Treffer: Effect of a consistent reconstruction algorithm on inter‐scanner reproducibility in diffusion MRI.

Background: Diffusion MRI (dMRI) enables non‐invasive characterization of brain microstructure and connectivity. However, multi‐center studies face reproducibility challenges due to inter‐scanner variability, which arises from differences in hardware, acquisition protocols, and image reconstruction algorithms. While prior harmonization efforts have focused on standardizing protocols and post‐processing methods, the impact of using a consistent reconstruction algorithm across scanners on inter‐scanner reproducibility remains unexplored. Purpose: To evaluate the impact of consistent reconstruction algorithms on cross‐vendor, inter‐scanner reproducibility in diffusion MRI (dMRI) microstructure and tractography‐derived measures. Methods: Identical single‐shell dMRI protocols were used on two clinical 3T scanners (Siemens Prisma and GE Premier) using simultaneous multi‐slice (SMS) EPI sequences. Five healthy volunteers were scanned twice for capturing within‐scanner variability and also on both scanners for computing cross‐scanner variability (total of 20 scans). Three MRI image reconstruction methods were assessed: vendor‐provided online reconstruction (Product), offline Split slice‐GRAPPA (Split‐GRAPPA), and offline L1‐wavelet regularized SENSE (L1‐ESPIRiT). Microstructure measures that were estimated included fiber‐specific fractional anisotropy (FA) and mean diffusivity (MD) (from a multi‐tensor UKF tractography model) and FA and MD (from a diffusion tensor imaging (DTI) model). Tractography measures included the number of streamlines and the volumetric overlap (weighted Dice coefficient, wDice). Standard error (SE) and wDice were used to evaluate within‐ and inter‐scanner variability. Additional analyses included voxelwise noise estimation using a homomorphic filtering algorithm and bootstrapped quantification of uncertainty in FA/MD using a residual‐resampling approach. Results: Offline Split‐GRAPPA significantly reduced the inter‐scanner SE of FA in both the multi‐tensor and DTI models compared to Product (p‐value < 0.001, Wilcoxon rank‐sum test). MD values showed similar inter‐scanner variability across all reconstruction methods. For tractography measures, the SE in the number of streamlines and wDice values (∼0.8) were similar across reconstruction algorithms. Noise analysis confirmed that Split‐GRAPPA achieved the lowest noise levels, as well as consistently lower FA variability. Notably, for both microstructural measures and tractography measures, inter‐scanner variability remained significantly higher than within‐scanner variability. Conclusions: Offline Split‐GRAPPA reconstruction algorithm reduced inter‐scanner variability in FA but not MD. Overall, a consistent reconstruction (with matched acquisition parameters) did not improve inter‐vendor reproducibility in dMRI measures or tractography results using other reconstruction methods. These findings highlight the need for further harmonization at the acquisition level (i.e. sequences) to achieve robust cross‐vendor comparability in dMRI studies. [ABSTRACT FROM AUTHOR]