Unsupervised anomaly detection using generative adversarial networks in (1)H-MRS of the brain.


Jang J(1), Lee HH(1), Park JA(2), Kim H(3).
Author information:
(1)Department of Biomedical Sciences, Seoul National University, Seoul, South Korea.
(2)Division of Applied RI, Korea Institute of Radiological & Medical Science, Seoul, South Korea.
(3)Department of Medical Sciences, Seoul National University, Seoul, South Korea; Department of Radiology, Seoul National University Hospital, Seoul, South Korea. Electronic address: [Email]


The applicability of generative adversarial networks (GANs) capable of unsupervised anomaly detection (AnoGAN) was investigated in the management of quality of 1H-MRS human brain spectra at 3.0 T. The AnoGAN was trained in an unsupervised manner solely on simulated normal brain spectra and used for filtering out abnormal spectra with a broad range of abnormalities, which were simulated by including abnormal ranges of SNR, linewidth and metabolite concentrations and spectral artifacts such as ghost, residual water, and lipid. The AnoGAN was able to filter out those spectra with SNR less than ~11-12 dB with an accuracy of ~80% or higher (assuming a normal SNR range to be 15-18 dB). It also detected with an accuracy of ~80% or higher those spectra, in which NAA levels were reduced by ~25-30% or more from the lower bound and elevated by ~20-30% or more from the upper bound of the normal concentration range (7.5-17 mmol/L), while the concentrations of the rest of the metabolites were all within the normal ranges. Despite the fact that those spectra contaminated with ghost, residual water or lipid have never been involved in the training or optimization of the AnoGAN, they were correctly classified as abnormal regardless of the types of the artifacts, depending solely on their intensity. Although the current version of our AnoGAN requires further technical improvement particularly for the detection of linewidth-associated abnormality and validation on in vivo data, our unsupervised deep learning-based approach could be an option in addition to those previously reported supervised deep learning-based approaches in the binary classification of spectral quality with an extended abnormal spectra regime.