Optimization of MR protocols: A statistical decision analysis approach

Abstract

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 T1, T2). 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.

Spin locking for magnetic resonance imaging with application to human breast

“…This study confirms that water in agarose gels and tissues can be described by a average correlation time r, < lo-' s compared to an average correlation time r, E Spin locking is an effective method of obtaining T ,,-weighted images at static field strengths up to about 0.15 T and changes in image signal strength observed by varying f ; and TSL agree with those predicted by Eq. [2]. Measurement of T I , frequency dispersions in tissues is useful in predicting the signal strengths to be obtained in MR images.…”

Target‐point combination of MR images

“…in advance, and this is usually difficult or impossible because of the wide variability in these pa- rameters. McVeigh et al (21 ) have recently addressed this problem by modeling the NMR parameters of each tissue as probability density functions, rather than as specific values, and showed that the pulse sequence which maximizes the probability of distinguishing the two tissues may be significantly different from the pulse sequence optimized for the mean values of the parameters. Finally, the clinical goal is often to produce good contrast between several pairs of tissues, either for detection of several possible lesions or simply for good anatomical definition.…”

Feature space analysis: Effects of MRI protocols

“…[7][8][9] The decision boundaries based on the calculated tissue parameters are also different from the correct statistical boundaries. 10 Category ͑3͒ can be applied to any MRI image set. It can improve the clustering properties of the data for the feature space representation while reducing its dimensionality.…”