Unsupervised anomaly detection in Brain MRIs aims to identify abnormalities as outliers from a healthy training distribution. Reconstruction-based approaches that use generative models to learn to reconstruct healthy brain anatomy are commonly used for this task. Diffusion models are an emerging class of deep generative models that show great potential regarding reconstruction fidelity. However, they face challenges in preserving intensity characteristics in the reconstructed images, limiting their performance in anomaly detection. To address this challenge, we propose to condition the denoising mechanism of diffusion models with additional information about the image to reconstruct coming from a latent representation of the noise-free input image. This conditioning enables high-fidelity reconstruction of healthy brain structures while aligning local intensity characteristics of input-reconstruction pairs. We evaluate our method's reconstruction quality, domain adaptation features and finally segmentation performance on publicly available data sets with various pathologies. Using our proposed conditioning mechanism we can reduce the false-positive predictions and enable a more precise delineation of anomalies which significantly enhances the anomaly detection performance compared to established state-of-the-art approaches to unsupervised anomaly detection in brain MRI. Furthermore, our approach shows promise in domain adaptation across different MRI acquisitions and simulated contrasts, a crucial property of general anomaly detection methods.

通过在扩散模型上引入条件机制，对无监督异常检测中的脑部MRI图像重构进行优化，以提高重构质量、域适应性特征和分割性能，并且可在不同MRI采集和模拟对比度之间进行领域适应，从而提高脑部MRI无监督异常检测的性能和泛化能力。

基于条件扩散模型的引导重建用于无监督脑磁共振成像异常检测