Milos Vicic

MicroRT - Small animal conformal irradiator

A novel small animal conformal radiation therapy system has been designed and prototyped: MicroRT. The microRT system integrates multimodality imaging, radiation treatment planning, and conformal radiation therapy that utilizes a clinical 192Ir isotope high dose rate source as the radiation source (teletherapy). A multiparameter dose calculation algorithm based on Monte Carlo dose distribution simulations is used to efficiently and accurately calculate doses for treatment planning purposes. A series of precisely machined tungsten collimators mounted onto a cylindrical collimator assembly is used to provide the radiation beam portals. The current design allows a source-to-target distance range of 1-8 cm at four beam angles: 0 degrees (beam oriented down), 90 degrees, 180 degrees, and 270 degrees. The animal is anesthetized and placed in an immobilization device with built-in fiducial markers and scanned using a computed tomography, magnetic resonance, or positron emission tomography scanner prior to irradiation. Treatment plans using up to four beam orientations are created utilizing a custom treatment planning system-microRTP. A three-axis computer-controlled stage that supports and accurately positions the animals is programmed to place the animal relative to the radiation beams according to the microRTP plan. The microRT system positioning accuracy was found to be submillimeter. The radiation source is guided through one of four catheter channels and placed in line with the tungsten collimators to deliver the conformal radiation treatment. The microRT hardware specifications, the accuracy of the treatment planning and positioning systems, and some typical procedures for radiobiological experiments that can be performed with the microRT device are presented.

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose–volume outcome relationships

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearmans rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Progress toward a microradiation therapy small animal conformal irradiator

Microradiation therapy (microRT) systems are being designed to provide conformal radiation therapy to small animals enabling quantitative radiation response evaluation. We used a Monte Carlo approach to estimate the radiation dose distributions from proposed blueprints and developed a beam model to aid in the microRT system design process. This process was applied to a prototype irradiator that uses a small (3 mm long and 3 mm in diameter), cylindrical, high-activity 192Ir source delivering the radiation beam using custom-fabricated tungsten collimators. The BEAMnrc Monte Carlo code was used to simulate dose distributions from these prototype collimators. Simulations were performed at three source-to-surface distances (50, 60, and 70 mm), and with five circular field sizes (5, 7.5, 10, 12.5, and 15 mm). A dose to a 50 X 50 X 50 mm3 water phantom with 1 X 1 X 1 mm3 voxel spacing was computed. A multiparameter dose calculation algorithm was developed to efficiently and accurately calculate doses for treatment planning exercises. The parametrization was selected so that the parameters varied smoothly as a function of depth, source-to-surface distance, and field size, allowing interpolation for geometries that were not simulated using the Monte Carlo simulation. Direct comparison of the model with the Monte Carlo simulations showed that the variations were within 5% error for field sizes larger than 10 mm, and up to 10% for smaller field sizes.

Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods.

In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.

Dependence of radiochromic film optical density post‐exposure kinetics on dose and dose fractionation

Radiochromic film (RCF) has been shown to be a precise and accurate secondary planar dosimeter for acute exposure radiation fields. However, its application to low dose-rate brachytherapy has been questioned because of possible dose-rate effects. To address this concern, we have measured the optical density (OD) of Model 55-2 RCF as a function of time (interval between the completion of irradiation and densitometry using a 633 nm laser scanner) following exposure (from less than 1 hour to 90 days) for single and split doses from 1 Gy to 100 Gy. Our work demonstrates that film darkening as a function of post-irradiation time depends significantly on total dose, with films exposed to lower doses developing faster than films given higher doses. At 1 Gy, the OD 90 days after exposure is 200% larger than that measured 1 h after exposure compared to a 20% increase over 90 days for doses larger than 20 Gy. An empirical model with time-independent, fast and slow growth terms was used to fit single exposure data. The dependence of the resulting best-fit parameters on dose was investigated. Splitting the dose into two fractions (20 Gy followed by doses of 1-80 Gy 24 h later) results in modest post-irradiation time-dependent changes in the total optical density (at most 15% at small doses), which dissipates within 20 hours following the second exposure. This experimental finding is consistent with the predictions of a simple cumulative dose superposition model. Overall, both experimental and empirical modeling suggest that dose-rate effects may be relatively small despite the strong dependence of film darkening kinetics on total dose. However, more experimental evaluation of radiochromic film response dependence on dose rate and dose-time-fractionation patterns is needed.

