Simple and broadly applicable automatic quality control for 3D 1H MR spectroscopic imaging data of the prostate. Issue 5 (2nd December 2018)

• Record Type: Journal Article
• Authors: Tayari, Nassim; Obels, Jiri; Kobus, Thiele; Scheenen, Tom W.J.; Heerschap, Arend
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 5(2019)
• Page Start: 2887
• Page End: 2895
• Publication Date: 2018-12-02
• Subjects: prostate -- quality control -- spectroscopic imaging; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27616
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: Quality control (QC) is a prerequisite for clinical MR spectroscopic imaging (MRSI) to avoid that bad spectra hamper data interpretation. The aim of this work was to present a simple automatic QC for prostate 1 H MRSI that can handle data obtained with different commonly used pulse sequences, echo times, field strengths, and MR platforms. Methods: A QC method was developed with a ratio (Q ratio ) where the numerator and the denominator are functions of several signal heights, logically combined for their positive or negative contribution to spectral quality. This Q ratio was tested on 4 data sets obtained at 1.5, 3, and 7T, with and without endorectal coil and different localization sequences and echo times. Spectra of 25, 248 voxels in 26 prostates were labeled as acceptable or unacceptable by MRS experts as gold standard. A threshold value was determined for Q ratio from a subset of voxels, labeled in consensus by 4 experts, for an optimal accuracy to separate spectra. Results: Applying this Q ratio threshold to the remaining test voxels, an automatic separation of good and bad spectra was possible with an accuracy of 0.88, similar to manual separation between the 2 classes. Q ratio values were used to generate maps representing spectral quality on a binary or continuous scale. Conclusion: Automated QC of prostate 1 H MRSI by Q ratio is fast, simple, easily transferable and more practical than supervised feature extraction methods and therefore easy to integrate into different clinical MR systems. Moreover, quality maps can be generated to read the reliability of spectra in each voxel.