GITNUXSOFTWARE REVIEW
Medical Conditions DisordersTop 10 Best Diffusion Tensor Imaging Software of 2026
Ranked roundup of diffusion tensor imaging software tools for DTI analysis, covering BrainVoyager, ExploreDTI, DIPY, with key tradeoffs.
How we ranked these tools
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Framework: Features 40% · Ease 30% · Value 30%
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BrainVoyager is the best pick for teams doing interactive DTI analysis with repeatable batching across many diffusion acquisitions, while if you prefer a more guided, GUI-driven DTI workflow with standard outputs, ExploreDTI is the cleaner alternative; budgetReviewId is null.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
BrainVoyager
ROI-to-tract analysis stays linked inside the project, so edits and comparisons stay consistent across runs.
Best for: Fits when teams need interactive DTI analysis with repeatable batching across many diffusion acquisitions.
ExploreDTI
Editor pickSession-based DTI projects link preprocessing, tensor fitting, tractography, and ROI statistics into one saved configuration.
Best for: Fits when teams need a repeatable DTI workflow with GUI control and standard output formats.
DIPY
Editor pickTight Python integration lets workflows customize tensor fitting inputs and tractography sampling in code.
Best for: Fits when research teams need configurable DTI modeling and tractography inside Python pipelines.
Comparison Table
BrainVoyager
commercial research platformCommercial neuroimaging platform with diffusion-weighted data processing, tensor analysis, and tractography functions.
ROI-to-tract analysis stays linked inside the project, so edits and comparisons stay consistent across runs.
BrainVoyager covers typical DTI needs including tensor fitting, diffusion-derived measures, and tractography with deterministic and probabilistic modes. The analysis workflow connects diffusion outputs to brain anatomy views for ROI analysis and tract-based visualization, which reduces format juggling during iterative studies. It also includes established interoperability with common neuroimaging ecosystems through import and export of widely used data formats used in DTI pipelines.
A tradeoff appears in automation depth versus pure code-driven pipelines, because batch processing still follows BrainVoyager project conventions rather than a standalone script-first data model. BrainVoyager fits best when a team needs consistent interactive QC and ROI workflows across many diffusion runs, then repeats the same steps through command-line batching.
- +End-to-end DTI workflow inside one project with consistent ROI handling
- +Deterministic and probabilistic tractography with diffusion map outputs for review
- +Command-line batching supports throughput across diffusion datasets
- +Strong multimodal alignment workflow for integrating diffusion with anatomical context
- –Automation is shaped by BrainVoyager project conventions, not a pure data-first schema
- –Advanced customization often needs GUI-to-parameter mapping rather than reusable scripts
- –GPU acceleration for diffusion steps is not the default assumption in standard workflows
- –Interoperability requires careful selection of import and export options per dataset
Neuroimaging core facilities
Batch QC across diffusion cohorts
Lower per-subject analysis variability
Clinical research groups
Consistent tract visualization for studies
More reproducible study outputs
Show 2 more scenarios
Academic DTI method developers
Iterate tractography settings safely
Faster parameter evaluation cycles
Developers rerun deterministic or probabilistic tractography and re-check derived diffusion maps in the same project.
Connectome analysis teams
Connect structural pathways via ROIs
Cleaner cross-subject comparisons
Teams compute tract-based measures from ROIs and maintain consistent visualization across subjects.
Best for: Fits when teams need interactive DTI analysis with repeatable batching across many diffusion acquisitions.
ExploreDTI
vertical specialistDiffusion MRI software focused on DTI processing, tractography, and white matter connectivity analysis.
Session-based DTI projects link preprocessing, tensor fitting, tractography, and ROI statistics into one saved configuration.
ExploreDTI pairs tensor fitting with tractography and multiple ROI-based measurement views, which fits labs that want consistent outputs across studies without jumping between separate apps. The project model keeps preprocessing, model fitting, tract generation, and statistics linked to the same session settings, which helps teams reproduce runs and compare subjects. Output sets are designed for downstream workflows that expect standard neuroimaging volumes, including NIfTI and common coordinate-space conventions.
