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.

用 T2 磁共振成像合成大脑 Willis 环的主要脑动脉分割的编码器 - 解码器模型，其根据 T2 磁共振成像提取相关信息，与现有模型相比，产生具有更高分辨率的 Willis 环血管分割。