Machine Learning Features from Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

Extracted features from the simulation data found at DOI: 10.5281/zenodo.7951680 Each simulation constitutes a single data sample. The following features were extracted. Detector features: Total number of active pixels Total number of clusters (hits) Number of clusters over threshold (5, 20 pixels) Mean and standard deviation of cluster sizes The number of clusters of any given size (1–72) Mean and standard deviation of x- and y-coordinates over each layer (0–42), and the entire detector Number of active pixels in each layer (0–42) Number of clusters (hits) in each layer (0–42) Total energy deposition of the hits in each layer (0–42) Higher-level detector features, i.e., function fits (linear, cubic, exponential) with their mean squared residuals over the following quantities: Active pixels over layer Number of clusters over layer Total deposited energy over layer 201 RSP features extracted from the beam spot, the phantom rotation, and its 3D RSP image (Giacometti et al. 2017). After extracting features, 14 samples were determined as outliers and removed. The rest of the samples were split into train (70%), validation (10%), and test (20%) sets, which can be found in separate CSV files (features_train.csv, features_val.csv, features_test.csv). The last file (features_shifted_test.csv) contains 40 additional samples for each data point in the test set, representing a simulated lateral shift between 1 mm and 10 mm in 1 mm intervals in all directions along the x- and y-axis of the beam.