Chronic subdural hematoma (cSDH) is a common neurological condition
characterized by the accumulation of blood between the brain and the dura
mater. This accumulation of blood can exert pressure on the brain, potentially
leading to fatal outcomes. Treatment options for cSDH are limited to invasive
surgery or non-invasive management. Traditionally, the midline shift,
hand-measured by experts from an ideal sagittal plane, and the hematoma volume
have been the primary metrics for quantifying and analyzing cSDH. However,
these approaches do not quantify the local 3D brain deformation caused by cSDH.
We propose a novel method using anatomy-aware unsupervised diffeomorphic
pseudo-healthy synthesis to generate brain deformation fields. The deformation
fields derived from this process are utilized to extract biomarkers that
quantify the shift in the brain due to cSDH. We use CT scans of 121 patients
for training and validation of our method and find that our metrics allow the
identification of patients who require surgery. Our results indicate that
automatically obtained brain deformation fields might contain prognostic value
for personalized cSDH treatment. Our implementation is available on:
github.com/Barisimre/brain-morphing

提出了一种使用解剖感知的非监督可微分假设健康合成方法生成脑部变形场的新方法，并利用该方法派生的变形场提取通过慢性硬脑膜下血肿引起的脑部位移的生物标志物，结果表明自动获得的脑部变形场可能具有个性化慢性硬脑膜下血肿治疗的预测价值。