bluuebunny/crossref_metadata_2025 huggingface dataset:
TU‐E‐141‐01: Using Respiratory Motion as a Degree of Freedom Through Weighted Phase‐Correlated 4D Planning
Authors: Tachibana H , Sawant A
Published: July 2013

Purpose: We investigate a novel 4D treatment planning strategy that exploits respiratory motion as an additional degree of freedom. The key idea is to deliver more (less) fluence when the tumor target is out‐of‐line (in‐line) with an organ‐at‐risk (OAR). We have developed our weighted‐phase‐correlated (WPC) 4D treatment planning paradigm on a commercial platform (Eclipse, Varian) and designed it for an “open‐gate” delivery, where beam is on throughout the respiratory cycle. Methods: 4DCT data were collected from twenty lung SBRT patients who exhibited >= 5mm tumor motion despite the use of abdominal compression. For each patient, (i)f × θ 3D‐conformal plans were created and corresponding 3D dose distributions were calculated, where θ = number of respiratory phases (10) and f = number of fields (7 – 12) (ii) For each beam, the dose from nine phases was registered to the reference phase (end‐exhale) using a GPU‐based B‐spline deformable registration, NiftyReg. (iv) using dynamic‐penalized‐likelihood optimization, fluence weights were optimized over all dose distributions. This key step ensured that for each motion‐induced state of the anatomy, an optimal fluence would be delivered. (v) A summed fluence map was imported back into Eclipse and an MLC leaf sequence was generated. In order to ensure deliverability, a maximum leaf velocity constraint (v <= 3.5 cm/s) was imposed. Each WPC‐4D plan was compared with the corresponding, clinically‐delivered, ITV‐based plan. Results: In all 20 patients, WPC‐4D plans gave PTV conformity comparable to ITV‐based plans. The percent dose‐sparing (maximum, average) achieved using WPC‐4D compared to ITV‐based planning: Spinal Cord Dmax=(66,20); Esophagus Dmax=(94,29), Heart Dmax=(84,30); Heart Dmean=(82,35); Lung V20=(27,7); Lung Dmean=(27,13). Conclusion: We have developed a novel 4D‐planning solution that exploits rather than trying to mitigate respiratory motion. Initial results indicate that this approach achieves significant dose‐sparing in OARs compared to conventional motion‐management (abdominal compression) + ITV‐based planning. This research was partially supported by Varian Medical Systems, Palo Alto, CA
TU‐E‐141‐07: Clinical Evaluation of the Iterative Metal Artifact Reduction Algorithm for CT Simulation in Radiotherapy
Authors: Axente M , Lee K , Paidi A , Bani‐Hashemi A , Hristov D
Published: July 2013

Purpose: The iterative metal artifact reduction (IMAR) algorithm has been proposed for commercial implementation in upcoming Siemens platforms. The purpose of this study is to evaluate the performance of this algorithm in radiation oncology settings. Methods: Mean CT numbers and noise (standard‐deviation) within delineated regions of interest were compared before/after IMAR correction on standard electron‐density phantom images. Patient IMAR‐corrected images were evaluated by 4 observers and ranked based on conspicuity of structures near artifacts (0–5 scale, 5 best score). The dosimetric impact of utilizing IMAR‐corrected patient images for planning was analyzed by comparing original dose distributions and those recalculated on IMAR‐corrected images. All images were acquired on a Siemens Definition scanner. In order to reference the observations herein, all analyses were also conducted on images corrected with a second algorithm: metal deletion technique (MDT), available for public use. Results: IMAR accurately recovers CT numbers. CT number percent differences were reduced on average from 62% to 18%, while average noise percent differences were minimally reduced (146% before, 140% after). MDT performed worse retrieving mean CT numbers (62% to 27%), and better at reducing noise (146% to 24%). After visually inspecting the images, physicians agreed that IMAR‐corrected images offered better confidence at reading patient anatomy than original images. The MDT‐corrected images scored 4.3 on average while IMAR‐corrected images scored 4 with reviewing physicians (p = 0.052). Local dose differences up to ±20–30cGy were noted, but γ‐analysis (3%/3mm) did not indicate major overall differences between plans calculated on original images and those calculated on IMAR‐corrected images. Conclusion: The IMAR algorithm accurately recovered CT numbers (better than MDT), while minimally reducing noise values (worse than MDT). No clinically significant differences were detected between dose distributions calculated on original CT images and those planned on IMAR‐corrected images. Initial analysis indicates that IMAR images could be used for treatment planning. Siemens Healthcare
TU‐E‐141‐10: 3D Delivered Dose Assessment for SBRT Treatment Based On Estimated Volumetric Time‐Varying Images From a Single Projection Image
Authors: Mishra P , Seco J , Li R , St. James S , Berbeco R , Williams C , Lewis J
Published: July 2013

