{"doi":"10.1002/mp.70096","title":"Effect of a consistent reconstruction algorithm on inter‐scanner reproducibility in diffusion MRI","abstract":"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.","journal":"Medical Physics","year":2025,"id":548016,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9516,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":784671,"name":"Ante Zhu","orcid":"0000-0001-6251-2341","position":1,"is_corresponding":false},{"id":1344749,"name":"Xiaoqing Wang","orcid":"0000-0003-3965-5728","position":2,"is_corresponding":false},{"id":264211,"name":"Deniz Erdoğmuş","orcid":"0000-0002-1114-3539","position":3,"is_corresponding":false},{"id":286815,"name":"Carl‐Fredrik Westin","orcid":null,"position":4,"is_corresponding":false},{"id":273472,"name":"Lauren J. O’Donnell","orcid":"0000-0003-0197-7801","position":5,"is_corresponding":false},{"id":263263,"name":"Berkin Bilgic̦","orcid":"0000-0002-9080-7865","position":6,"is_corresponding":false},{"id":291213,"name":"Lipeng Ning","orcid":"0000-0003-4992-459X","position":7,"is_corresponding":false},{"id":273470,"name":"Yogesh Rathi","orcid":"0000-0002-9946-2314","position":8,"is_corresponding":false},{"id":1221482,"name":"Qiang Liu","orcid":"0009-0007-6818-0115","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T02:53:53.962932Z","pmid":"41145995","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}