Tutorials#
The main purpose of the PEPT library is to provide a common, consistent foundation for PEPT-related algorithms, including tracer tracking, visualisation and post-processing tools - such that they can be used interchangeably, mixed and matched for any PEPT camera and system. Virtually all PEPT processing routine follows these steps:
Convert raw gamma camera / scanner data into 3D lines (i.e. the captured gamma rays, or lines of response - LoRs).
Take a sample of lines, locate tracer locations, then repeat for the next samples.
Separate out individual tracer trajectories.
Visualise and post-process trajectories.
For these algorithm-agnostic steps, PEPT provides five base data structures upon which the rest of the library is built:
pept.LineData
: general 3D line samples, formatted as [time, x1, y1, z1, x2, y2, z2, extra…].pept.PointData
: general 3D point samples, formatted as [time, x, y, z, extra…].pept.Pixels
: single 2D pixellised space with physical dimensions, including fast line traversal.pept.Voxels
: single 3D voxellised space with physical dimensions, including fast line traversal.
For example, once you convert your PEPT data - from any scanner - into
pept.LineData
, all the algorithms in this library can be used.
All the data structures above are built on top of NumPy and integrate natively with the rest of the Python / SciPy ecosystem. The rest of the PEPT library is organised into submodules:
pept.scanners
: converters between native scanner data and the base data structures.pept.tracking
: radioactive tracer tracking algorithms, e.g. the Birmingham method, PEPT-ML, FPI.pept.plots
: PEPT data visualisation subroutines.pept.utilities
: general-purpose helpers, e.g.read_csv
,traverse3d
.pept.processing
: PEPT-oriented post-processing algorithms, e.g.VectorField3D
.
If you are new to the PEPT library, we recommend going through this interactive online notebook, which introduces all the fundamental concepts of the library:
Once you get the idea of LineData
samples, Pipeline
and
PlotlyGrapher
, you can use these copy-pastable tutorials to build PEPT data
analysis pipelines tailored to your specific systems.
Pre-processing
Tracking