Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Comput Med Imaging Graph. 2023 Oct;109:102285. doi: 10.1016/j.compmedimag.2023.102285. Epub 2023 Aug 14.
The lack of standardization and consistency of acquisition 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. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. 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 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. 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 harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.
采集缺乏标准化和一致性是磁共振成像（MR）中的一个突出问题。这通常会导致采集图像中的对比度产生不理想的变化，原因是硬件和采集参数存在差异。近年来，提出了基于图像合成的解缠磁共振调和方法来补偿不理想的对比度变化。其基本思想是从 MR 图像中解缠解剖结构和对比度信息，以实现跨站点调和。尽管现有方法取得了成功，但我们认为可以从三个方面进行重大改进。首先，大多数现有方法都是基于多对比度 MR 图像的同一主题共享相同解剖结构的假设。这个假设是值得怀疑的，因为不同的 MR 对比度专门用于突出不同的解剖特征。其次，这些方法通常需要一组固定的 MR 对比度进行训练（例如，T1 加权和 T2 加权图像），限制了它们的适用性。最后，现有方法通常对成像伪影敏感。在本文中，我们提出了一种新的方法 Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness（HACA3），用于解决这三个问题。HACA3 包含一个解剖融合模块，用于解释 MR 对比度之间固有的解剖差异。此外，HACA3 可以针对任何组合的 MR 对比度进行训练和应用，并且对成像伪影具有鲁棒性。HACA3 是在具有不同场强、扫描仪平台和采集协议的 21 个站点采集的各种 MR 数据集上开发和评估的。实验表明，HACA3 在多个图像质量指标下实现了最先进的调和性能。我们还展示了 HACA3 在下游任务中的多功能性和潜在临床影响，包括多发性硬化症患者的白质病变分割和正常老化受试者的纵向体积分析。代码可在 https://github.com/lianruizuo/haca3 上获得。
