A Chvetsov

Tumor Localization Using Cone-Beam CT Reduces Setup Margins in Conventionally Fractionated Radiotherapy for Lung Tumors

PURPOSE To determine whether setup margins can be reduced using cone-beam computed tomography (CBCT) to localize tumor in conventionally fractionated radiotherapy for lung tumors. METHODS AND MATERIALS A total of 22 lung cancer patients were treated with curative intent with conventionally fractionated radiotherapy using daily image guidance with CBCT. Of these, 13 lung cancer patients had sufficient CBCT scans for analysis (389 CBCT scans). The patients underwent treatment simulation in the BodyFix immobilization system using four-dimensional CT to account for respiratory motion. Daily alignment was first done according to skin tattoos, followed by CBCT. All 389 CBCT scans were retrospectively registered to the planning CT scans using automated soft-tissue and bony registration; the resulting couch shifts in three dimensions were recorded. RESULTS The daily alignment to skin tattoos with no image guidance resulted in systematic (Sigma) and random (sigma) errors of 3.2-5.6 mm and 2.0-3.5 mm, respectively. The margin required to account for the setup error introduced by aligning to skin tattoos with no image guidance was approximately 1-1.6 cm. The difference in the couch shifts obtained from the bone and soft-tissue registration resulted in systematic (Sigma) and random (sigma) errors of 1.5-4.1 mm and 1.8-5.3 mm, respectively. The margin required to account for the setup error introduced using bony anatomy as a surrogate for the target, instead of localizing the target itself, was 0.5-1.4 cm. CONCLUSION Using daily CBCT soft-tissue registration to localize the tumor in conventionally fractionated radiotherapy reduced the required setup margin by up to approximately 1.5 cm compared with both no image guidance and image guidance using bony anatomy as a surrogate for the target.

Evaluation of kV cone-beam ct performance for prostate IGRT: a comparison of automatic grey-value alignment to implanted fiducial-marker alignment.

Purpose: Cone-beam computed tomography (CBCT) is a new image-guided radiation therapy (IGRT) technique for patient alignment in radiotherapy. The CBCT x-ray volume imaging system from Elekta allows for a variety of alignment methods. The aim of this study is to assess the accuracy of soft-tissue-based automatic alignment as compared with manual alignment using intraprostatic fiducials. Methods and Materials: All patients were treated on an Elekta Synergy S linear accelerator with kilovoltage CBCT. All alignments were performed using the x-ray volume imaging system and associated software. Automatic alignment with gray-value-based registration and manual alignment to fiducial markers were performed. Transitional corrections along each axis as well as 3-dimensional vectors were compared with evaluate the accuracy of gray-value-based registration compared with fiducials. Results: The distribution of the 3-dimensional vectors between gray-value and fiducial registrations demonstrated notable differences. The mean summed vector was 0.75 cm, with a standard deviation (SD) of 0.52 cm and range from 0.04 to 2.06 cm. There was minimal difference along the lateral direction, with a mean ± SD of −0.02 cm ± 0.13 cm. However, there were large discrepancies along the superior-inferior and anterior-posterior direction alignments, with mean ± SD values of −0.55 ± 0.48 cm and −0.31 ± 0.43 cm, respectively. Conclusions: CBCT with soft-tissue-based automatic corrections is not an accurate alignment compared with manual alignment to fiducial markers for prostate IGRT. We have concluded that a daily manual alignment to fiducials is one of the most reliable methods to maintain accuracy in prostate IGRT.

