Figure 1
An illustration of the conceptual NOVCoDA design and the analysis workflow. (A) A virtual 3D patient model (a patient with non-small cell lung cancer) is imported into the MC simulation environment. (B) A mono-energetic proton pencil beam is then directed at the target volume. (C) Secondary FNs and PGs are tracked from their points of origin, through the patient geometry and the sensitive volume of the NOVCoDA. Also shown is the conceptual design of the NOVCoDA. (D) To enable kinematic reconstruction of event cones, triple and double coincidences are required for PGs and FNs, respectively. Event cones are back projected onto a plane coinciding with the proton beam axis. The back projection is followed by image reconstruction resulting in 2D FN and PG production distributions. (E) The 2D production distributions are projected onto a single dimension resulting in 1D histograms of FN and PG distributions as a function of depth in the patient geometry, i.e. along the primary proton beam direction. (F) The 1D distributions are then used to calculate a range landmark (RL) parameter that can be related to the primary proton beam range and thereby potential range shifts. (G) A bootstrapping procedure combined with a Gaussian Naive Bayes classifier is implemented to estimate the distributions of the calculated RLs and quantify range-shift-resolving capabilities of the NOVCoDA.