The tradeoff is that ExploreDTI’s automation and API surface is oriented around its own project execution rather than a general-purpose scripting interface. This makes it a better fit for bench teams that run repeatable study pipelines from the GUI than for environments that require deep programmatic integration into larger orchestration systems. When a processing definition changes frequently between graders or cohorts, the saved configuration approach still helps, but governance and external versioning remain more manual than in server-centric platforms.
- +GUI-driven DTI pipeline keeps preprocessing and fiber steps in one project
- +Batch-style project runs support repeatability across multiple subjects
- +Outputs align with common neuroimaging formats for downstream review
- +ROI and measurement views reduce extra export steps
- –Automation depth is limited versus full external command-line orchestration
- –Multi-step preprocessing requires careful manual parameter review
- –Integration into external schedulers and APIs is not the primary workflow
- –Tractography tuning can be time-consuming when dataset quality varies
Neuroimaging core labs
Run consistent DTI analyses per cohort
Faster cohort turnaround
Clinically oriented research groups
Standardize tractography reports across sites
More consistent findings
Show 2 more scenarios
Methods teams using FSL pipelines
Use DTI outputs in mixed workflows
Lower manual format work
Generated diffusion-derived volumes and tract results support downstream tooling that expects common formats.
Graduate labs repeating analyses
Iterate on preprocessing choices
Quicker parameter iteration
Saved project settings make it easier to re-run changes and compare outputs across sessions.
Best for: Fits when teams need a repeatable DTI workflow with GUI control and standard output formats.
DIPY
developer toolkitPython library for diffusion MRI analysis with tensor models, tractography, reconstruction, and visualization tools.
Tight Python integration lets workflows customize tensor fitting inputs and tractography sampling in code.
DIPY provides core steps for diffusion modeling and DTI-derived measures using Python APIs, including diffusion metric computation and tractography routines. It reads and writes common neuroimaging formats in a way that fits iterative development, including file-based NIfTI workflows. Interoperability typically happens through the surrounding Python ecosystem and external tools for eddy current correction and distortion correction rather than a single integrated GUI.
The main tradeoff is engineering overhead, because DIPY expects users to assemble preprocessing, QA, and export steps into a working pipeline. DIPY works best for reproducible research pipelines where changing model settings, sampling strategies, or derived outputs matters more than clicking through standard templates.
- +Python API exposes model and fitting parameters end to end
- +Reproducible notebooks support versioned experimentation and batch runs
- +Tensor-derived metrics integrate directly into custom analysis code
- +Flexible tractography components fit deterministic and probabilistic workflows
- –Requires building preprocessing and QA around Dipy outputs
- –Large pipelines need careful dependency management and environment control
- –GUI-driven interactions and one-click exports are limited
- –Performance tuning may be required for high angular resolution datasets
Academic DTI method developers
Benchmarking tensor models and metrics
Faster reproducible method testing
Neuroimaging pipeline engineers
Batch tractography for cohorts
Higher cohort processing throughput
Show 2 more scenarios
Computational researchers
ROI-based diffusion analysis
More flexible ROI statistics
Custom region sampling and metric aggregation plug into existing analysis libraries.
ML teams on brain imaging
Feature extraction for models
Cleaner training feature consistency
Tensor-derived maps feed directly into training data generation with consistent preprocessing steps.
Best for: Fits when research teams need configurable DTI modeling and tractography inside Python pipelines.
FSL
research suiteNeuroimaging software suite with mature diffusion MRI and DTI processing tools including FDT and tractography.
Built-in diffusion preprocessing chain that links motion correction, distortion correction, and tensor metric generation without format remapping.
FSL from fsl.fmrib.ox.ac.uk is a diffusion tensor imaging toolchain that covers preprocessing, tensor fitting, and higher-level analysis in a single workflow family. It includes command-line programs for eddy current and motion correction, susceptibility distortion correction, and robust tensor-derived metric generation.
- +End-to-end diffusion preprocessing to tensor metrics in one tool suite
- +Deterministic tensor fitting outputs integrate cleanly with downstream analysis
- +Command-line pipelines support batch throughput across large cohorts
- +Consistent output conventions reduce scripting friction between steps
- –Tractography workflows require careful parameter tuning for reproducibility
- –Interactive GUI coverage is thinner than the CLI feature depth
Best for: Fits when neuroimaging teams need a reproducible diffusion tensor workflow with CLI batch control and consistent outputs.
