3D Dose Reconstruction Research Articles

3D Dose Reconstruction to Insure Correct External Beam Treatment of Patients

Radiation therapy treatments have become increasingly more complicated. There are multiple opportunities for humans, machines, software, and combinations thereof to result in a treatment error that could be of significance. Current methods for quality assurance are often abstract in nature and may have unclear underlying assumptions as to what is assumed to be working correctly, or may depend upon the diligence of persons to discover errors from a review of the treatment plan. Here, an example will be shown of a direct method to reconstruct and demonstrate the dose and the dose distribution delivered to a particular patient. By measuring the radiation fields that come out of the accelerator, and using the measurement as input to a 3-dimensional (3D) dose algorithm, the delivered patient dose is determined and presented in a manner similar to the treatment plan. The intended treatment plan dose may be directly compared. Using this feedback mechanism, there is less abstraction and dependence upon the diligence of individuals checking multiple steps in a treatment process, and assumptions can be clearly stated. With this system, the dose is determined and presented minimizing assumptions and dependence upon other systems.

3D dose reconstruction for clinical evaluation of IMRT pretreatment verification with an EPID

Background and purpose Pretreatment verification with an electronic portal imaging device is an important part of our patient-specific quality assurance program for advanced treatment techniques. Up to now, this verification has been performed for over 400 IMRT patient plans. For every treatment field, a 2D portal dose image (PDI) is measured and compared with a predicted PDI. Often it is not straightforward to interpret dose deviations found in these 2D comparisons in terms of clinical implications for the patient. Therefore, a method to derive the 3D patient dose based on the measured PDIs was implemented. Methods and materials For reconstruction of the 3D patient dose, the actual fluences delivered by the accelerator are derived from measured portal dose images using an iterative method. The derived fluence map for each beam direction is then used as input for the treatment planning system to generate an adapted 3D patient dose distribution. The accuracy of this method was assessed by measurements in a water phantom. Clinical evaluation of the 3D dose reconstruction was performed for 17 IMRT patients with different tumor sites. Dose differences with respect to the original treatment plan were evaluated in individual CT slices using dose difference maps and a 3D γ analysis and by comparing dose-volume histograms (DVHs). Results The measurements indicated that the accuracy of the 3D dose reconstruction was within 2%/2 mm. For the patients observed dose differences with respect to the original plan were generally within 2%, except at the field edges and in the sharp dose gradients around the planning target volume (PTV). Gamma analysis showed that the dose differences were within 2%/2 mm for more than 95% of the points in all cases. Differences in DVH parameters for the PTV and organs at risk were also within 2% in nearly all cases. Conclusion A method to derive actual delivered fluence maps from measured PDIs and to use them to reconstruct the 3D patient dose was implemented. The reconstruction eases the estimation of the clinical relevance of observed dose differences in the pretreatment measurements.

TH‐D‐VaIB‐03: Unified Algorithm for KV and MV Scatter and Beam‐Hardening Correction Using the Convolution‐Superposition Method

Purpose: Quantitative cone beam CT (CBCT) is essential for advanced radiation oncology (RO) applications such as portal image‐based 3D dose reconstruction. Quantitative CT requires accurate modeling of scatter, beam‐hardening and detector response. Scatter correction methods are typically semi‐empirical in nature and are designed to reduce visible artifacts while incurring low computational cost. In contrast, Monte Carlo (MC) methods are accurate but impractically slow. Convolution‐superposition (CS) scatter models offer a good balance between accuracy and computational complexity. We show how CS can be employed to implement a unified correction method that enables quantitative kV and MV imaging. Method and Materials: (1) We perform detailed MC modeling of the kV and MV cone beam imaging systems. (2) Using MC, we generate calibration data that map intensities recorded on the flat panel imagers to water‐equivalent thicknesses (WETs). (3) The MC models are used to generate pencil beam kernels for water cylinders of varying thickness. (4) Scattergrams are generated from acquired projection images via the CS method using these kernels indexed by the WET at each pixel. (5) Scattergrams are iteratively refined using a multiplicative correction formula that ensures that the estimated primary image remains non‐negative even when scatter‐to‐primary ratios are very high. (6) The FDK reconstruction algorithm is applied directly to the thickness maps corresponding to the estimated primary images. Results: The algorithm is able to reduce maximum non‐uniformity in the reconstruction of a 16cm cylindrical homogeneous tissue equivalent phantom from 11.7% to 1.5%. When applied to a challenging 35cm × 22.5cm oblong water phantom, a non‐uniformity reduction in from 36% to 2.5% is achieved. A dataset of 200 1024×1024 projections can be processed in 25 seconds. Conclusions: CS methods can be used at both kV and MV energies to enable reconstruction of quantitative CBCT images. Conflict of Interest: Supported by Siemens.

