Engineering topics

Lewis, J

Publications and source records attributed to Lewis, J.

12 records

DOE OSTI2016

SU-C-BRA-07: Variability of Patient-Specific Motion Models Derived Using Different Deformable Image Registration Algorithms for Lung Cancer Stereotactic Body Radiotherapy (SBRT) Patients

Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived were compared using patient 4DCT scans. Results: Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm. Conclusion: The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-C-209-02: 3D Fluoroscopic Image Generation From Patient-Specific 4DCBCT-Based Motion Models Derived From Clinical Patient Images

Purpose: We develop a method to generate time varying volumetric images (3D fluoroscopic images) using patient-specific motion models derived from four-dimensional cone-beam CT (4DCBCT). Methods: Motion models are derived by selecting one 4DCBCT phase as a reference image, and registering the remaining images to it. Principal component analysis (PCA) is performed on the resultant displacement vector fields (DVFs) to create a reduced set of PCA eigenvectors that capture the majority of respiratory motion. 3D fluoroscopic images are generated by optimizing the weights of the PCA eigenvectors iteratively through comparison of measured cone-beam projections and simulated projections generated from the motion model. This method was applied to images from five lung-cancer patients. The spatial accuracy of this method is evaluated by comparing landmark positions in the 3D fluoroscopic images to manually defined ground truth positions in the patient cone-beam projections. Results: 4DCBCT motion models were shown to accurately generate 3D fluoroscopic images when the patient cone-beam projections contained clearly visible structures moving with respiration (e.g., the diaphragm). When no moving anatomical structure was clearly visible in the projections, the 3D fluoroscopic images generated did not capture breathing deformations, and reverted to the reference image. For the subset of 3D fluoroscopic images generated from projections with visibly moving anatomy, the average tumor localization error and the 95th percentile were 1.6 mm and 3.1 mm respectively. Conclusion: This study showed that 4DCBCT-based 3D fluoroscopic images can accurately capture respiratory deformations in a patient dataset, so long as the cone-beam projections used contain visible structures that move with respiration. For clinical implementation of 3D fluoroscopic imaging for treatment verification, an imaging field of view (FOV) that contains visible structures moving with respiration should be selected. If no other appropriate structures are visible, the images should include the diaphragm. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-135: Tumor Displacement-Based Binning for Respiratory-Gated Time-Independent 5DCT Treatment Planning

Purpose: A published 5DCT breathing motion model enables image reconstruction at any user-selected breathing phase, defined by the model as a specific amplitude (v) and rate (f). Generation of reconstructed phase-specific CT scans will be required for time-independent radiation dose distribution simulations. This work answers the question: how many amplitude and rate bins are required to describe the tumor motion with a specific spatial resolution? Methods: 19 lung-cancer patients with 21 tumors were scanned using a free-breathing 5DCT protocol, employing an abdominally positioned pneumatic-bellows breathing surrogate and yielding voxel-specific motion model parameters α and β corresponding to motion as a function of amplitude and rate, respectively. Tumor GTVs were contoured on the first (reference) of 25 successive free-breathing fast helical CT image sets. The tumor displacements were binned into widths of 1mm to 5mm in 1mm steps and the total required number of bins recorded. The simulation evaluated the number of bins needed to encompass 100% of the breathing-amplitude and between the 5th and 95th percentile amplitudes to exclude breathing outliers. Results: The mean respiration-induced tumor motion was 9.90mm ± 7.86mm with a maximum of 25mm. The number of bins required was a strong function of the spatial resolution and varied widely between patients. For example, for 2mm bins, between 1–13 amplitude bins and 1–9 rate bins were required to encompass 100% of the breathing amplitude, while 1–6 amplitude bins and 1–3 rate bins were required to encompass 90% of the breathing amplitude. Conclusion: The strong relationship between number of bins and spatial resolution as well as the large variation between patients implies that time-independent radiation dose distribution simulations should be conducted using patient-specific data and that the breathing conditions will have to be carefully considered. This work will lead to the assessment of the dosimetric impact of binning resolution. This study is supported by Siemens Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-P-59: A Graphical Interface for XCAT Phantom Configuration, Generation and Processing

