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.

翻译：光光磁共振振动动像仪(TOF-MRAs)通常能对大脑动脉进行视觉化和分析。 此分析可能显示脑血管系统或血管异常(如动脉瘤)的配置的正常变化。 一个模型将有利于代表健康人群中的正常脑血管结构和变异性,并区分异常。 目前使用自动解析变异神经网络的异常检测通常使用一种异质性向异质异质性向中位器优化。 我们提议优化一个具有结构相似性的变异-自动振动变异器(VAE),为重建TO-MRA重建提供结构上的变异性。 一个经过补补补的2D全振动性 VAE通过比较原始和再整改的 MMAs 元件部分来优化。 该方法在两个数据集中进行了培训和测试: IXI 改进的数据集,以及 ADAM 挑战的子组。 两种经过训练的网络都是在包含 EurysIM- Redia Redia Redia- remacial 和SMA 进行原始的变现性变压数据,我们用S- 和SMADRAVADRAD 进行进行了分析。