The lack of standardization is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. Despite the success of existing methods, we argue that three major improvements can be made. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art performance under multiple image quality metrics. We also demonstrate the applicability and versatility of HACA3 on downstream tasks including white matter lesion segmentation and longitudinal volumetric analyses.

翻译：磁共振（MR）成像中缺乏标准化是一个突出问题。由于硬件和采集参数的差异，采集到的图像常出现非期望的对比度变化。近年来，基于图像合成与解耦的MR统一化方法已被提出以补偿此类非期望对比度变化。尽管现有方法取得一定成功，我们认为仍有三个主要改进方向。首先，多数现有方法基于同一受试者的多对比度MR图像共享相同解剖结构的假设，但该假设具有争议性，因不同MR对比度专用于突出不同解剖特征。其次，这些方法通常需要固定MR对比度组合进行训练（如同时具备T1加权和T2加权图像），限制了其适用性。最后，现有方法普遍对成像伪影敏感。本文提出基于注意力机制的对比度-解剖-伪影感知统一化方法（HACA3），以解决上述三个问题。HACA3包含一个解剖结构融合模块，可处理不同MR对比度间的固有解剖差异。此外，HACA3对成像伪影具有鲁棒性，可基于任意MR对比度组合进行训练与应用。我们在来自21个中心、涵盖不同场强、扫描仪平台及采集协议的多源MR数据集上开发并评估了HACA3。实验表明，HACA3在多项图像质量指标上均达到最优性能。同时，我们通过白质病变分割和纵向体积分析等下游任务验证了HACA3的适用性和通用性。