Probabilistic Constraint Satisfaction: Application to Radiosurgery

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Citation: Russ B. Altman and Rhea Tombropoulos. (1994) Probabilistic Constraint Satisfaction: Application to Radiosurgery. In KSL-94-35, 1994.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Russ B. Altman and Rhea Tombropoulos
title Probabilistic Constraint Satisfaction: Application to Radiosurgery
number KSL-94-35
institution Knowledge Systems, AI Laboratory
address Stanford, CA, USA
year 1994
Bibtex more
note Updated November 1994. Medical Computer Science
Access Paper
abstract Although quite successful in a variety of settings, standard optimization approaches can have drawbacks within medical applications. For example,they often provide a single solution which is difficult to explain, or which can not be incrementally modified using secondary "soft" constraints that are difficult to encode within the optimization. In order to address these issues, we have developed a probabilistic optimization technique that allows the user to enter prior probability distributions (Gaussian) for the parameters to be optimized as well as for the constraints on the parameters. Our technique combines the prior distributions with the constraints using Bayes' rule. The algorithm produces not only a set of parameter values, but variances on these values and covariances showing the correlations between parameters. We have applied this method to the problem of planning a radiosurgical ablation of brain tumors. The radiation plan should maximize dose to tumor, minimize dose to surrounding areas, and provide an even distribution of dosage across the tumor. It also should be explainable to and modifiable by the expert physicians based on external considerations. We have compared the results of our method with the standard linear programming approach.

KSL Technical Report ID: KSL-94-35
Facts about Probabilistic Constraint Satisfaction: Application to RadiosurgeryRDF feed
Abstract Although quite successful in a variety of … Although quite successful in a variety of settings, standard optimization approaches can have drawbacks within medical applications. For example,they often provide a single solution which is difficult to explain, or which can not be incrementally modified using secondary "soft" constraints that are difficult to encode within the optimization. In order to address these issues, we have developed a probabilistic optimization technique that allows the user to enter prior probability distributions (Gaussian) for the parameters to be optimized as well as for the constraints on the parameters. Our technique combines the prior distributions with the constraints using Bayes' rule. The algorithm produces not only a set of parameter values, but variances on these values and covariances showing the correlations between parameters. We have applied this method to the problem of planning a radiosurgical ablation of brain tumors. The radiation plan should maximize dose to tumor, minimize dose to surrounding areas, and provide an even distribution of dosage across the tumor. It also should be explainable to and modifiable by the expert physicians based on external considerations. We have compared the results of our method with the standard linear programming approach. the standard linear programming approach.
Address Stanford, CA, USA  +
Author Russ B. Altman and Rhea Tombropoulos  +
Bibtype techreport  +
Has author Russ B. Altman and Rhea Tombropoulos  +
Has identifier KSL-94-35  +
Has publishing details 1994  +
Has title Probabilistic Constraint Satisfaction: Application to Radiosurgery  +
Has where published KSL-94-35  +
Has year 1994  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-94-35  +
Note Updated November 1994. Medical Computer Science
Number KSL-94-35  +
Process note NO  +
Title Probabilistic Constraint Satisfaction: Application to Radiosurgery  +
Year 1994  +
Retrieved from "http://tw.rpi.edu/wiki/Probabilistic_Constraint_Satisfaction:_Application_to_Radiosurgery"
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