This paper describes a software system currently being developed at Clemson University in which a client provides recently obtained data to a remote server running a compute-intensive algorithm. To improve performance and speed up delivery of the results, the server distributes the data among multiple sub-server processors and assembles partial output from each processor into a coherent whole before sending the final results to the client. To demonstrate the capabilities of the system, a specific application is presented in this paper: a fluorescence image reconstruction system for breast cancer detection. An experimental instrument optically scans the patient’s breast and generates some files of experimental data which are then sent to the server via the web. The data is processed by the numerical finite-element based algorithm running in parallel on a server and several sub-servers at Clemson. The algorithm is based on a set of coupled diffusion equations which are used to describe the propagation of excitation and fluorescent emission light in multiply scattering media (such as a breast). The algorithm reconstructs the fluorescence image of the breast in parallel. The resulting fluorescence lifetime and quantum yield mapping data can be sent back to the doctor for image display and analysis. This paper describes the numerical algorithm briefly and the software system which uses Java servlets to collect the data from the client and remote method invocation (Java RMI) to distribute the data to multiple processors. The output of the numerical algorithm, combined with the corresponding finite element mesh information, are input into a mathematical software package called Matlab which is used to produce the final images. Experiments are performed using indocyanine green (ICG) dye and tissue-like phantoms in both single- and multi-target configurations. Phantom experimental results of both lifetime and quantum yield are shown in this paper. Future work includes a refinement of the algorithm to incorporate adaptive mesh techniques. The expectation is that such techniques will improve the accuracy of the reconstructed images.

Date Published: 15 March 2004
PDF: 9 pages
Proc. SPIE 5261, Smart Medical and Biomedical Sensor Technology, (15 March 2004); doi: 10.1117/12.514799

Published in SPIE Proceedings Vol. 5261:
Smart Medical and Biomedical Sensor Technology
Brian M. Cullum, Editor(s)