| EPSRC Reference: |
GR/K92696/01 |
| Title: |
THE APPLICATION OF NEURAL NETWORKS TO THE FACTOR ANALYSIS OF DYNAMIC STRUCTURES PROBLEM IN NUCLEAR MEDICINE |
| Principal Investigator: |
Boyce, Dr J |
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| Department: |
Physics |
| Organisation: |
Kings College London |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 January 1997 |
Ends: |
31 December 1999 |
Value (£): |
127,398
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Summary on Grant Application Form |
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The question will address two complementary questions:(a) How many networks can be used to improve the various stages of existing FADS methods; and(b) What is the optimal manner of combining constraints in image space, temporal space and feature space in order to restrict the domain possible solutions?Firstly, the use of an unsupervised neural network, such as a Kohonen Network, in cluster analysis. Such networks have been demonstrated to perform well in difficult, noisy clustering problems, of which FADs is an example. Secondly, the spatial and temporal information used to constrain the FADS process will be quantified and represented to the FADS technique by training a supervised neural network such as a Multi Layer Perceptron on a set of test data obtained by a kidney phantom. Two principal applications of neural networks to enhance the performance of FADS will be considered.Spatial constraints require a definition of the spatial extent of the structure. A constraint on the behaviour of blood background in the region of the structures is also required. By applying cluster analysis it is possible to define pixels with similar contributions from the underlying structures. Temporal constraints require consistency of interpretation within a sequence.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
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| Description |
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Sectors submitted by the Researcher |
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