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me-view

Local web viewer for multi-echo fMRI datasets using a FastAPI backend, a React frontend, NiiVue for image rendering, and Plotly for quantitative voxel plots.

MVP scope

  • Upload one or more 4D NIfTI files representing separate echoes.
  • Build a session manifest that groups echoes into datasets.
  • View data in single-echo and synchronized compare layouts.
  • Keep compare-view echoes aligned on crosshair position, timepoint, and shared colormap/display bounds by default.
  • Show two quantitative panels:
    • echo curve at the selected voxel and current timepoint
    • time course at the selected voxel for the active echo
  • Reserve room for reversible preprocessing through niivue-niimath session revisions.

Out of scope for this first implementation slice:

  • GIFTI and surface rendering
  • long-term persistent session storage
  • ROI analysis and derived QC dashboards

Required toolchain

  • Backend: Python 3.13 managed with uv
  • Frontend: React managed and built with Vite

These are required project choices, not interchangeable defaults.

Repository structure

.
├── backend/
├── docs/
└── frontend/

Quickstart

Workspace

npm install
npm run dev

This starts both the FastAPI backend and the Vite frontend together from the repository root.

Backend

cd backend
uv sync
uv run uvicorn me_view.main:app --reload --app-dir src

Frontend

cd frontend
npm install
npm run dev

The Vite dev server proxies /api requests to the FastAPI backend on port 8000.

Data assumptions

  • Each uploaded echo is expected to be a NIfTI volume, typically one 4D file per echo.
  • Echoes in the same dataset should share spatial dimensions, affine alignment, and timepoint count.
  • Echo ordering is inferred from filenames when possible and can be finalized explicitly through the session API.

Documentation map