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Optimization of MR protocols: A statistical decision analysis approach
Author(s) -
McVeigh Elliot R.,
Bronskill Michael J.,
Henkelman R. Mark
Publication year - 1988
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910060310
Subject(s) - computer science , data acquisition , signal (programming language) , protocol (science) , noise (video) , statistical model , signal processing , estimation theory , signal to noise ratio (imaging) , artificial intelligence , pattern recognition (psychology) , data mining , algorithm , image (mathematics) , digital signal processing , pathology , medicine , telecommunications , alternative medicine , computer hardware , programming language , operating system
A new method of optimizing MRI data acquisition protocols is presented. Tissues are modeled with probability density functions (PDFs) of tissue parameter values (such as T 1 , T 2 ). The imaging data acquisition process is modeled as a mapping from a tissue parameter space to a signal strength space. Tissue parameter PDFs are mapped to signal strength PDFs for each tissue in a clinical problem. The efficacy of an MRI protocol is evaluated using the methods of statistical decision analysis applied to the signal strength PDFs, including the propagation of noise. This procedure evaluates the ability to discriminate different tissues based on the signal strengths produced with the protocol. The model can incorporate an arbitrary number of tissues, parameters, and pulse sequences in the protocol. The multivariate nature of MRI and the observed broad distribution of tissue parameter values makes this model more appropriate for optimizing data acquisition protocols than methods which maximize the signal‐difference‐to‐noise ratio between discrete values of the tissue parameters. It is shown that these two methods may calculate different optimal protocols. The method can be used to optimize data acquisition for quantitative computer‐based tissue classification, as well as imaging. Data acquisition and image processing philosophies are discussed in light of the method. © 1988 Academic Press, Inc.