MRtrix3
research suiteOpen-source diffusion MRI platform focused on tractography, tensor analysis, and advanced white matter modeling.
Fiber orientation processing and tractography that integrate with advanced response estimation within a single command-driven toolchain.
MRtrix3 runs diffusion MRI pipelines from raw diffusion data to tensor-derived maps and tractography outputs using a command-line workflow. It provides dedicated engines for deterministic and probabilistic tractography, along with tools for response estimation, fiber orientation handling, and quantitative diffusion metrics.
- +End-to-end diffusion pipelines built around tractography-specific processing commands
- +Deterministic and probabilistic tractography engines with consistent command interfaces
- +Scripting and batch processing support for large multi-subject cohorts
- +Solid diffusion metric tooling with controlled preprocessing dependencies
- –Command-line workflow requires training and repeatable shell scripting discipline
- –Documentation coverage can require cross-referencing for advanced custom fiber options
- –Complex preprocessing stacks increase sensitivity to input gradient metadata quality
- –Workflow flexibility can outpace turnkey GUI guidance for basic analyses
Best for: Fits when neuroimaging teams need scriptable DTI and tractography throughput with reproducible batch control.
DSI Studio
vertical specialistDiffusion MRI analysis software for tractography, connectometry, tensor metrics, and connectome generation.
Fiber assignment by continuous tracking with both deterministic and probabilistic tractography modes in one workflow.
DSI Studio supports diffusion tensor imaging workflows end-to-end, from tensor fitting and diffusion scalar maps to DTI tractography outputs and quantitative region analysis. It is distinct for deterministic and probabilistic tractography controls that map directly to acquisition settings like b-matrix handling and fiber orientation behavior.
- +Deterministic and probabilistic tractography options with tunable parameters
- +Command-line workflow supports batch processing of diffusion pipelines
- +ROI-based extraction using diffusion-derived scalar outputs
- +Produces tract visualizations aligned with diffusion scalar maps
- –Workflow configuration depends on correct b-matrix and gradient inputs
- –Some advanced correction and registration steps need external preprocessing
- –GUI parameter exposure can be overwhelming for small studies
- –Automation still requires manual setup of per-run configuration files
Best for: Fits when teams need repeatable DTI tractography runs with CLI batching and ROI statistics.
3D Slicer
platformOpen-source medical imaging platform with diffusion MRI support through SlicerDMRI and related modules.
Extension-driven DTI tractography workflows integrated into Slicer’s scene, segmentation, and registration model.
3D Slicer combines DTI visualization with an extensible module system that supports many neuroimaging workflows in one desktop application. It supports tensor handling and tractography through dedicated extensions, then records outputs in common interchange formats such as NIfTI.
- +Interactive tensor visualization tightly coupled to segmentation and registration workflows
- +Extension system enables adding DTI fitting and tractography behaviors without changing core code
- +Common file support like NIfTI helps move DTI outputs across other neuroimaging tools
- +Programmable scripting supports repeatable UI actions and batch-style processing
- –DTI analysis depth varies heavily by which extensions are installed
- –End-to-end preprocessing coverage like eddy current correction depends on external modules
- –Reproducing a fully specified DTI pipeline takes discipline across modules and parameters
- –Some tractography settings can be harder to validate than in pipeline-first tools
Best for: Fits when research teams need interactive DTI work plus cross-modality registration and segmentation in one desktop workflow.
TORTOISE
vertical specialistDiffusion MRI processing software for correction, registration, tensor estimation, and structural connectivity workflows.
Deterministic fiber tracking with fine-grained tractography controls designed for parameterized pipeline execution.
TORTOISE is a diffusion tensor imaging software solution from the NIH DTI community that focuses on command-line and scripted workflows around DTI fitting and tractography. It supports deterministic tractography and fiber tracking with configurable preprocessing, and it produces analysis outputs that are easy to fold into repeatable pipelines.
