Open Access Research article

Decision theory applied to image quality control in radiology

Patrícia S Lessa12*, Cristofer A Caous12, Paula R Arantes12, Edson Amaro12 and Fernando M Campello de Souza3

Author Affiliations

1 Instituto Israelita de Ensino e Pesquisa Albert Einstein, São Paulo, SP, Brasil

2 Neuroimagem Funcional, LIM 44, Universidade de São Paulo, FMUSP, São Paulo, SP, Brasil

3 Eletrônica e Sistemas, Universidade Federal de Pernambuco, Recife, PE, Brasil

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BMC Medical Informatics and Decision Making 2008, 8:51  doi:10.1186/1472-6947-8-51

Published: 13 November 2008



The present work aims at the application of the decision theory to radiological image quality control (QC) in diagnostic routine. The main problem addressed in the framework of decision theory is to accept or reject a film lot of a radiology service. The probability of each decision of a determined set of variables was obtained from the selected films.


Based on a radiology service routine a decision probability function was determined for each considered group of combination characteristics. These characteristics were related to the film quality control. These parameters were also framed in a set of 8 possibilities, resulting in 256 possible decision rules. In order to determine a general utility application function to access the decision risk, we have used a simple unique parameter called r. The payoffs chosen were: diagnostic's result (correct/incorrect), cost (high/low), and patient satisfaction (yes/no) resulting in eight possible combinations.


Depending on the value of r, more or less risk will occur related to the decision-making. The utility function was evaluated in order to determine the probability of a decision. The decision was made with patients or administrators' opinions from a radiology service center.


The model is a formal quantitative approach to make a decision related to the medical imaging quality, providing an instrument to discriminate what is really necessary to accept or reject a film or a film lot. The method presented herein can help to access the risk level of an incorrect radiological diagnosis decision.