WE-E-ValA-07: 3D Dose Reconstruction with Megavoltage Cone-Beam CT and EPID Exit Dosimetry

Purpose: The ability to reconstruct the delivered patient dose can help ensure that the integrated doses to important structures faithfully adhere to prescription. The objective of this work is to develop and test a 3D dose reconstruction procedure based on exit‐dose measurements at treatment time and MV cone‐beam CT.Methods and Materials: The proposed dose reconstruction method uses a Megavoltage cone‐beam computed tomography (MVCBCT) image acquired on the treatment table prior to treatment, 2D portal images taken with an amorphous‐silicon electronic portal imaging device(EPID) during treatment, and an independent validated dose calculation engine. The energy fluence obtained from the EPID is back‐projected through the 3D MVCBCT image. A dose calculation engine based on a collapsed‐cone convolution algorithm subsequently calculates the dose in each voxel. To test the model, a MVCBCT of a cylindrical solid‐water QA phantom was acquired and the MVCBCT numbers mapped to appropriate attenuation coefficients. The phantom was then treated with a 5cmx5cm beam and a portal image acquired. During the treatment, a CC13 ion chamber and MOSFETdetectors were used to measure the dose at 21 points to compare with reconstructed dose. A Pinnacle dose calculation using a conventional CT was also performed for comparison. Results: The mean difference between reconstructed and measured doses was −0.2% (standard deviation = 2.8%). The reconstructed dose in the inner regions of the cylinder differed less than 2% from the measured, although discrepancies of about 10% occurred at one point in the buildup region and at two other peripheral points. In comparison, the mean difference between Pinnacle calculations and measurements was −2.9% (standard deviation =1.6%). Conclusion: Preliminary calculations of reconstructed dose demonstrated good agreement with experiments. Further refinement of the model and its application to clinical conditions are under investigation. Conflict of Interest: Research supported by Siemens.

SU‐FF‐T‐116: Calibration of the Perkin Elmer AG9 Flat Panel Portal Imager for Exit Dosimetry

Purpose: The new and highly sensitive amorphous‐silicon AG9 flat panel portal imager by Perkin Elmer allows imaging with extremely low doses. The purpose of this work is to establish the dosimetric response of the panel. Dosimetric calibration of the detector is also a necessary step for a dose reconstruction program under investigation. Methods and Materials: A convolution model was developed to map the flat panel images to equivalent dose in water at a depth of 1.5cm. The three model parameters are the flat‐panel and water energy‐deposition kernels, the flat‐panel‐to‐water‐dose conversion function, and the pixel sensitivity matrix. They account for differing flat‐panel and water energy responses, field size effects, and the panel's pixel nonuniformity. To determine these parameters, identical setups with the flat panel and with a water phantom were used to carry out numerous measurements. To validate the model, 10cm and 5cm square fields were delivered through 16cm of solid‐water slabs and a cylindrical solid‐water QA phantom. The flat‐panel converted dose profiles were compared with that measured in the Wellhofer water tank. Results: Both the flat‐panel and water kernels decrease sharply with distance. The magnitude of the flat‐panel kernel was greater, possibly due to the additional scatter in the high‐density panel material. The conversion function varies with the off‐axis distance and is nearly linear as the attenuation thickness varies. The sensitivity matrix values were approximately within 1% of unity, demonstrating good pixel uniformity of the panel. Validation tests showed that the model‐extracted dose agrees with measurements to within 1%. Conclusion: A good understanding of the dosimetric response of the AG9 flat panel was achieved. The developed convolution model was shown to be accurate and suitable for generating energy‐fluence maps for 3D dose reconstruction. Conflict of Interest: Research supported by Siemens.

