解耦的潜在基于能量的风格转换：一种图像级别的结构MRI协调框架
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework
AI总结：
提出一种解耦潜在基于能量的风格转换框架DLEST，通过在潜在空间分离图像生成与风格转换实现高效非配对MRI协调，在多任务上优于现有方法。
摘要：
脑磁共振成像（MRI）在临床和研究领域被广泛使用，但通常对由场强和扫描仪供应商等非生物学差异引起的站点效应表现出敏感性。许多回顾性MRI协调技术在减少图像级别的站点效应方面取得了令人鼓舞的成果。然而，现有方法通常存在计算需求高和泛化能力有限的问题，限制了它们对未见MRI的适用性。本文设计了一种用于非配对图像级MRI协调的新型解耦潜在基于能量的风格转换（DLEST）框架，包含站点不变图像生成（SIG）、站点特定风格转换（SST）和站点特定MRI合成（SMS）。具体而言，SIG采用潜在自编码器将MRI编码到低维潜在空间，并从潜在编码重建MRI。SST利用基于能量的模型来理解目标域的全局潜在分布，并将源潜在编码向目标域转换，而SMS能够合成具有目标特定风格的MRI。通过在潜在空间中解耦图像生成和风格转换，DLEST能够实现高效的风格转换。我们的模型在来自公共数据集（包含58个采集站点/设置下的3,984名受试者）的T1加权MRI上进行训练，并在一个独立数据集（包含在11个站点/设置下扫描的9名移动受试者）上进行验证，任务包括：直方图和特征可视化、站点分类、脑组织分割以及站点特定结构MRI合成。定性和定量结果证明了我们的方法优于几种最先进的方法。
英文摘要：
Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors. Numerous retrospective MRI harmonization techniques have demonstrated encouraging outcomes in reducing the site effects at the image level. However, existing methods generally suffer from high computational requirements and limited generalizability, restricting their applicability to unseen MRIs. In this paper, we design a novel disentangled latent energy-based style translation (DLEST) framework for unpaired image-level MRI harmonization, consisting of (a) site-invariant image generation (SIG), (b) site-specific style translation (SST), and (c) site-specific MRI synthesis (SMS). Specifically, the SIG employs a latent autoencoder to encode MRIs into a low-dimensional latent space and reconstruct MRIs from latent codes. The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style. By disentangling image generation and style translation in latent space, the DLEST can achieve efficient style translation. Our model was trained on T1-weighted MRIs from a public dataset (with 3,984 subjects across 58 acquisition sites/settings) and validated on an independent dataset (with 9 traveling subjects scanned in 11 sites/settings) in four tasks: histogram and feature visualization, site classification, brain tissue segmentation, and site-specific structural MRI synthesis. Qualitative and quantitative results demonstrate the superiority of our method over several state-of-the-arts.
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