Purpose: To develop a method of verifying delivered dose based on time‐varying 3D treatment images estimated from single kV projection images and a 4DCT‐based patient‐specific motion model. Methods: Delivered dose calculation is performed in two steps. In the first step 3D fluoroscopic (time‐varying) images are generated from single 2D kV projection images. In the second step a set of 3D fluoroscopic images are used to calculate the delivered dose. 3D fluoroscopic images are estimate by creating a patient‐specific lung motion model through deformable registration of 4DCT, and then iteratively optimization the parameters of the motion model. Using a modified digital XCAT phantom, the accuracy of the delivered dose calculated with our method was compared to the dose that would be calculated during routine 4DCT‐based treatment planning and to the actual delivered dose. The planning dose was calculated on an average intensity projection (AIP) image. The actual delivered dose is calculated based on the defined motion of the digital XCAT phantom. The same treatment plan was delivered to all three phantoms. The treatment plan was developed from the AIP and designed as a 3×18 Gy SBRT plan. Results: The dose calculated on 3D fluoroscopic images closely matches to the actual delivered dose. Both the calculated and delivered doses vary from AIP‐based planned dose. D95 tumor dose values of planned, delivered and calculated dose are 54.0, 59.0 and 58.4 Gy. Corresponding V20 lung values are 4.5%, 4.8% and 4.8% respectively. Conclusions: We introduced a novel method for delivered dose calculation. Our algorithm successfully generates a 3D fluoroscopic image from a single kV images and then uses these time‐varying 3D images to accurately calculate delivered dose. The project described was supported, in part, by an RSNA Research Scholar Grant and Award Number R21CA156068 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA, National Cancer Institute or the National Institutes of Health.
TU‐F‐116‐01: TG132: Image Registration and Fusion
Authors: Brock K , Mutic S , McNutt T , Li H , Kessler M
Published: July 2013

Image registration and fusion has been in use in the clinic for a long time, however, in recent years its role has expanded to include multi‐modality registration for planning, motion assessment, in‐room treatment guidance, dose accumulation, replanning, and adaptive radiotherapy. These advances increase the complexity of the commissioning and quality assurance required. The AAPM formed a Task Group 132, to formalize the use of image registration and fusion in radiotherapy. The task group recommendations will be presented here. A brief summary of image registration and fusion techniques will be provided, however attendees are referred to Therapy Educational Session: Deformable Image Registration, Contour Propagation, and Dose Mapping for in depth details. The focus of this session will be methods for validation and quality assurance, commissioning, and clinical implementation in treatment planning and delivery. Validation of image registration and fusion includes both qualitative and quantitate evaluations of the image registration results. End‐to‐end tests with physical phantoms can ensure that the image registration software integrates into the clinical workflow. In addition, virtual phantoms can allow detailed quantitative evaluation of both rigid and deformable registration. As the end goal is how to use the registration algorithm on clinical images safely, a comprehensive commissioning and QA program should also include evaluation on true clinical image sets.

Learning Objectives:

1. Describe the methods for validation and quality assurance of image registration techniques.

2. Understand the importance of acceptance testing, including end‐to‐end tests, phantom tests, and clinical data tests.

3. Describe techniques for patient specific validation.

Dr. Brock has financial interest in deformable registration technology through a licensing agreement with Ray Search Laboratories.
TU‐G‐108‐08: Variable RBE and Incidence of Radiation Pneumonitis in Lung Patients Treated with Proton Therapy
Authors: Mirkovic D , Titt U , Peeler C , Perles L , Liao Z , Mohan R
Published: July 2013

Purpose: To determine the correlation between the Relative Biological Effectiveness (RBE)‐weighted dose and radiation pneumonitis (RP) incidence in cancer patients treated with proton therapy. Methods: The Monte Carlo proton dose calculation system MC2 based on MCNPX was used to recalculate treatment plans taken from the clinical treatment planning system (TPS) and compute the dose‐averaged LET in each voxel of the CT based patient model. The linear scaling model (LSM) for proton RBE was used with biological coefficients for RP to compute the variable RBE‐weighted dose distributions. These were compared with the constant RBE‐weighted doses computed by the TPS and currently used in our clinical practice. A restaging FDG PET/CT was registered to the treatment planning CT and the standard uptake values (SUV) were used as a measure of radiation dose response. Results: The variable RBE model results in higher biological dose at the distal ends of proton beams and the corresponding lung volumes receiving elevated doses can be significant if the beam is terminating inside the low density lung tissue. Preliminary results for the first two patients in the study show that the regions of elevated variable RBE‐weighted doses inside the lung are well correlated to the high SUV values in the restaging PET. Both patients were diagnosed with grade 3 RP after completing the treatment. Conclusion: The preliminary results indicate that variable RBE‐weighted doses may provide additional insight into toxicity risk for patients treated with proton radiotherapy. The complete study will include the analysis of many more patients diagnosed with grade 3 RP or higher, treated with protons in our institution. NCI grant P01CA021239
TU‐G‐141‐05: Limited Field‐Of‐View Cone‐Beam CT Reconstruction for Adaptive Radiotherapy
Authors: Lu W , Yan H , Zhou L , Cervino L , Jiang S , Jia X
Published: July 2013