Purpose: To design a comprehensive open-source, publicly available, graphical user interface (GUI) to facilitate the configuration, generation, processing and use of the 4D Extended Cardiac-Torso (XCAT) phantom. Methods: The XCAT phantom includes over 9000 anatomical objects as well as respiratory, cardiac and tumor motion. It is widely used for research studies in medical imaging and radiotherapy. The phantom generation process involves the configuration of a text script to parameterize the geometry, motion, and composition of the whole body and objects within it, and to generate simulated PET or CT images. To avoid the need for manual editing or script writing, our MATLAB-based GUI uses slider controls, drop-down lists, buttons and graphical text input to parameterize and process the phantom. Results: Our GUI can be used to: a) generate parameter files; b) generate the voxelized phantom; c) combine the phantom with a lesion; d) display the phantom; e) produce average and maximum intensity images from the phantom output files; f) incorporate irregular patient breathing patterns; and f) generate DICOM files containing phantom images. The GUI provides local help information using tool-tip strings on the currently selected phantom, minimizing the need for external documentation. The DICOM generation feature is intended to simplify the process of importing the phantom images into radiotherapy treatment planning systems or other clinical software. Conclusion: The GUI simplifies and automates the use of the XCAT phantom for imaging-based research projects in medical imaging or radiotherapy. This has the potential to accelerate research conducted with the XCAT phantom, or to ease the learning curve for new users. This tool does not include the XCAT phantom software itself. We would like to acknowledge funding from MRA, Varian Medical Systems Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-34: Setup Accuracy in Spine SBRT Using CBCT 6D Image Guidance in Comparison with 6D ExacTrac

Purpose Volumetric information of the spine captured on CBCT can potentially improve the accuracy in spine SBRT setup that has been commonly performed through 2D radiographs. This work evaluates the setup accuracy in spine SBRT using 6D CBCT image guidance that recently became available on Varian systems. Methods ExacTrac radiographs have been commonly used for Spine SBRT setup. The setup process involves first positioning patients with lasers followed by localization imaging, registration, and repositioning. Verification images are then taken providing the residual errors (ExacTracRE) before beam on. CBCT verification is also acquired in our institute. The availability of both ExacTrac and CBCT verifications allows a comparison study. 41 verification CBCT of 16 patients were retrospectively registered with the planning CT enabling 6D corrections, giving CBCT residual errors (CBCTRE) which were compared with ExacTracRE. Results The RMS discrepancies between CBCTRE and ExacTracRE are 1.70mm, 1.66mm, 1.56mm in vertical, longitudinal and lateral directions and 0.27°, 0.49°, 0.35° in yaw, roll and pitch respectively. The corresponding mean discrepancies (and standard deviation) are 0.62mm (1.60mm), 0.00mm (1.68mm), −0.80mm (1.36mm) and 0.05° (0.58°), 0.11° (0.48°), −0.16° (0.32°). Of the 41 CBCT, 17 had high-Z surgical implants. No significant difference in ExacTrac-to-CBCT discrepancy was observed between patients with and without the implants. Conclusion Multiple factors can contribute to the discrepancies between CBCT and ExacTrac: 1) the imaging iso-centers of the two systems, while calibrated to coincide, can be different; 2) the ROI used for registration can be different especially if ribs were included in ExacTrac images; 3) small patient motion can occur between the two verification image acquisitions; 4) the algorithms can be different between CBCT (volumetric) and ExacTrac (radiographic) registrations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-151: Day-To-Day Variations in Fraction-Specific Motion Modeling Using Patient 4DCBCT Images