- +Script-first DTI workflows make runs repeatable across datasets
- +Configurable deterministic tractography parameters for controlled tracing
- +NIfTI-based inputs and outputs fit common neuroimaging pipelines
- +Well-scoped preprocessing options support standard DTI prerequisites
- –Limited interactive, end-to-end GUI guidance for full analyses
- –Advanced preprocessing requires external tooling or careful setup
- –Less cohesive multimodal workflow coverage than GUI-centric DTI suites
- –Parameter tuning for stable tractography can be time-consuming
Best for: Fits when teams need repeatable, script-driven DTI tractography runs on many subjects with controlled parameters.
Elements Fibertracking
enterpriseNeurosurgical planning software for white matter tract visualization based on diffusion tensor imaging data.
Interactive fiber editing tools that keep tractography results linked to the Elements project context.
Elements Fibertracking performs DTI tractography inside Brainlab’s Elements workflow, with interactive fiber editing and visualization tied to the same workspace used for clinical imaging. The tool supports deterministic tractography and downstream quantitative maps such as fractional anisotropy and mean diffusivity.
- +Interactive tract editing inside the Brainlab Elements workspace
- +Deterministic tractography workflow with immediate visual QA
- +DTI-derived maps like fractional anisotropy and mean diffusivity
- +Project-linked outputs that stay consistent across sessions
- –Probabilistic tractography coverage is limited compared with research tools
- –Automation depends on Brainlab-style configuration rather than open scripting
- –Advanced diffusion preprocessing controls are less exposed than DTI research stacks
- –FSL-style command-line reproducibility requires extra pipeline work
Best for: Fits when clinical teams need tractography and QA tightly integrated into Brainlab imaging projects.
syngo.via
enterpriseEnterprise imaging post-processing platform that includes diffusion and tractography applications for neuro imaging.
Tightly integrated Siemens postprocessing workspace that keeps DICOM-driven DTI reconstruction and review under one configuration model.
syngo.via is Siemens Healthineers imaging software used for diffusion tensor imaging workflows inside a broader clinical postprocessing environment. It focuses on end-to-end handling from DICOM ingestion through QA-oriented reconstruction steps and tract-based outputs, with tight integration into Siemens acquisition ecosystems.
- +Workflow coherence when DTI originates from Siemens acquisition pipelines
- +Consistent UI patterns for reconstruction, parameter review, and exports
- +Good support for standard clinical diffusion-derived map generation
- +Structured outputs that fit radiology and research review practices
- –Limited breadth of diffusion modeling options compared with research toolchains
- –Automation and API access for batch processing is less transparent than open ecosystems
- –Extensibility for niche diffusion processing steps is constrained by packaging
- –Cross-platform scripting workflows require external orchestration
Best for: Fits when teams want Siemens-centered DTI postprocessing with consistent review and export, not custom pipeline research.
Conclusion
After evaluating 10 diffusion tensor imaging software, BrainVoyager stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Diffusion Tensor Imaging Software
Diffusion tensor imaging software turns diffusion-weighted MRI into tensor-derived maps and tractography results that teams can inspect, quantify, and batch across subjects. This guide focuses on workflow depth and operational fit for DTI tractography outputs, comparing BrainVoyager, ExploreDTI, DIPY, and the other tools in the top set.
The coverage emphasizes integration paths that matter in day-to-day use, including project-linking behavior, automation and API surface, and how each tool handles reproducible parameterization from preprocessing through ROI statistics and tract generation. Each tool review below includes concrete setup behaviors and constraints that affect throughput, configuration discipline, and reproducibility.
Diffusion Tensor Imaging Software for tensor fitting, diffusion metrics, and tractography workflows
Diffusion tensor imaging software fits diffusion tensors from diffusion-weighted MRI, then generates tensor metrics such as fractional anisotropy and mean diffusivity alongside tractography results. BrainVoyager supports an end-to-end DTI workflow inside a single project, where ROI-to-tract analysis stays linked so edits and comparisons remain consistent across runs.
ExploreDTI organizes preprocessing, tensor fitting, tractography, and ROI statistics into a session-based DTI project that supports batch-style runs with a repeatable GUI-driven configuration. DIPY targets research customization by exposing a Python API that lets teams control model and fitting parameters directly in code, but it shifts preprocessing and QA responsibility to surrounding pipelines.