Validation of calculations for electrons modulated with conventional photon multileaf collimators

Treating shallow tumors with a homogeneous dose while simultaneously minimizing the dose to distal critical organs remains a challenge in radiotherapy. One promising approach is modulated electron radiotherapy (MERT). Due to the scattering properties of electron beams, the commercially provided secondary and tertiary photon collimation systems are not conducive for electron beam delivery when standard source-to-surface distances are used. Also, commercial treatment planning systems may not accurately model electron-beam dose distributions when collimated without the standard applicators. However, by using the photon multileaf collimators (MLCs) to create segments to modulate electron beams, the quality of superficial tumor dose distributions may improve substantially. The purpose of this study is to develop and evaluate calculations for the narrow segments needed to modulate megavoltage electron beams using photon beam multileaf collimators. Modulated electron radiotherapy (MERT) will be performed with a conventional linear accelerator equipped with a 120 leaf MLC for 6-20 MeV electron beam energies. To provide a sharp penumbra, segments were delivered with short SSDs (70-85 cm). Segment widths (SW) ranging from 1 to 10 cm were configured for delivery and planning, using BEAMnrc Monte Carlo (MC) code, and the DOSXYZnrc MC dose calculations. Calculations were performed with voxel size of 0.2 x 0.2 x 0.1 cm3. Dosimetry validation was performed using radiographic film and micro- or parallel-plate chambers. Calculated and measured data were compared using technical computing software. Beam sharpness (penumbra) degraded with decreasing incident beam energy and field size (FS), and increasing SSD. A 70 cm SSD was found to be optimal. The PDD decreased significantly with decreasing FS. The comparisons demonstrated excellent agreement for calculations and measurements within 3%, 1 mm. This study shows that accurate calculations for MERT as delivered with existing photon MLC are feasible and allows the opportunity to take advantage of the dynamic leaf motion capabilities and control systems, to provide conformal dose distributions.

Retrospective Monte Carlo dose calculations with limited beam weight information

An important unresolved issue in outcomes analysis for lung complications is the effect of poor or completely lacking heterogeneity corrections in previously archived treatment plans. To estimate this effect, we developed a novel method based on Monte Carlo (MC) dose calculations which can be applied retrospectively to RTOG/AAPM-style archived treatment plans (ATP). We applied this method to 218 archived nonsmall cell lung cancer lung treatment plans that were originally calculated either without heterogeneity corrections or with primitive corrections. To retrospectively specify beam weights and wedges, beams were broken into Monte Carlo-generated beamlets, simulated using the VMC++ code, and mathematical optimization was used to match the archived water-based dose distributions. The derived beam weights (and any wedge effects) were then applied to Monte Carlo beamlets regenerated based on the patient computed tomography densities. Validation of the process was performed against five comparable lung treatment plans generated using a commercial convolution/superposition implementation. For the application here (normal lung, esophagus, and planning target volume dose distributions), the agreement was very good. Resulting MC and convolution/superposition values were similar when dose distributions without heterogeneity corrections or dose distributions with corrections were compared. When applied to the archived plans (218), the average absolute percent difference between water-based MC and water-based ATPs, for doses above 2.5% of the maximum dose was 1.8+/-0.6%. The average absolute percent difference between heterogeneity-corrected MC and water-based ATPs increased to 3.1+/-0.9%. The average absolute percent difference between the MC heterogeneity-corrected and the ATP heterogeneity-corrected dose distributions was 3.8+/-1.6% (available in 132/218 archives). The entire dose-volume-histograms for lung, tumor, and esophagus from the different calculation methods, as well as specific dose metrics, were compared. The average difference in maximum lung dose between water-based ATPs and heterogeneity-corrected MC dose distributions was -1.0+/-2.1 Gy. Potential errors in relying on primitive heterogeneity corrections are most evident from a comparison of maximum lung doses, for which the average MC heterogeneity-corrected values were 5.3+/-2.8 Gy less than the ATP heterogeneity-corrected values. We have demonstrated that recalculation of archived dose distributions, without explicit information about beam weights or wedges, is feasible using beamlet-based optimization methods. The method provides heterogeneity-corrected dose data consistent with convolution-superposition calculations and is one feasible approach for improving dosimetric data for outcomes analyses.

