Abstract

The aim of this work is to demonstrate the feasibility of using water‐equivalent thickness (WET) and virtual proton depth radiographs (PDRs) of intensity corrected cone‐beam computed tomography (CBCT) to detect anatomical change and patient setup error to trigger adaptive head and neck proton therapy. The planning CT (pCT) and linear accelerator (linac) equipped CBCTs acquired weekly during treatment of a head and neck patient were used in this study. Deformable image registration (DIR) was used to register each CBCT with the pCT and map Hounsfield units (HUs) from the planning CT (pCT) onto the daily CBCT. The deformed pCT is referred as the corrected CBCT (cCBCT). Two dimensional virtual lateral PDRs were generated using a ray‐tracing technique to project the cumulative WET from a virtual source through the cCBCT and the pCT onto a virtual plane. The PDRs were used to identify anatomic regions with large variations in the proton range between the cCBCT and pCT using a threshold of 3 mm relative difference of WET and 3 mm search radius criteria. The relationship between PDR differences and dose distribution is established. Due to weight change and tumor response during treatment, large variations in WETs were observed in the relative PDRs which corresponded spatially with an increase in the number of failing points within the GTV, especially in the pharynx area. Failing points were also evident near the posterior neck due to setup variations. Differences in PDRs correlated spatially to differences in the distal dose distribution in the beam's eye view. Virtual PDRs generated from volumetric data, such as pCTs or CBCTs, are potentially a useful quantitative tool in proton therapy. PDRs and WET analysis may be used to detect anatomical change from baseline during treatment and trigger further analysis in adaptive proton therapy.PACS number(s): 87.55‐x, 87.55.‐D, 87.57.Q‐

Highlights

  • 428 Wang et al.: Virtual proton depth radiography for adaptive proton therapy photon therapy,(1) it is necessary to find a quick way to identify and quantify these changes and integrate them into the image guidance or adaptive therapy workflow during proton therapy.Cone-beam computed tomography (CBCT) facilitates the visualization of the patient’s anatomy in the treatment position

  • A. planning CT (pCT) and CBCT data CBCT for proton therapy is still in development at several institutions and not available clinically at the time of this study, linac CBCTs of one head and neck patient diagnosed with stage IVA malignant neoplasm of the base of tongue and treated with intensity-modulated radiation therapy was used to demonstrate the proof of principle in this work

  • This was presumably due to larger anatomic differences between the pCT and the CBCT and, larger extent of deformation required, which may contribute to larger registration errors

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Summary

Introduction

428 Wang et al.: Virtual proton depth radiography for adaptive proton therapy photon therapy,(1) it is necessary to find a quick way to identify and quantify these changes and integrate them into the image guidance or adaptive therapy workflow during proton therapy.Cone-beam computed tomography (CBCT) facilitates the visualization of the patient’s anatomy in the treatment position. Compared to multislice fan-beam CT, the Hounsfield units (HUs) in a CBCT image are highly sensitive to the effects of beam hardening and X-ray scatter within the patient. Since the proton stopping power is calibrated to the HU,(2) a high degree of HU accuracy is desirable to minimize proton range uncertainty. The HU accuracy of CBCT technology currently is not accurate enough to be used directly for calculation of the water-equivalent thickness (WET) or resultant proton dose distribution. Changes in scatter conditions due to a 1 cm increase in the radius of a phantom can result in 11% decrease of the HU for water[3] and a corresponding 5% change in calculated WET, which is much larger than the typical proton range uncertainty margin of at least 2.5%–3.5%(4) used in proton planning

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