non-linear operations can be considered. To extract physiological data, we need to fit models. Two classes of method are important: data driven and hypothesis driven. Examples of data driven methods are principal component analysis and factor analysis, where the model is derived form the data. Hypothesis driven methods are all implicitly or explicitly based on model fitting. A preliminary data driven step followed by an hypothesis driven approach could be called constrained statistical image analysis. Examples are shown as used in nuclear medicine and are being extended to MRI. Another important problem considered is that of multi-modality image registration and fusion. Although many methods exist, all based on the minimisation of an appropriate distance functions between 2 image data sets such as mutual information, additional constraints are required when the images are not so similar. Additional constraints can be imposed by means of cluster analysis of the n-dimensional feature space. In the analysis of such data, tests against reference data sets (atlases) are required, normally requiring warping the data sets in space, for example by the use of optic flow, or some kind of diffusion equation. Real time analysis of data during acquisition can lead to optimisation of acquisition procedures. Incorporation of such image analysis into a decision support system is desirable.