Unsupervised AnomalyCEST: Unsupervised anomaly detection using CEST MRI of brain at 7T

Motivation: Chemical exchange saturation transfer (CEST) MRI is a sensitive technique that provides metabolic information in vivo. However, conventional methods of deriving CEST metrics present limits of ill-posed conditions and computational complexity, while comparing healthy and pathological individuals requires sufficiently large sample sizes. Goal(s): Use unsupervised anomaly detection for the assessment of anomalous voxels on an individual basis, circumventing the need for deriving traditional CEST metrics. Approach: We implement a 1D convolutional autoencoder trained on healthy participants to detect anomalous regions in an MS subject by identifying voxels with high reconstruction error. Results: The network effectively captures anomalous regions on an individual basis. Impact: Traditional CEST MRI methods require large sample sizes and complex fitting techniques to compare healthy individuals with those having pathology. Unsupervised anomaly detection of Z-spectra will enable the detection of anomalous voxels linked to pathology at an individual level.