  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.