Compensation of breathing motion artifacts in thoracic PET images by wavelet-based deconvolution

In biological imaging of thoracic tumors using FDG-PET, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. The effect could be detrimental in small lesions. We demonstrate a deconvolution technique that combines patient-specific motion estimates of tissue trajectories with wavelet decomposition to compensate for breathing-motion induced artifacts. The lung motion estimates were obtained using a breathing model that maps spatial trajectories in CT data as a function of tidal volume and airflow measured by spirometry. Initial results showed good improvement in the spatial resolution, especially in the direction of major lung motion (craniocaudal) on phantom data as well as on clinical data with large or small tumors

TU‐D‐224C‐05: Validation of a Linear Accelerator Source Model and Commissioning Process for Routine Clinical Monte Carlo Calculations

Purpose: Many source models for Monte Carlotreatment planning include parameters which are difficult or ambiguous to determine. We developed and tested a straightforward source model and commissioning process for clinical Monte Carlo calculations. Method and Materials: Our commissioning process of fitting treatment planning system data includes fitting the photon spectrum, electron contamination, penumbra fluence blurring, jaw leakage, and the flattening filter effect. The energy spectrum is fit using a modified Fatigue Life Distribution multiplied by a Fermi. Electron contamination is modeled separately an exponential function, as suggested by Fippel. Penumbral blurring is modeled using a Gaussian filter. Leakage radiation is modeled as a low‐intensity wide‐field monoenergetic source. The flattening filter effect is modeled by multiplying the optimized fluence by a Gaussian reduction. The penumbra and the flattening filter are applied to the fluence map. We tested our methodology on doses produced by a Varian accelerator for 6 MV and 18 MV photons and 5×5, 10×10, and 20×20 cm2field sizes.Results: We found that nine published photon spectra of Varian, Eleckta, and Siemens linear accelerators, ranging in energy from 4 MV to 25 MV could be modeled by the Fatigue Life Distribution with a Fermi cutoff. The agreement between the TPS doses and the commissioned MCdoses were within 2%. Off‐axis energy spectrum softening was unneeded. Conclusion: We have developed a straightforward, yet flexible source modeling system. The commissioning process affords a high‐degree of automation with an unambiguous determination of the relevant parameters. Commissioning of clinical Monte Carlotreatment planning systems is facilitated by using a source model which is only as complicated as necessary to accurately simulates dose distributions. Conflict of Interest: This research was partially supported by NIH grant R01 CA90445 and a grant from Sun Nuclear, Corp.

Locally adaptive denoising of Monte Carlo dose distributions via hybrid median filtering

A fundamental prerequisite of computer aided radiotherapy treatment is the accurate estimation of the dose distributions so as to deliver a high homogeneous dose volume to the tumor without causing unnecessary side effects for the patient. The Monte Carlo (MC) method is considered as the most effective dose distribution computational technique. However, it is too slow and contaminated with noisy degradations that could affect the dose contour visibility and the estimates of dosimetric parameters. In this work, we propose a feature-adaptive median hybrid filter for the denoising of MC dose distributions. Median filtering has been shown to outperform the moving average (mean) in removal of impulsive noise (outliers) and preservation of edges, but it fails to provide the same degree of smoothness in homogeneous regions. We combine linear filters with the median operation to produce hybrid median filters. The filter output can be obtained as a weighted sum of the linear filter and the median operation depending on the properties of the local neighborhood. We evaluated the technique on different datasets, a challenging 2-D synthetic dataset of different geometric shapes at different scales with added noise and blurring, and 2-D/3-D water phantoms. The proposed filter, judged by mean square error, performed well in comparison with currently existing techniques. Denoising of full 3-D real treatment plan datasets has shown similar promise.