  The use of supervised deep learning techniques to detect pathologies in brain
MRI scans can be challenging due to the diversity of brain anatomy and the need
for annotated data sets. An alternative approach is to use unsupervised anomaly
detection, which only requires sample-level labels of healthy brains to create
a reference representation. This reference representation can then be compared
to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To
accomplish this, generative models are needed to create anatomically consistent
MRI scans of healthy brains. While recent diffusion models have shown promise
in this task, accurately generating the complex structure of the human brain
remains a challenge. In this paper, we propose a method that reformulates the
generation task of diffusion models as a patch-based estimation of healthy
brain anatomy, using spatial context to guide and improve reconstruction. We
evaluate our approach on data of tumors and multiple sclerosis lesions and
demonstrate a relative improvement of 25.1% compared to existing baselines.
