Estimation of respiratory phases during proton radiotherapy from a 4D-CT and Prompt gamma detection profiles

Abstract

Proton radiotherapy has a potential to provide an effective cancer treatment while sparing greater volume of healthy tissue than the conventional x-ray based radiotherapy. However, in lungs this potential is hindered by motion due to breathing. An important quantity in treatment verification is the correlation between the breathing phases (BP) and the timing of pencil beam scanning (PBS). In this note, we demonstrate how the BP can be estimated using Prompt gamma (PG) detection profiles collected during a treatment. For our analysis, we utilized a 4D-CT of a patient with lung cancer, a treatment plan and a PG simulator. The treatment plan consisted of ten layers corresponding to ten proton energies. The ten BPs of the 4D-CT were interpolated using a deformable registration algorithm, so as to have fifty BPs in total. Deviations from regular breathing was introduced via time dependent frequency modulation. Fifty unique breathing patterns were generated, for which PG profiles were simulated for each pencil beam. In order to simulate the photon statistics, Poisson noise was added to each PG profile. We also computed noiseless PG profiles for each pencil beam passing through each of the fifty BPs. The BPs were estimated by comparing the two sets of PG profiles via three different methods: the BP associated with each layer was estimated 1) independently of the other layers, 2) using a linear correlation between the layers, and 3) using a quadratic correlation between the layers. The best model, the quadratic model, yielded an average error in BP estimation relative to the breathing period of 5% of the breathing period or less with a 90% confidence interval.

The use of dynamic CT imaging for tracking mandibular movements in a phantom

“…The clinical use of 4D CT scanning has seen a vast increase in the last two decades, with investigation and visualization opportunities ranging from cardiac [6,7] and lung [8] applications and from research in the central nervous system [7,9,10,11] to dynamic MSK applications in several joints (lower limb [12], wrist [13,14,15]) and many other fields [16]. In addition to providing 3D morphological information, this method allows clinicians and radiologists to evaluate multiple timeframes and thus the motion of the inspected structures.…”