When processing more difficult datasets - scattering environments, low tracer activities, etc. - it is often useful to use some tracer statistics to remove erroneous locations.
Most PEPT algorithms will include some measure of the tracer location errors, for example:
Centroids(error = True)filter appends a column “error” representing the standard deviation of the distances from the computed centroid to the constituent points. For a 500 mm scanner, a spread in a tracer location of 100 mm is clearly an erroneous point.
Centroids(cluster_size = True)filter appends a column “cluster_size” representing the number of points used to compute the centroid. If a sample of 200 LoRs yields a tracer location computed from 5 points, it is clearly noise.
BirminghamMethodfilter includes a column “error” representing the standard deviation of the distances from the tracer position to the constituent LoRs.
Histogram of Tracking Errors#
You can select a named column via string indexing, e.g.
trajectories["error"]; you can
then plot a histogram of the relative errors with:
import plotly.express as px px.histogram(trajectories["error"]).show() # Large values are noise px.histogram(trajectories["cluster_size"]).show() # Small values are noise
It is often useful to remove points with an error higher than a certain value, e.g. 20 mm:
trajectories = Condition("error < 20").fit(trajectories) # Or simply append the `Condition` to the `pept.Pipeline` pipeline = pept.Pipeline([ ... Condition("cluster_size > 30, error < 20"), ... ])