空间信息引导的图像和谐化在改进扫描效应去除与预测中取得成效
Spatially-informed Image Harmonization Results in Improved Scanner Effect Removal and Prediction
AI总结：
本文提出Tensor-ComBat方法，通过低秩PARAFAC分解和后处理ComBat步骤，提升结构神经影像数据中扫描效应去除与生物预测的准确性。
摘要：
我们提出了一种新的数据和谐化方法，称为张量-ComBat（TC），用于结构性神经影像数据。Tensor-ComBat是一种新颖的空间意识和谐化方法，旨在估计并去除不同研究站点或扫描仪之间 voxel 级图像中的 unwanted 技术变化。Tensor-ComBat 使用贝叶斯张量响应回归（BTRR）模型，通过在模型系数上进行低秩 PARAFAC 分解来估计空间分布的扫描效应，并随后通过后处理 ComBat 和谐化步骤去除这些扫描效应。与经典 ComBat 方法将图像中的 ROIs 或 voxels 视为可互换不同，Tensor-ComBat 方法在估计模型参数时纳入了成像 voxels 的空间配置信息，从而得到改进的和谐化流程。所提出的 Tensor-ComBat 方法使用马尔可夫链蒙特卡洛（MCMC）进行拟合，不仅能产生点估计，还能量化这些估计相关的不确定性。由于 Tensor-ComBat 的全贝叶斯实现，它能够执行与显著的空间分布扫描效应和/或生物效应相关的推断，这在现有方法中并不 straightforward。该方法应用于 ADNI-1 中的超过 2100 个 T1w-MRI 扫描，展示了比 ComBat 等现有方法更优的扫描效应去除、改进的生物预测和优越的可重复性。
英文摘要：
We propose a novel data harmonization approach known as Tensor-ComBat (TC) for structural neuroimaging data. Tensor-Combat is a novel spatially aware harmonization method that aims to estimate and remove unwanted technical variation between voxel-level images from different study sites or scanners. Tensor-ComBat uses a Bayesian tensor response regression (BTRR) model to estimate spatially distributed scanner effects via a low-rank PARAFAC decomposition on the model coefficients, and subsequently removes these scanner effects via a post-hoc ComBat harmonization step. Unlike the classical ComBat method that treats the ROIs or voxels in the image as interchangeable, the Tensor-ComBat approach incorporates the information about the spatial configurations of imaging voxels when estimating the model parameters, resulting an improved harmonization pipeline. The proposed Tensor-ComBat method is fit using a Markov chain Monte Carlo (MCMC) that is not only able to produce point estimates, but also able to quantify uncertainty associated with these estimates. Due to the fully Bayesian implementation of Tensor-ComBat, it is able to perform inference related to significant spatially distributed scanner effects and/or biological effects that is not straightforward under existing methods. The methods is applied to over 2100 T1w-MRI scans in ADNI-1 that illustrates greater scanner effect removal, improved biological prediction, and superior reproducibility compared to state-of-the-art approaches such as ComBat.
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