An image quality comparison study between XVI and OBI CBCT systems

The purpose of this study is to evaluate and compare image quality characteristics for two commonly used and commercially available CBCT systems: the X‐ray Volumetric Imager and the On‐Board Imager. A commonly used CATPHAN image quality phantom was used to measure various image quality parameters, namely, pixel value stability and accuracy, noise, contrast to noise ratio (CNR), high‐contrast resolution, low contrast resolution and image uniformity. For the XVI unit, we evaluated the image quality for four manufacturer‐supplied protocols as a function of mAs. For the OBI unit, we did the same for the full‐fan and half‐fan scanning modes, which were respectively used with the full bow‐tie and half bow‐tie filters. For XVI, the mean pixel values of regions of interest were found to generally decrease with increasing mAs for all protocols, while they were relatively stable with mAs for OBI. Noise was slightly lower on XVI and was seen to decrease with increasing mAs, while CNR increased with mAs for both systems. For XVI and OBI, the high‐contrast resolution was approximately limited by the pixel resolution of the reconstructed image. On OBI images, up to 6 and 5 discs of 1% and 0.5% contrast, respectively, were visible for a high mAs setting using the full‐fan mode, while none of the discs were clearly visible on the XVI images for various mAs settings when the medium resolution reconstruction was used. In conclusion, image quality parameters for XVI and OBI have been quantified and compared for clinical protocols under various mAs settings. These results need to be viewed in the context of a recent study that reported the dose‐mAs relationship for the two systems and found that OBI generally delivered higher imaging doses than XVI. (1) PACS numbers: 85.57.C‐, 85.57.cj, 85.57.cm, 85.57.cf

Tumor-Volume Simulation During Radiotherapy for Head-and-Neck Cancer Using a Four-Level Cell Population Model

PURPOSE To develop a fast computational radiobiologic model for quantitative analysis of tumor volume during fractionated radiotherapy. The tumor-volume model can be useful for optimizing image-guidance protocols and four-dimensional treatment simulations in proton therapy that is highly sensitive to physiologic changes. METHODS The analysis is performed using two approximations: (1) tumor volume is a linear function of total cell number and (2) tumor-cell population is separated into four subpopulations: oxygenated viable cells, oxygenated lethally damaged cells, hypoxic viable cells, and hypoxic lethally damaged cells. An exponential decay model is used for disintegration and removal of oxygenated lethally damaged cells from the tumor. RESULTS We tested our model on daily volumetric imaging data available for 14 head-and-neck cancer patients treated with an integrated computed tomography/linear accelerator system. A simulation based on the averaged values of radiobiologic parameters was able to describe eight cases during the entire treatment and four cases partially (50% of treatment time) with a maximum 20% error. The largest discrepancies between the model and clinical data were obtained for small tumors, which may be explained by larger errors in the manual tumor volume delineation procedure. CONCLUSIONS Our results indicate that the change in gross tumor volume for head-and-neck cancer can be adequately described by a relatively simple radiobiologic model. In future research, we propose to study the variation of model parameters by fitting to clinical data for a cohort of patients with head-and-neck cancer and other tumors. The potential impact of other processes, like concurrent chemotherapy, on tumor volume should be evaluated.

Time-dependent cell disintegration kinetics in lung tumors after irradiation

We study the time-dependent disintegration kinetics of tumor cells that did not survive radiotherapy treatment. To evaluate the cell disintegration rate after irradiation, we studied the volume changes of solitary lung tumors after stereotactic radiotherapy. The analysis is performed using two approximations: (1) tumor volume is a linear function of the total cell number in the tumor and (2) the cell disintegration rate is governed by the exponential decay with constant risk, which is defined by the initial cell number and a half-life T(1/2). The half-life T(1/2) is determined using the least-squares fit to the clinical data on lung tumor size variation with time after stereotactic radiotherapy. We show that the tumor volume variation after stereotactic radiotherapy of solitary lung tumors can be approximated by an exponential function. A small constant component in the volume variation does not change with time; however, this component may be the residual irregular density due to radiation fibrosis and was, therefore, subtracted from the total volume variation in our computations. Using computerized fitting of the exponent function to the clinical data for selected patients, we have determined that the average half-life T(1/2) of cell disintegration is 28.2 days for squamous cell carcinoma and 72.4 days for adenocarcinoma. This model is needed for simulating the tumor volume variation during radiotherapy, which may be important for time-dependent treatment planning of proton therapy that is sensitive to density variations.

