Abstract
PurposeProton CT (pCT) has the ability to reduce inherent uncertainties in proton treatment by directly measuring the relative proton stopping power with respect to water, thereby avoiding the uncertain conversion of X‐ray CT Hounsfield unit to relative stopping power and the deleterious effect of X‐ ray CT artifacts. The purpose of this work was to further evaluate the potential of pCT for pretreatment positioning using experimental pCT data of a head phantom.MethodsThe performance of a 3D image registration algorithm was tested with pCT reconstructions of a pediatric head phantom. A planning pCT simulation scan of the phantom was obtained with 200 MeV protons and reconstructed with a 3D filtered back projection (FBP) algorithm followed by iterative reconstruction and a representative pretreatment pCT scan was reconstructed with FBP only to save reconstruction time. The pretreatment pCT scan was rigidly transformed by prescribing random errors with six degrees of freedom or deformed by the deformation field derived from a head and neck cancer patient to the pretreatment pCT reconstruction, respectively. After applying the rigid or deformable image registration algorithm to retrieve the original pCT image before transformation, the accuracy of the registration was assessed. To simulate very low‐dose imaging for patient setup, the proton CT images were reconstructed with 100%, 50%, 25%, and 12.5% of the total number of histories of the original planning pCT simulation scan, respectively.ResultsThe residual errors in image registration were lower than 1 mm and 1° of magnitude regardless of the anatomic directions and imaging dose. The mean residual errors ranges found for rigid image registration were from −0.29 ± 0.09 to 0.51 ± 0.50 mm for translations and from −0.05 ± 0.13 to 0.08 ± 0.08 degrees for rotations. The percentages of sub‐millimetric errors found, for deformable image registration, were between 63.5% and 100%.ConclusionThis experimental head phantom study demonstrated the potential of low‐dose pCT imaging for 3D image registration. Further work is needed to confirm the value pCT for pretreatment image‐guided proton therapy.
Highlights
Proton therapy provides superior dose distributions in the low to intermediate dose range compared to photon therapy, which may lead to improved outcomes for some types of cancer and reduced side effects.[1,2,3] Uncertainties in patient positioning and beam range as well as internal changes of tumor and patient anatomy could, compromise treatment effectiveness.[4]
A planning Proton CT (pCT) simulation scan of the phantom was obtained with 200 MeV protons and reconstructed with a 3D filtered back projection (FBP) algorithm followed by iterative reconstruction and a representative pretreatment pCT scan was reconstructed with FBP only to save reconstruction time
This experimental head phantom study demonstrated the potential of low‐dose pCT imaging for 3D image registration
Summary
Proton therapy provides superior dose distributions in the low to intermediate dose range compared to photon therapy, which may lead to improved outcomes for some types of cancer and reduced side effects.[1,2,3] Uncertainties in patient positioning and beam range as well as internal changes of tumor and patient anatomy could, compromise treatment effectiveness.[4] efforts to develop and improve treatment planning accuracy and image guidance for proton therapy are ongoing.[5,6] Currently, for treatment planning in proton therapy, an X‐ray CT dataset of the patient is acquired and Hounsfield units of the scan are converted to relative stopping power (RSP) This conversion is one important source for range uncertainties, which are typically estimated on the order of 3–5% of the planned proton range.[7] Replacing X‐ray planning CT with proton CT (pCT) planning CT simulations with individual proton tracking during the scan has been proposed as a low‐dose method to reduce this planning uncertainty; pretreatment pCT would provide a method for pretreatment verification of correct patient setup and RSP distribution. This method is currently in the preclinical stage of its development.[8,9,10]
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