Purpose: The goal of this study is to quantify the interfraction reproducibility of patient-specific motion models derived from 4DCBCT acquired on the day of treatment of lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived from patient 4DCBCT images acquired daily over 3–5 fractions of treatment by 1) applying deformable image registration between each 4DCBCT image and a reference phase from that day, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs to derive a motion model. The motion model from the first day of treatment is compared to motion models from each successive day of treatment to quantify variability in motion models generated from different days. Four SBRT patient datasets have been acquired thus far in this IRB approved study. Results: Fraction-specific motion models for each fraction and patient were derived and PCA eigenvectors and their associated eigenvalues are compared for each fraction. For the first patient dataset, the average root mean square error between the first two eigenvectors associated with the highest two eigenvalues, in four fractions was 0.1, while it was 0.25 between the last three PCA eigenvectors associated with the lowest three eigenvalues. It was found that the eigenvectors and eigenvalues of PCA motion models for each treatment fraction have variations and the first few eigenvectors are shown to be more stable across treatment fractions than others. Conclusion: Analysis of this dataset showed that the first two eigenvectors of the PCA patient-specific motion models derived from 4DCBCT were stable over the course of several treatment fractions. The third, fourth, and fifth eigenvectors had larger variations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-186: Using 4DCT-Based Motion Modeling to Predict Motion and Duty Cycle On Successive Days of Gated Radiotherapy

Purpose: To determine if 4DCT-based motion modeling and external surrogate motion measured during treatment simulation can enhance prediction of residual tumor motion and duty cycle during treatment delivery. Methods: This experiment was conducted using simultaneously recorded tumor and external surrogate motion acquired over multiple fractions of lung cancer radiotherapy. These breathing traces were combined with the XCAT phantom to simulate CT images. Data from the first day was used to estimate the residual tumor motion and duty cycle both directly from the 4DCT (the current clinical standard), and from external-surrogate based motion modeling. The accuracy of these estimated residual tumor motions and duty cycles are evaluated by comparing to the measured internal/external motions from other treatment days. Results: All calculations were done for 25% and 50% duty cycles. The results indicated that duty cycle derived from 4DCT information alone is not enough to accurately predict duty cycles during treatment. Residual tumor motion was determined from the recorded data and compared with the estimated residual tumor motion from 4DCT. Relative differences in residual tumor motion varied from −30% to 55%, suggesting that more information is required to properly predict residual tumor motion. Compared to estimations made from 4DCT, in three out of four patients examined, the 30 seconds of motion modeling data was able to predict the duty cycle with better accuracy than 4DCT. No improvement was observed in prediction of residual tumor motion for this dataset. Conclusion: Motion modeling during simulation has the potential to enhance 4DCT and provide more information about target motion, duty cycles, and delivered dose. Based on these four patients, 30 seconds of motion modeling data produced improve duty cycle estimations but showed no measurable improvement in residual tumor motion prediction. More patient data is needed to verify this Result. I would like to acknowledge funding from MRA, VARIAN Medical Systems, Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-266: Cone Beam Computed Tomography (CBCT) Inter-Scan and Inter-Observer Tumor Volume Variability Assessment in Patients Treated with Stereotactic Body Radiation Therapy (SBRT) for Early Stage Non-Small Cell Lung Cancer (NSCLC)

