Abstract

The purpose of this investigation was the creation of a new protocol allowing more precise dose calculations on megavoltage CT (MVCT) images for tomotherapy, both for adaptive and StatRT planning. Daily MVCT images offer, next to positioning purposes, the possibility for daily dose check and adaptive planning. Dose calculations use the image value to density table (IVDT) to calculate physical densities from Hounsfield Units (HUs). These measured HUs change over time, leading to a dose calculation error. We noticed dose calculation variations due to IVDT changes of: 0.2% dose during a day, up to 1.6% dose from long‐term variations, and up to 1.5% dose due to technical interventions. An analysis was performed applying the general methodology of a calibration problem. A model HU=bρc‐1020 was obtained using a weighted least squares inverse prediction method (HU as function of density) taking into account the heteroscedasticity. The b parameter is the major variable and depends also on the dose rate (DR). We demonstrate the correction for DR variations and the constance of the c parameter. Instead of scanning the whole tissue characterization phantom daily, we propose a simplified daily protocol: (a) morning airscan‐like procedure with only two inserts on the table (defining b and thus the IVDT curve), (b) DR variations throughout the day can be corrected for using the DR model. A patient‐specific protocol for which two inserts next to the patient are scanned could also be used, but results in equal uncertainties and is less practical. Therefore we recommend the morning procedure with dose rate variation correction. Applying the proposed transformations and the model, the correct IVDT of the moment can be reconstructed, with a simple measurement in the morning, and corrected with DR changes during the day. This corresponds with a linear mapping in time of the proposed IVDT function. The dosimetric variation is hereby reduced from up to 3% to 0.4 % for the tested pelvic and head‐and‐neck cases. In practice, several IVDT curves corresponding to “b” values can be entered. The correct IVDT curve of that moment can then be chosen from the list. Instead of the two high‐density inserts on table, any calibrated single density phantom could be used in order to create the IVDT curve of the day, but it should have a larger size than the current inserts.PACS number: 87.55.Gh

Highlights

  • 242 Crop et al.: Tomotherapy megavoltage CT (MVCT) dose calculations (HU) into mass densities.[4,5,6] These densities are used for dose calculations. ­there is an important variation in time for the Hounsfield Units (HUs) values and the image value to density table (IVDT).[7,8,9] If the recalculated densities are incorrect, a dosimetric uncertainty is induced

  • Up to 3% dose differences were observed due to dose rate instabilities[7] and important changes were observed after a target change.[9]. Incorrect decisions regarding replanning a patient can be associated to this uncertainty on the IVDT and resulting dose calculations

  • We investigate two possible protocols to improve calculations on MVCT images: 1. Preparation: (a) Measure HU values in phantom and calculate the b and c parameter of the response curve HU = b * ρc − 1020 for the machine

Read more

Summary

Introduction

242 Crop et al.: Tomotherapy MVCT dose calculations (HU) into mass densities.[4,5,6] These densities are used for dose calculations. ­there is an important variation in time for the HU values and the IVDT.[7,8,9] If the recalculated densities are incorrect, a dosimetric uncertainty is induced. The system calculates densities from the HUs, but the uncertainties are linked to the HUs. The system calculates densities from the HUs, but the uncertainties are linked to the HUs This comes down to the famous ‘calibration problem’ in which the problem is first inversed: a model is created to calculate HUs from densities using weighted least squares (WLS) linear regression (inverse prediction using uncertainty per data point). This model is inversed to obtain a practical model.[10]

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call