SU‐FF‐J‐115: Registering the Planning CT to the Treatment Geometry in IGRT, Using a Limited Reconstructed Volume Derived From Planar Images

A set of megavoltage (MV) and kilovoltage (kV) images are obtained during a patient's image‐guided radiotherapy treatment (IGRT). The kV images are acquired prior to treating the fields using an on‐board x‐ray imager. However, The MV images can be acquired both prior and during the actual treatments. Fan‐beam reconstruction of back‐projected kV images obtained at four or more gantry angles can be used to orient the planning CT to match the treatment geometry. Registering the planning CT to the treatment geometry is necessary if one is to perform dose reconstruction using the multi‐planar images and the planning CT, in the absence of a cone beam CT. The MV and kV images provide the exit fluence information for 3D dose reconstruction. Using these 2D treatment images along with the 3D planning CT volume provides adequate information about the treatment anatomy that is needed for the dose reconstruction process. This system might alleviate the need for cone beam CT for patient positioning and/or dose reconstruction. The planar treatment images provide adequately sampled sinogram planes in the spatial dimension, but not in the radial direction. Nonetheless, there is enough data in the sinogram space for registering the treatment geometry to the planning CT. Canny edge finding method is used to decipher anatomical structure in the limited reconstructed volume as well as in the CT volume. This technique can detect both strong and weak edges; and it is less likely than the other edge finding techniques to be fooled by noise. The resulting edge maps are registered to each other by affine transformations. The transformation matrices are then applied to the CT volume in order to register it to the treatment geometry.

Three Dimensional Dose Reconstruction IMRT Verification using an EPID and the Convolution Superposition Algorithm

Purpose/Objective: IMRT is becoming more common in radiation oncology fields which in turn is making heavier demand for the patient specific QA. Authors developed a practical and reliable IMRT QA verification tool that uses intensity maps measured using an electronic portal imaging device to perform a fully 3D dose reconstruction.

Experimental verification of a portal dose prediction model

Electronic portal imaging devices (EPIDs) can be used to measure a two-dimensional (2D) dose distribution behind a patient, thus allowing dosimetric treatment verification. For this purpose we experimentally assessed the accuracy of a 2D portal dose prediction model based on pencil beam scatter kernels. A straightforward derivation of these pencil beam scatter kernels for portal dose prediction models is presented based on phantom measurements. The model is able to predict the 2D portal dose image (PDI) behind a patient, based on a PDI without the patient in the beam in combination with the radiological thickness of the patient, which requires in addition a PDI with the patient in the beam. To assess the accuracy of portal dose and radiological thickness values obtained with our model, various types of homogeneous as well as inhomogeneous phantoms were irradiated with a 6 MV photon beam. With our model we are able to predict a PDI with an accuracy better than 2% (mean difference) if the radiological thickness of the object in the beam is symmetrically situated around the isocenter. For other situations deviations up to 3% are observed for a homogeneous phantom with a radiological thickness of 17 cm and a 9 cm shift of the midplane-to-detector distance. The model can extract the radiological thickness within 7 mm (maximum difference) of the actual radiological thickness if the object is symmetrically distributed around the isocenter plane. This difference in radiological thickness is related to a primary portal dose difference of 3%. It can be concluded that our model can be used as an easy and accurate tool for the 2D verification of patient treatments by comparing predicted and measured PDIs. The model is also able to extract the primary portal dose with a high accuracy, which can be used as the input for a 3D dose reconstruction method based on back-projection.

342 oral Integrated verification of patient set-up and delivered IMRT fluence for daily 3D dose reconstruction

342 oral Integrated verification of patient set-up and delivered IMRT fluence for daily 3D dose reconstruction