Keywords: CEST / APT / NOE, Diagnosis/Prediction, AI/ML, Anomaly detection, CEST/MT, Convolutional neural network
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.
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