Purpose: Quantification of volume changes on CBCT during SBRT for NSCLC may provide a useful radiological marker for radiation response and adaptive treatment planning, but the reproducibility of CBCT volume delineation is a concern. This study is to quantify inter-scan/inter-observer variability in tumor volume delineation on CBCT. Methods: Twenty earlystage (stage I and II) NSCLC patients were included in this analysis. All patients were treated with SBRT with a median dose of 54 Gy in 3 to 5 fractions. Two physicians independently manually contoured the primary gross tumor volume on CBCTs taken immediately before SBRT treatment (Pre) and after the same SBRT treatment (Post). Absolute volume differences (AVD) were calculated between the Pre and Post CBCTs for a given treatment to quantify inter-scan variability, and then between the two observers for a given CBCT to quantify inter-observer variability. AVD was also normalized with respect to average volume to obtain relative volume differences (RVD). Bland-Altman approach was used to evaluate variability. All statistics were calculated with SAS version 9.4. Results: The 95% limit of agreement (mean ± 2SD) on AVD and RVD measurements between Pre and Post scans were −0.32cc to 0.32cc and −0.5% to 0.5% versus −1.9 cc to 1.8 cc and −15.9% to 15.3% for the two observers respectively. The 95% limit of agreement of AVD and RVD between the two observers were −3.3 cc to 2.3 cc and −42.4% to 28.2% respectively. The greatest variability in inter-scan RVD was observed with very small tumors (< 5 cc). Conclusion: Inter-scan variability in RVD is greatest with small tumors. Inter-observer variability was larger than inter-scan variability. The 95% limit of agreement for inter-observer and inter-scan variability (∼15–30%) helps define a threshold for clinically meaningful change in tumor volume to assess SBRT response, with larger thresholds needed for very small tumors. Part of the work was funded by a Kaye award; Disclosure/Conflict of interest: Raymond H. Mak: Stock ownership: Celgene, Inc. Consulting: Boehringer-Ingelheim, Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-352: Effects of Skull Attenuation and Missing Backscatter On Brain Dose in HDR Treatment of the Head with Surface Applicators

Purpose: To calculate the effect of lack of backscatter from air and attenuation of bone on dose distributions in brachytherapy surface treatment of head. Existing treatment planning systems based on TG43 do not account for heterogeneities, and thus may overestimate the dose to the brain. While brachytherapy generally has rapid dose falloff, the dose to the deeper tissues (in this case, the brain) can become significant when treating large curved surfaces. Methods: Applicator geometries representing a range of clinical cases were simulated in MCNP5. An Ir-192 source was modeled using the energy spectrum presented by TG-43. The head phantom was modeled as a 7.5-cm radius water sphere, with a 7 -mm thick skull embedded 5-mm beneath the surface. Dose values were calculated at 20 points inside the head, in which 10 of them were on the central axis and the other 10 on the axis connecting the central of the phantom with the second to last source from the applicator edge. Results: Central and peripheral dose distributions for a range of applicator and head sizes are presented. The distance along the central axis at which the dose falls to 80% of the prescribed dose (D80) was 7 mm for a representative small applicator and 9 mm for a large applicator. Corresponding D50 and D30 for the same small applicator were 17 mm and 32 mm respectively. D50 and D30 for the larger applicator were 32 mm and 60 mm respectively. These results reflect the slower falloff expected for larger applicators on a curved surface. Conclusion: Our results can provide guidance for clinicians to calculate the dose reduction effect due to bone attenuation and the lack of backscatter from air to estimate the brain dose for the HDR treatments of surface lesions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-CD-304-11: Veritas 2.0: A Cloud-Based Tool to Facilitate Research and Innovation

Purpose: We introduce Veritas 2.0, a cloud-based, non-clinical research portal, to facilitate translation of radiotherapy research ideas to new delivery techniques. The ecosystem of research tools includes web apps for a research beam builder for TrueBeam Developer Mode, an image reader for compressed and uncompressed XIM files, and a trajectory log file based QA/beam delivery analyzer. Methods: The research beam builder can generate TrueBeam readable XML file either from scratch or from pre-existing DICOM-RT plans. DICOM-RT plan is first converted to XML format and then researcher can interactively modify or add control points to them. Delivered beam can be verified via reading generated images and analyzing trajectory log files. Image reader can read both uncompressed and HND-compressed XIM images. The trajectory log analyzer lets researchers plot expected vs. actual values and deviations among 30 mechanical axes. The analyzer gives an animated view of MLC patterns for the beam delivery. Veritas 2.0 is freely available and its advantages versus standalone software are i) No software installation or maintenance needed, ii) easy accessibility across all devices iii) seamless upgrades and iv) OS independence. Veritas is written using open-source tools like twitter bootstrap, jQuery, flask, and Python-based modules. Results: In the first experiment, an anonymized 7-beam DICOM-RT IMRT plan was converted to XML beam containing 1400 control points. kV and MV imaging points were inserted into this XML beam. In another experiment, a binary log file was analyzed to compare actual vs expected values and deviations among axes. Conclusions: Veritas 2.0 is a public cloud-based web app that hosts a pool of research tools for facilitating research from conceptualization to verification. It is aimed at providing a platform for facilitating research and collaboration. I am full time employee at Varian Medical systems, Palo Alto.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-D-303-02: Applications of Volumetric Images Generated with a Respiratory Motion Model Based On An External Surrogate Signal

