Mimicking the human expert: Pattern recognition for an automated assessment of data quality in MR spectroscopic images

Abstract

Besides the diagnostic evaluation of a spectrum, the assessment of its quality and a check for plausibility of its information remains a highly interactive and thus time-consuming process in MR spectroscopic imaging (MRSI) data analysis. In the automation of this quality control, a score is proposed that is obtained by training a machine learning classifier on a representative set of spectra that have previously been classified by experts into evaluable data and nonevaluable data. In the first quantitative evaluation of different quality measures on a test set of 45,312 long echo time spectra in the diagnosis of brain tumor, the proposed pattern recognition (using the random forest classifier) separated high-and low-quality spectra comparable to the human operator (area-under-the-curve of the receiver-operator-characteristic, AUC >0.993), and performed better than decision rules based on the signal-to-noise-ratio (AUC <0.934) or the estimated Cramé r-Rao-bound on the errors of a spectral fitting (AUC <0.952). This probabilistic assessment of the data quality provides comprehensible confidence images and allows filtering the input of any subsequent data processing, i.e., quantitation or pattern recognition, in an automated fashion. It thus can increase robustness and reliability of the final diagnostic evaluation and allows for the automation of a tedious part of MRSI data analysis.

Random Forest as a Non-parametric Algorithm for Near-infrared (NIR) Spectroscopic Discrimination for Geographical Origin of Agricultural Samples

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An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements

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A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation— With Application to Tumor and Stroke

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