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:

1. Convert raw gamma camera / scanner data into 3D lines (i.e. the captured gamma rays, or lines of response - LoRs).

2. Take a sample of lines, locate tracer locations, then repeat for the next samples.

3. Separate out individual tracer trajectories.

4. 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:

1. pept.LineData: general 3D line samples, formatted as [time, x1, y1, z1, x2, y2, z2, extra…].

2. pept.PointData: general 3D point samples, formatted as [time, x, y, z, extra…].

3. pept.Pixels: single 2D pixellised space with physical dimensions, including fast line traversal.

4. 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:

1. pept.scanners: converters between native scanner data and the base data structures.

2. pept.tracking: radioactive tracer tracking algorithms, e.g. the Birmingham method, PEPT-ML, FPI.

3. pept.plots: PEPT data visualisation subroutines.

4. pept.utilities: general-purpose helpers, e.g. read_csv, traverse3d.

5. pept.processing: PEPT-oriented post-processing algorithms, e.g. VectorField3D.

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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:

https://colab.research.google.com/drive/1G8XHP9zWMMDVu23PXzANLCOKNP_RjBEO?usp=sharing

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

• Absolute Basics
  • pept.LineData
• Saving / Loading Data
• Plotting
  • Interactive 3D Plots
  • Adding Colourbars
  • Histogram of Tracking Errors
  • Exporting Plotly Graphs as Images
  • Modifying the Underlying Figure
• Initialising PEPT Scanner Data
  • ADAC Forte
  • Parallel Screens
  • Modular Camera

Tracking

• Adaptive Sampling
• The Birmingham Method
  • Birmingham Method recipe
  • Recipe with Trajectory Separation
• PEPT-ML
  • PEPT-ML one pass of clustering recipe
  • PEPT-ML second pass of clustering recipe
  • PEPT-ML complete recipe
  • Example of a Complex Processing Pipeline
• Feature Point Identification
  • FPI Recipe

Post-processing

• Tracking Errors
  • Histogram of Tracking Errors
• Trajectory Separation
  • Segregate Points
• Filtering Data
  • Remove
  • Condition
  • SamplesCondition
  • GroupBy
  • RemoveStatic
• Extracting Velocities
• Interpolating Timesteps