Optimal Image-Guidance Scenario With Cone-Beam Computed Tomography in Conventionally Fractionated Radiotherapy for Lung Tumors

Purpose:To determine the residual setup errors of several image guidance scenarios, using cone-beam computed tomography (CBCT) in conventionally fractionated radiotherapy for lung tumors. Methods:Thirteen lung cancer patients were treated with conventionally fractionated radiotherapy, using daily image guidance with CBCT, resulting in 389 CBCT scans which were registered to the planning scan using automated soft-tissue registration. Using the resulting daily alignment data, 4 imaging frequency scenarios were analyzed: (A) no imaging; (B) weekly imaging with a 3-mm threshold; (C) first 5 fractions imaged, then weekly imaging with a patient-specific threshold; and (D) imaging every other day. Results:The systematic setup error (Σ) was reduced with increasing frequency of imaging from 3.4 mm for no imaging to 1.0 mm for imaging every other day. Random setup error (σ), however, varied little regardless of the frequency of imaging: 2.9, 3.0, 3.4, and 3.2 mm for scenarios A, B, C, and D, respectively. The setup margins required to account for the residual error of each imaging scenario were 1 to 1.6 cm for scenario A, 4 to 6 mm for scenarios B and C, and 4 to 5 mm for scenario D. As the residual error of daily CBCT was not included in this analysis, these margins compare with a margin of zero for daily CBCT. Conclusions:Daily image guidance is ideal as the setup margin can be reduced by about 5 mm versus a nondaily imaging scenario. However, if daily image guidance is not possible, there is little benefit in imaging more often than once a week.

Optimization of equivalent uniform dose using the L-curve criterion.

Optimization of equivalent uniform dose (EUD) in inverse planning for intensity-modulated radiation therapy (IMRT) prevents variation in radiobiological effect between different radiotherapy treatment plans, which is due to variation in the pattern of dose nonuniformity. For instance, the survival fraction of clonogens would be consistent with the prescription when the optimized EUD is equal to the prescribed EUD. One of the problems in the practical implementation of this approach is that the spatial dose distribution in EUD-based inverse planning would be underdetermined because an unlimited number of nonuniform dose distributions can be computed for a prescribed value of EUD. Together with ill-posedness of the underlying integral equation, this may significantly increase the dose nonuniformity. To optimize EUD and keep dose nonuniformity within reasonable limits, we implemented into an EUD-based objective function an additional criterion which ensures the smoothness of beam intensity functions. This approach is similar to the variational regularization technique which was previously studied for the dose-based least-squares optimization. We show that the variational regularization together with the L-curve criterion for the regularization parameter can significantly reduce dose nonuniformity in EUD-based inverse planning.

TU‐C‐BRB‐06: Modeling Tumor‐Volume Variation During Fractionated Radiotherapy for Non‐Small Cell Lung Cancer

Purpose: To validate the four‐level population tumormodel using tumor volumetric changes obtained using on‐board imaging techniques during fractionated radiotherapy for non‐small‐cell lungcancer.Method and Materials: The four‐level population tumormodel is based on separation of tumor cell population into four subpopulations: 1) oxygenated viable cells, 2) oxygenated lethally damaged cells, 3) hypoxic viable cells, and 4) hypoxic lethally damaged cells. The oxygenated lethally damaged cells are removed from tumor using an exponential decay model. The hypoxic lethally damaged cells stay in tumor for unlimited time; therefore, their removal is governed by reoxygenation process. The model utilizes the following six radiobiological parameters: alpha, beta, potential doubling time Tpot, half‐life T1/2 of lethally damaged cells, initial hypoxic fraction R and reoxygenation rate A. To test the model, we use the clinical data on volumetric tumor changes during fractionated radiotherapy for non‐small‐cell lungcancer obtained using Tomotherapy and Cone‐Beam CT at different institutions. Results: Our preliminary data indicate that adenocarcinoma and squamous cell carcinoma demonstrate different rate of tumor volume variation after irradiation; therefore only cases have been selected where adenocarcinoma o squamous cell carcinoma diagnosis was available. Another problem of accurate tumor‐volume simulation for lungtumors is a significant hypoxic tumor fraction according to the experimental data obtained using fluoromisonidazole PET imaging. The hypoxic tumor fraction can be between 1.3% and 94.7.9% with a median value of 47.6%. Our model with average values of radiobiological parameters describes majority of lung squamous carcinoma cases. However, significant discrepancies have been observed between the model and clinical data for lung adenocarcinoma. Conclusions: The proposed radiobiological model with average values of parameters can be used for simulation of tumor‐volume for lung squamous cell carcinoma with acceptable accuracy. However, this approach does not describe the tumor volume variation for significant fraction of lung adenocarcinoma cases.