DTI workflow fit: project linking, automation depth, and tract output control
DTI teams need software behavior that stays consistent from tensor fitting through ROI statistics and tract generation, not separate tools that lose parameter context. BrainVoyager keeps ROI-to-tract analysis linked inside a single project, so edits remain comparable across runs.
Automation depth matters because DTI throughput depends on repeatable configuration and low-friction batching. DIPY provides a Python API for tensor modeling and tractography inputs, while FSL and MRtrix3 center on command-line driven pipelines that support batch control.
Project linking that preserves ROI-to-tract consistency
BrainVoyager links ROI-to-tract analysis inside a project so changes stay comparable across diffusion acquisitions.
Session-based configuration that binds preprocessing to outputs
ExploreDTI saves preprocessing, tensor fitting, tractography, and ROI statistics into one session-based DTI project configuration.
Python control surface for tensor fitting and tractography parameters
DIPY exposes model and fitting parameters end to end through a Python API and supports reproducible notebooks for batch runs.
Preprocessing chain that spans motion, distortion, and tensor metrics
FSL provides an end-to-end diffusion preprocessing chain that links motion correction, distortion correction, and tensor metric generation.
Command-driven throughput for diffusion pipelines and tractography
MRtrix3 uses a command-driven toolchain built around tractography-oriented processing commands for deterministic and probabilistic engines.
Tracking workflow that depends on correct gradient inputs
DSI Studio combines deterministic and probabilistic tractography modes with ROI statistics, while workflow setup relies on correct b-matrix and gradient inputs.
How to choose Diffusion Tensor Imaging Software for reproducible DTI batching
The first fork is whether the workflow should stay inside a single GUI project model or move to code and shell pipelines for reproducible automation. BrainVoyager and ExploreDTI emphasize project conventions that keep preprocessing, fitting, tractography, and ROI operations consistent within one saved configuration.
The second fork is how parameterization should be managed, either through tool-managed project settings or through an external pipeline that controls inputs in code. DIPY targets teams that want Python-driven control and can build preprocessing and QA around DIPY outputs, while FSL and MRtrix3 prioritize CLI batch control and consistent output generation.
Decide whether project-linked ROI work must remain consistent across runs
Choose BrainVoyager when ROI-to-tract analysis must stay linked inside one project so edits and comparisons remain consistent across multiple diffusion acquisitions. Choose ExploreDTI when a session-based DTI project should bind preprocessing, tensor fitting, tractography, and ROI statistics into one saved configuration.
Pick the automation model that matches the team’s operational workflow
Choose DIPY when tensor fitting inputs and tractography sampling need to be customized in Python for versioned experimentation and notebook-driven batch runs. Choose FSL or MRtrix3 when CLI batching is the dominant operational pattern and diffusion preprocessing to tensor metrics should be reproducible through scripted command sequences.
Validate that your preprocessing and corrections can be anchored in one place
Choose FSL when diffusion preprocessing steps like motion correction and distortion correction should link directly to tensor metric generation without format remapping. Choose DSI Studio when tractography reproducibility is acceptable to depend on correct gradient inputs and external preprocessing for corrections and registration steps.
Check tractography reproducibility against your parameter governance process
Choose MRtrix3 or TORTOISE when controlled parameterized tractography runs must be repeatable across many subjects using command-driven discipline. Choose DSI Studio when both deterministic and probabilistic tractography modes must be available in one workflow, with careful setup of gradient inputs.
Plan for integration with segmentation, registration, and scene workflows
Choose 3D Slicer when DTI fitting and tractography behaviors must live inside a scene that already handles segmentation and registration. Choose syngo.via when DTI reconstruction and review must follow a Siemens-centered postprocessing workspace configuration tied to DICOM-driven reconstruction.
Test whether automation is first-class or depends on GUI-to-parameter mapping
Choose BrainVoyager when interactive DTI analysis and repeatable batching across many diffusion acquisitions must still preserve project-level conventions. Choose ExploreDTI when GUI-driven pipeline configuration should remain the main governance mechanism, because automation depth is limited versus external command orchestration.
