Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse events such as haemorrhagic stroke (resulting from the rupture of aneurysms in blood vessels). However, MRAs are not acquired routinely, hence, an approach to synthesise blood vessel segmentations from more routinely acquired MR contrasts such as T1 and T2, would be useful. We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI. We propose a two-phase multi-objective learning approach, which captures both global and local features. It uses learned local attention maps generated by dilating the segmentation labels, which forces the network to only extract information from the T2 MRI relevant to synthesising the CoW. Our synthetic vessel segmentations generated from only T2 MRI achieved a mean Dice score of $0.79 \pm 0.03$ in testing, compared to state-of-the-art segmentation networks such as transformer U-Net ($0.71 \pm 0.04$) and nnU-net($0.68 \pm 0.05$), while using only a fraction of the parameters. The main qualitative difference between our synthetic vessel segmentations and the comparative models was in the sharper resolution of the CoW vessel segments, especially in the posterior circulation.

翻译：磁共振血管造影（MRA）是一种用于可视化血管的成像模态，在多种诊断应用以及评估不良事件（如由血管中动脉瘤破裂引起的出血性中风）风险中具有重要价值。然而，MRA并非常规采集，因此，从更常规采集的MR对比（如T1和T2）中合成血管分割的方法将十分有用。我们提出了一种仅从T2 MRI合成Willis环（CoW）主要脑动脉分割的编码器-解码器模型。我们采用了一种两阶段多目标学习方法，同时捕捉全局和局部特征。该方法利用通过扩张分割标签生成的习得性局部注意力图，迫使网络仅从T2 MRI中提取与CoW合成相关的信息。仅从T2 MRI生成的合成血管分割在测试中取得了平均Dice得分$0.79 \pm 0.03$，而相比之下，如Transformer U-Net（$0.71 \pm 0.04$）和nnU-net（$0.68 \pm 0.05$）等前沿分割网络仅使用了一小部分参数。我们的合成血管分割与对比模型之间的主要定性差异在于CoW血管段的更清晰分辨率，尤其是在后循环中。