Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs - Timmins, Kimberley M; Irene C van der Schaaf; Ruigrok, Ynte M; Velthuis, Birgitta K; Kuijf, Hugo J (arXiv.org, 2021)
Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) enable visualization and analysis of cerebral arteries. This analysis may indicate normal variation of the configuration of the cerebrovascular system or vessel abnormalities, such as aneurysms. A model would be useful to represent normal cerebrovascular structure and variabilities in a healthy population and to differentiate from abnormalities. Current anomaly detection using autoencoding convolutional neural networks usually use a voxelwise mean-error for optimization. We propose optimizing a variational-autoencoder (VAE) with structural similarity loss (SSIM) for TOF-MRA reconstruction. A patch-trained 2D fully-convolutional VAE was optimized for TOF-MRA reconstruction by comparing vessel segmentations of original and reconstructed MRAs. The method was trained and tested on two datasets: the IXI dataset, and a subset from the ADAM challenge. Both trained networks were tested on a dataset including subjects with aneurysms. We compared VAE optimization with L2-loss and SSIM-loss. Performance was evaluated between original and reconstructed MRAs using mean square error, mean-SSIM, peak-signal-to-noise-ratio and dice similarity index (DSI) of segmented vessels. The L2-optimized VAE outperforms SSIM, with improved reconstruction metrics and DSIs for both datasets. Optimization using SSIM performed best for visual image quality, but with discrepancy in quantitative reconstruction and vascular segmentation. The larger, more diverse IXI dataset had overall better performance. Reconstruction metrics, including SSIM, were lower for MRAs including aneurysms. A SSIM-optimized VAE improved the visual perceptive image quality of TOF-MRA reconstructions. A L2-optimized VAE performed best for TOF-MRA reconstruction, where the vascular segmentation is important. SSIM is a potential metric for anomaly detection of MRAs.