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DCEMapper is a Python desktop application designed for researchers and clinicians working with Dynamic Contrast Enhancement (DCE) MRI data. Built on a PyQt6 graphical interface, it delivers a complete end-to-end workflow — from loading raw NIfTI volumes and inspecting them slice by slice, through signal preprocessing and region-of-interest (ROI) definition, all the way to the automated generation of semi-quantitative parametric maps. Whether you are analysing a single scan or an entire BIDS-compliant dataset, DCEMapper keeps every step within a single, cohesive environment.

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Workflow overview

The DCEMapper workflow follows four sequential stages:
  1. Load — Open a NIfTI file (single file, BIDS dataset, or previously processed output) or convert raw Bruker data directly inside the application.
  2. Preprocess — Reduce acquisition noise with denoising filters and suppress Gibbs ringing artefacts before any quantitative step.
  3. ROI — Draw rectangular, elliptical, or polygon masks over any slice to restrict analysis to the tissue of interest.
  4. Map — Run the semi-quantitative pipeline to produce three voxel-wise parametric NIfTI outputs.

Data Loading

Open single NIfTI files, BIDS datasets, previously processed outputs, or convert raw Bruker data to NIfTI format.

Preprocessing

Apply denoising filters (e.g. Non-Local Means) and Gibbs artefact suppression to improve signal quality before mapping.

ROI Tools

Draw, save, and reload rectangular, elliptical, and polygon masks on individual slices for reproducible region-of-interest analysis.

Semi-Quantitative Mapping

Generate three parametric NIfTI maps — RCE, RCEmax, and Time-to-RCEmax — to characterise contrast enhancement dynamics.

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Key features

  • PyQt6 GUI — A native desktop interface with a resizable three-panel layout: slice selector (left), main canvas (centre), intensity graph (right).
  • Flexible data loading — Supports single NIfTI files, BIDS-compliant datasets with automatic multi-subject detection, reloading of previously processed files, and Bruker raw data conversion.
  • Pixel intensity curves — Click any voxel on the main canvas to display its full signal-intensity-over-time curve in the right panel, with coordinate and intensity-increase logging.
  • Denoising and artefact removal — Preprocessing menu provides selectable denoising filters and optional Gibbs artefact suppression via dipy.
  • ROI tools — Rectangle, ellipse, and polygon selectors backed by Matplotlib; masks can be saved as NIfTI files and reloaded in future sessions.
  • Semi-quantitative parametric maps — Three output maps saved as compressed NIfTI files:
    • RCE (rce_process.nii.gz) — Relative Contrast Enhancement across all time points.
    • RCEmax (rce_max_process.nii.gz) — Maximum Relative Contrast Enhancement per voxel.
    • Time-to-RCEmax (tto_rce_max_process.nii.gz) — The time point at which peak enhancement occurs.
  • Interactive visualisation — Pan, zoom, and custom colormaps (including jet applied automatically to processed maps) for detailed inspection.
  • Keyboard shortcuts — Full shortcut coverage for slice navigation, time-frame stepping, movie mode playback, ROI modes, zoom/pan, and fullscreen.
  • Resizable panels — All three panels can be dragged and resized; layout can be reset to defaults with a single key press.

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