TU‐D‐BRC‐05: Influence of CT Image Noise On Proton Range Uncertainty

Purpose: To evaluate the uncertainty of computed proton range in radiotherapytreatment planning which is attributed to random component in CT numbers. Method and Materials: We utilize a random number generator to simulate a white Gaussian noise in CT numbers along the proton pathlength. The proton range is computed using continuous slowing down approximation which is valid for most of proton range. To simulate the statistical straggling of computed proton range, this procedure is iteratively repeated to obtain convergence of proton range PDF which is approaching a Gaussian. The FWHM (full‐width at half maximum) of the range PDF is used as a measure of uncertainty. Results: We investigate parameters which affect the proton range uncertainty in the presence of CTimagenoise. These parameters may include 1) initial proton energy, 2) noise period and 3) noise amplitude. The FWHM of range PDF increases linearly with the noise period. These results indicate that low frequency fluctuations in CTimagenoise can significantly increase the range uncertainty. We have also computed the range PDF as a function of initial proton energy. The FWHM of range PDF increases linearly with the initial proton energy. For the maximum proton energy of 250 MeV, the FWHM of proton range PDF can achieve a value of 5 mm in the presence of CTimagenoise. We note that the ratio FWHM/range increases as the proton range decreases; therefore, the relative range uncertainty is larger for smaller ranges. Conclusions: Range uncertainties due to CTimagenoise can be significant and comparable to the uncertainties attributed to the calibration of CT numbers. The relative range uncertainty increases as the range decreases. Noise reduction in CTimages using smoothing and denoising algorithms can be recommended to reduce the standard deviation of range PDF.

SU‐GG‐T‐124: Probability Density Distribution of Proton Range as a Function of Noise in CT Images

Purpose:Proton range computed in geometry defined by the CTimages is a random parameter because of the stochastic component in the CT numbers. We evaluate numerically the probability density functions (PDF) of the computed proton range for different PDFs of the noise in CTimages.Method and Materials: We have used the random number generators to simulate white noise in the CT numbers along the proton pathlength in the model geometry. The noise was simulated using both Gaussian and uniform PDFs.Proton range was computed using continuous slowing down approximation which is valid for most of the proton range. To simulate the statistical straggling of the computed proton range, we have simulated 100000 random combinations of noise.Results: We show that the PDF of the computed proton range approaches Gaussian distribution for both Gaussian and uniform white noise in CT numbers. The parameters of the range PDF have been determined by least squares fitting of an analytical Gaussian distribution to the numerically obtained PDF using a quasi‐Newton optimization algorithm. We have investigated the range PDF as a function of the standard deviation of noise and the computational grid size. We show that 1) the standard deviation of the proton range increases linearly with the standard deviation of noise and 2) standard deviation of proton range linearly increases with the grid size. For homogeneous media, 200 MeV proton beam and the 3mm grid, the standard deviation of the proton range changed between 1–5mm when the standard deviation of noise changed between 2.5%–15%. Conclusion: Standard deviation of computed proton range increases linearly with the standard deviation of noise in CTimages.Noise reduction algorithms for the CTimages as smoothing or denoising can minimize the standard deviation of the computed proton range.