Automatic quality control in clinical (1) H MRSI of brain cancer.

Da Silva Mendes Pedrosa, Nuno Miguel; McKinley, Richard; Knecht, Urspeter; Wiest, Roland; Slotboom, Johannes (2016). Automatic quality control in clinical (1) H MRSI of brain cancer. NMR in biomedicine, 29(5), pp. 563-575. Wiley Interscience 10.1002/nbm.3470

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MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Da Silva Mendes Pedrosa, Nuno Miguel; McKinley, Richard; Knecht, Urspeter; Wiest, Roland and Slotboom, Johannes

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0952-3480

Publisher:

Wiley Interscience

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

08 Jun 2016 12:25

Last Modified:

08 Jun 2016 12:25

Publisher DOI:

10.1002/nbm.3470

PubMed ID:

27071355

Uncontrolled Keywords:

MRSI; automatic classification; brain cancer; pattern recognition; quality control; spectral features

BORIS DOI:

10.7892/boris.81254

URI:

https://boris.unibe.ch/id/eprint/81254

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