Purpose: Respiratory motion can vary significantly over the course of simulation and treatment. Our goal is to use volumetric images generated with a respiratory motion model to improve the definition of the internal target volume (ITV) and the estimate of delivered dose. Methods: Ten irregular patient breathing patterns spanning 35 seconds each were incorporated into a digital phantom. Ten images over the first five seconds of breathing were used to emulate a 4DCT scan, build the ITV, and generate a patient-specific respiratory motion model which correlated the measured trajectories of markers placed on the patients’ chests with the motion of the internal anatomy. This model was used to generate volumetric images over the subsequent thirty seconds of breathing. The increase in the ITV taking into account the full 35 seconds of breathing was assessed with ground-truth and model-generated images. For one patient, a treatment plan based on the initial ITV was created and the delivered dose was estimated using images from the first five seconds as well as ground-truth and model-generated images from the next 30 seconds. Results: The increase in the ITV ranged from 0.2 cc to 6.9 cc for the ten patients based on ground-truth information. The model predicted this increase in the ITV with an average error of 0.8 cc. The delivered dose to the tumor (D95) changed significantly from 57 Gy to 41 Gy when estimated using 5 seconds and 30 seconds, respectively. The model captured this effect, giving an estimated D95 of 44 Gy. Conclusion: A respiratory motion model generating volumetric images of the internal patient anatomy could be useful in estimating the increase in the ITV due to irregular breathing during simulation and in assessing delivered dose during treatment. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc. and Radiological Society of North America Research Scholar Grant #RSCH1206.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-G-207-06: 3D Fluoroscopic Image Generation From Patient-Specific 4DCBCT-Based Motion Models Derived From Physical Phantom and Clinical Patient Images

Purpose: Respiratory-correlated cone-beam CT (4DCBCT) images acquired immediately prior to treatment have the potential to represent patient motion patterns and anatomy during treatment, including both intra- and inter-fractional changes. We develop a method to generate patient-specific motion models based on 4DCBCT images acquired with existing clinical equipment and used to generate time varying volumetric images (3D fluoroscopic images) representing motion during treatment delivery. Methods: Motion models are derived by deformably registering each 4DCBCT phase to a reference phase, and performing principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated by optimizing the resulting PCA coefficients iteratively through comparison of the cone-beam projections simulating kV treatment imaging and digitally reconstructed radiographs generated from the motion model. Patient and physical phantom datasets are used to evaluate the method in terms of tumor localization error compared to manually defined ground truth positions. Results: 4DCBCT-based motion models were derived and used to generate 3D fluoroscopic images at treatment time. For the patient datasets, the average tumor localization error and the 95th percentile were 1.57 and 3.13 respectively in subsets of four patient datasets. For the physical phantom datasets, the average tumor localization error and the 95th percentile were 1.14 and 2.78 respectively in two datasets. 4DCBCT motion models are shown to perform well in the context of generating 3D fluoroscopic images due to their ability to reproduce anatomical changes at treatment time. Conclusion: This study showed the feasibility of deriving 4DCBCT-based motion models and using them to generate 3D fluoroscopic images at treatment time in real clinical settings. 4DCBCT-based motion models were found to account for the 3D non-rigid motion of the patient anatomy during treatment and have the potential to localize tumor and other patient anatomical structures at treatment time even when inter-fractional changes occur. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc., Palo Alto, CA. The project was also supported, in part, by Award Number R21CA156068 from the National Cancer Institute.

60 APPLIED LIFE SCIENCES↗