Quality of clinical brain tumor MR spectra judged by humans and machine learning tools

Abstract

Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A convolutional neural network to filter artifacts in spectroscopic MRI

“…Furthermore, the network was wrapped into a framework that enabled rapid deployment of the filter into the clinical research workflow, and could be applied in under 2 minutes to high‐resolution EPSI data with whole‐brain coverage. The accuracy achieved is similar to that reported in previous studies using machine learning for MR spectral quality analysis, such as those using random forests with engineered spectral features . It is difficult to compare results across studies, as a result of variation in study design (e.g., how data were collected, the biases of the raters generating ground truth, and which parameters were chosen as features).…”

“…Another challenge in developing algorithms for spectral quality filtering is the low percentage of poor‐quality voxels present in a whole‐brain volume compared with good quality voxels, which yields an imbalance in class proportions and consequently can hinder algorithm performance . In the data set collected in this work, 72% of spectra were of good quality and 28% were of poor quality, which is similar to proportions (65‐84% acceptable spectra) observed in other works .…”

“…Common to all of these approaches is that the definition of features is required before performing any analysis. Thus, these features capture criteria that MRS experts explicitly believe are important to spectral quality, and machine‐learning algorithms built using these engineered features have shown promise as spectral quality filters . In contrast to systems that model expert beliefs explicitly, deep learning broadly defines a category of machine learning algorithms that can learn underlying features from raw data without the need for any a priori definition of such features, and have been able to shatter benchmarks in natural language processing, medical image segmentation, survival analysis, and identification of pathology in medical images .…”

Characterisation of tissue-type metabolic content in secondary progressive multiple sclerosis: a magnetic resonance spectroscopic imaging study

“…However, reduced numbers were mainly due to rejection based on the Cramer Rao bounds and visual quality assessment. The latter is time consuming and necessarily subjective, thus leading to interest in developing machine-learning approaches to spectral quality control [19]. …”

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting

“…Machine learning has proven to have exceptional use in medical imaging, including MRSI . Hiltunen et al described an artificial neural network (ANN) architecture that could predict metabolite peak areas from magnitude spectra in patients with gliomas.…”