Patient-specific scatter correction in clinical cone beam computed tomography imaging made possible by the combination of Monte Carlo simulations and a ray tracing algorithm

Figures & data

Figure 1. Example CBCT projection images of a brain (A, D), thorax (B, E) and pelvis (C, F) scan. Primary images (A–C) were calculated using the ray tracing algorithm, and have been log transformed. Scatter images (D–E) were calculated by MC simulations, and the images show air kerma calculated at the detector plane. Histograms (G–I) were calculated from both the primary and scatter signal, and normalised to unit area in the displayed air kerma range. For the brain scan, the primary photons in the air region surrounding the head gave rise to a signal in the histogram at higher air kerma values than shown in (G).

Table I. Scatter simulation time.

	Brain CBCT	Thorax CBCT	Pelvis CBCT
Nhist	5 × 10^4	5 × 10^4	10^5
σmax	1.3%	1.5%	2.0%
TCPU	23 min	50 min	120 min

Simulated number of histories (N_hist), highest statistical uncertainty (σ_max) and CPU time (T_CPU) for the MC simulations of CBCT scatter distributions.

Figure 2. CBCT images reconstructed from the calculated brain (A–C), thorax (D–F) and pelvis (G–I) scans. Reconstructions are based on projection images calculated in steps of 1.25°, and reconstructed using an FDK type algorithm. For each scan, three images are reconstructed. The first reconstruction is based on the total signal from both primary and scattered photons (A, D, G). The second is based on a constant scatter subtraction of 20% in each projection image prior to reconstruction (B, E, H), and the third is based on the primary photons only (C, F, I). The displayed CBCT slices are examples, and not chosen centrally from the datasets.