Reducing MRI Inter‐Scanner Variability Using 3D Superpixel ComBat

Abstract

BackgroundInter‐scanner variability hinders the direct comparability of multi‐site/scanner MRI data for clinical research. The ComBat method is commonly used to reduce the variability based on an empirical Bayes framework1,2, harmonizing the data at the feature level (e.g., region‐of‐interest measures). However, directly harmonizing the scans at the voxel‐level using ComBat has been relatively less explored. In this study, we investigated the performance of the voxel‐wise ComBat. Also, going beyond voxels, we proposed a new ComBat approach which operates on a small group of voxels called superpixels3.MethodEighteen subjects (10 patients with Alzheimer's disease and 8 controls; age: 68.0 [9.3] years; 10 females) participated in this study. For each subject, T1‐weighted images were acquired on each of four 3T scanners with different manufacturers or models (i.e., GE, Philips, Siemens‐Prisma, Siemens‐Trio). After the standard image preprocessing including two‐step registration by using the Statistical Parametric Mapping (SPM12)4, the unharmonized scans (Raw data) were aligned in the standard template space. To reduce the computational load for ComBat at the voxel level (Voxel‐ComBat), we used a three‐dimensional superpixel algorithm3 to parcellate the images into hundreds of superpixels based on the study‐specific template, and then the ComBat was applied at the superpixel level (Figure 1). Compared to Voxel‐Combat operating on about half million voxels (computation time >>10,000 seconds), this superpixel ComBat (SP‐ComBat) operates on only a few hundred superpixels, significantly improving the computation efficiency (computation time < 5 seconds) while maintaining the harmonization performance. The harmonized scans were used to estimate cortical thickness by employing surface‐based morphometry5, and the coefficients of variation of thickness measures were calculated to evaluate the harmonization performance.ResultThe harmonized data provided similar contrasts across scanners compared to the Raw images in visual inspection (Figure 2) and had comparable distributions of the tissue‐specific signal intensity between scanners for both Voxel‐ComBat and SP‐ComBat (Figure 3). Also, these two methods significantly reduced the inter‐scanner variation (both p‐values < 0.001) in terms of cortical thickness measures (Figure 4).ConclusionThis study evaluated the feasibility and effectiveness of Voxel‐ComBat and proposed a new approach, SP‐ComBat, to optimize the efficiency of ComBat harmonization at the voxel level.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites

“…ComBat characterizes scanner effects into an additive (mean) and a multiplicative (variance) scanner effect for each imaging feature. Moreover, ComBat has been extended to harmonize imaging data collected in a longitudinal manner (Beer et al, 2020 ) and to harmonize MRI scans at the voxel level by incorporating the superpixel technique (Chen, Torbati, et al, 2022 ).…”

SAN: mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites

“…ComBat characterizes scanner effects into an additive (mean) and a multiplicative (variance) scanner effect for each imaging feature. Moreover, ComBat has been extended to harmonize imaging data collected in a longitudinal manner [27] and to harmonize MRI scans at the voxel level by incorporating the superpixel technique [28]. Recent harmonization methods (CovBat and RELIEF) have been shifted to expand the scope of statistical harmonization to address heterogeneous covariances, going beyond the mean-variance specifications in ComBat [29, 30].…”