Neural network assisted Gamma knife radiosurgery treatment planning: Basic studies

Y. J. Tseng, W. C. Chu, Pau-Choo Chung, W. Y. Chung, D. H C Pan

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the extreme accuracy requirement of Gamma knife stereotactic radiosurgery, the treatment planning procedure has been a time consuming and enduring process for neurosurgeons to uphold. For this reason, we propose using artificial neural network to integrate traditional intricate diagnosing procedures to determine multi-focal shots, focal positions and weightings in an automatic or semi-automatic way. We use error-back-propagation neural network algorithm to train 32 phantom cases and 74 real cases acquired from the Neurological Institute of Veterans General Hospital, Taipei. After pre- processing procedure, tumor lesion data was encoded as input vector and the treatment result as the output vector of the artificial neural network. The program was constructed with an object-oriented programming concept that runs on a PC platform. Partial successes have been obtained for simple-shaped tumors, however, for complex-shaped and/or large tumors, further endeavors are required. Discussions on this are included.

Original languageEnglish
Pages (from-to)97-105
Number of pages9
JournalChinese Journal of Medical and Biological Engineering
Volume18
Issue number2
Publication statusPublished - 1998

All Science Journal Classification (ASJC) codes

  • Biophysics

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