基于条件潜扩散的脑部MRI无配对体域协调方法
Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion
AI总结：
针对多中心脑部MRI存在站点相关非生物变异的问题，提出基于条件潜扩散的3D无配对体域协调框架HCLD，在4158例数据上验证其效果优于现有最优方法。
摘要：
多中心结构MRI在神经影像学研究中的应用日益广泛，以实现受试队列的多样化。然而，将不同站点/中心采集的MR图像整合可能会引入与站点相关的非生物学变异。回顾性图像协调有助于解决这一问题，但现有方法通常对预先提取的手工放射组学特征进行协调，限制了下游应用。若干图像层面的方法聚焦于2D切片，忽略了固有的体域信息，导致效果欠佳。为此，我们提出一种新型3D MRI协调框架——条件潜扩散协调（HCLD），该框架明确考虑图像风格与脑部解剖结构。它包含一个具有泛化能力的3D自编码器，通过4D潜空间对MRI进行编解码；还包含一个条件潜扩散模型，用于学习潜分布，并在以目标图像风格为条件的情况下，结合源MRI的解剖信息生成协调后的MRI。这一设计通过潜风格迁移实现了高效的体层面MRI协调，且训练过程无需源域与目标域的配对图像。我们在来自三个数据集的4158例T1加权脑部MRI上，通过三项任务对HCLD进行训练与评估，检验其在保留关键生物学特征的同时消除站点相关变异的能力。定性与定量实验均表明，HCLD的效果优于多种现有最优方法。
英文摘要：
Multi-site structural MRI is increasingly used in neuroimaging studies to diversify subject cohorts. However, combining MR images acquired from various sites/centers may introduce site-related non-biological variations. Retrospective image harmonization helps address this issue, but current methods usually perform harmonization on pre-extracted hand-crafted radiomic features, limiting downstream applicability. Several image-level approaches focus on 2D slices, disregarding inherent volumetric information, leading to suboptimal outcomes. To this end, we propose a novel 3D MRI Harmonization framework through Conditional Latent Diffusion (HCLD) by explicitly considering image style and brain anatomy. It comprises a generalizable 3D autoencoder that encodes and decodes MRIs through a 4D latent space, and a conditional latent diffusion model that learns the latent distribution and generates harmonized MRIs with anatomical information from source MRIs while conditioned on target image style. This enables efficient volume-level MRI harmonization through latent style translation, without requiring paired images from target and source domains during training. The HCLD is trained and evaluated on 4,158 T1-weighted brain MRIs from three datasets in three tasks, assessing its ability to remove site-related variations while retaining essential biological features. Qualitative and quantitative experiments suggest the effectiveness of HCLD over several state-of-the-arts
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