Who should use Diffusion Tensor Imaging Software with these workflow constraints
DTI software fit depends on how the team enforces reproducibility, because tensor fitting and tractography outputs change when parameterization and preprocessing steps shift between runs. The tools in this guide support two dominant operating modes: project-based consistency and code or CLI-driven automation.
Neuroimaging teams running repeatable ROI-driven tract workflows
BrainVoyager supports end-to-end DTI workflow inside one project with consistent ROI handling, which reduces mismatch risk when comparing edits across diffusion acquisitions.
Research groups that standardize DTI modeling in Python pipelines
DIPY provides a Python API that exposes model and fitting parameters end to end, which fits research setups that want versioned notebooks and code-controlled experimentation.
Institutions that require diffusion preprocessing reproducibility through a CLI suite
FSL integrates motion and distortion correction with tensor metric generation, and it supports CLI batch control when outputs must stay consistent for downstream analysis.
Teams moving tractography through script-first processing throughput
MRtrix3 and TORTOISE support script-driven batch execution, which supports deterministic tractography control across many subjects with parameter governance discipline.
Clinical teams that need segmentation and tract QA to share a desktop workflow
3D Slicer ties DTI visualization to segmentation and registration workflows through extension-driven tractography behaviors, which matches cross-modality interactive QA needs.
Common pitfalls that break diffusion tensor imaging reproducibility
DTI workflows fail when teams treat tractography parameters as informal GUI choices, because small changes in preprocessing or gradient handling can shift tensor metrics and ROI statistics. The tools here handle parameter governance differently, so reproducibility breaks when the chosen workflow model is mismatched to the team’s process.
Assuming tractography reproducibility without controlling gradients and b-matrix inputs
DSI Studio workflow configuration depends on correct b-matrix and gradient inputs, so tract outputs become unreliable if gradient handling differs between subjects or runs.
Treating a project-driven tool as if it were a data-first schema
BrainVoyager automation is shaped by BrainVoyager project conventions, so reusable scripting may be harder when customization requires GUI-to-parameter mapping instead of a purely data-first interface.
Running partial preprocessing in separate tooling without verifying QA coverage
FSL and MRtrix3 can produce consistent CLI outputs, but tractography parameter tuning still needs care, so reproducibility breaks if tuning is not governed through the same scripted pipeline.
Overlooking environment control when scaling Python-based DTI modeling
DIPY requires building preprocessing and QA around DIPY outputs, so large pipelines can drift if Python dependencies and environment state are not tightly managed.
Relying on extensions without confirming full preprocessing coverage
3D Slicer DTI analysis depth varies by installed extensions, and end-to-end preprocessing coverage like eddy current correction can depend on external modules.
How We Selected and Ranked These Tools
We evaluated BrainVoyager, ExploreDTI, DIPY, and the other top tools on workflow integration depth, automation and API surface, and operational fit for reproducible DTI batching. Features account for forty percent of the ranking because ROI-to-tract linkage, session-based configuration, and CLI or Python parameter control directly affect tensor metrics and tract outputs.
Ease and value each account for thirty percent because day-to-day throughput depends on whether preprocessing, fitting, tractography, and ROI statistics stay configured under one governance model. BrainVoyager ranked highest because ROI-to-tract analysis stays linked inside a single project across runs, which keeps edits and comparisons consistent while still supporting deterministic and probabilistic tractography with diffusion map outputs for review.
Frequently Asked Questions About Diffusion Tensor Imaging Software
How do BrainVoyager and ExploreDTI differ in how diffusion analyses stay organized across datasets?
Which tools provide scriptable command-line batch control for diffusion tensor workflows?
When do diffusion preprocessing steps like motion correction and distortion correction become a gating requirement for tractography results?
Which toolchain is better suited for Python-first diffusion tensor modeling and reproducible notebook workflows?
What tradeoff appears when switching between deterministic and probabilistic tractography modes?
How do DIPY and FSL handle tensor and diffusion metrics outputs for downstream interoperability?
How do Slicer-based and Brainlab-based workflows differ for interactive fiber editing and reproducibility?
Where does data migration most commonly break when moving DTI projects between tools like BrainVoyager and ExploreDTI?
How do integration and security controls differ between Siemens-centered DTI postprocessing and research-first toolchains?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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