Purpose: Reconstructing CBCT only inside a relatively small circular field of view (FOV) is preferred for adaptive radiotherapy due to the reduced imaging dose, possible short computation time, and sufficient anatomy information for treatment adaptation. Current analytical or iterative reconstruction approaches encounter a truncation problem with degraded image quality inside the FOV. Anatomy outside the FOV is also missing, which is required for dose calculation. The purpose of this study is to develop a technique to obtain CBCT images with high quality inside the FOV and lower quality outside the FOV. Methods: CBCT data acquisition is conducted first at all x‐ray projection angles with a standard FOV to cover the whole body but at a very low mAs. A reconstruction is conducted via FDK algorithm, yielding a noisy CBCT image. X‐ray projection is also acquired with a standard mAs for a small FOV of interest at sparse projection angles. We then forward project the noisy CBCT image outside the small FOV to those sparsely acquired projection angles and subtract the results from the high‐mAs projection data. A high‐quality CBCT image inside the FOV is reconstructed using the updated projection data at those sparse angles via our fast GPU‐based tight‐frame iterative CBCT reconstruction algorithm. Finally, the noisy image outside the FOV and the high‐quality image inside the FOV are merged. Our algorithm is validated in digital NCAT phantom cases. Results: It was found that the proposed method can remove the noise and truncation artifacts effectively and preserve anatomical features inside the FOV. It attains a relative root mean square (RRMS) error of 5.94%. Because of GPU implementation, the computational efficiency is high for clinical applications. Conclusion: The developed CBCT reconstruction method can provide CBCT images with high‐quality FOV for adaptive radiotherapy.
TU‐G‐103‐04: The Accuracy of Monte Carlo Based Dose Estimates Compared to In‐Vivo Dose Measurements
Authors: Khatonabadi M , Mueller J , McMillan K , Flores C , Cody D , Cagnon C , DeMarco J , McNitt‐Gray M
Published: July 2013

Purpose: The purpose of this study was to extend the validation of Monte Carlo (MC) simulation based dose estimates by comparing simulated values with in‐vivo measurements from clinical patient scans. Methods: The Institutional Review Board approved the acquisition of in‐vivo rectal dose measurements in a pilot study of 9 patients undergoing CT Colonography on an MDCT (LightSpeed VCT, GE Healthcare). Per patient, two scans, prone and supine, were acquired both using a fixed mAs technique. In‐vivo dose measurements were obtained using TLD capsules that were affixed to the inner lumen of rectal catheters. Dose from the TLDs were determined taking into account their energy response. Simulations were performed using voxelized models of the patients and the TLDs based on CT image data. A previously developed MC based model was used to simulate the scanner and each patient scan. Dose to the TLD was estimated for each patient scan. The measured and simulated in‐vivo TLD dose values were compared for all 9 patients and the Root Mean Square (RMS) error was calculated as well as the correlation. Additionally the simulated results were compared with calculated Size Specific Dose Estimates (SSDEs) for each patient. Results: The RMS error between TLD measurements and MC simulations was 12.2% with a maximum of 20.3% and minimum of ‐ 19.7%. Comparison of simulations and SSDE resulted in an RMS of 17.0% with a minimum of ‐ 22.9% and maximum of 22.8%. Conclusion: The results of this study demonstrated that MC simulations using voxelized patient and equivalent source model Result in reasonably accurate doses compared to actual measured doses. Several possible improvements to the performance of the MC model were identified (including reconstructing images at the biggest field of view for full coverage of the anatomy, etc.) Dr. Michael McNitt‐Gray; Institutional research agreement, Siemens AG; Recipient research support Siemens AG; Consultant, Flaherty Sensabaugh Bonasso PLLC; Consultant, Fulbright and Jaworski, LLC; Maryam Khatonabadi and Kyle McMillan ripient research support Siemens AG
TU‐G‐103‐06: Evaluations of Cone Beam CT Dose in Image Guided Radiation Therapy for Brain Cancer Patients Via GPU‐Based Monte Carlo Simulations
Authors: Montanari D , Scolari E , Silvestri C , Rice R , Graves Y , Cervino L , Jiang S , Jia X
Published: July 2013

Purpose: Cone beam CT (CBCT) has been widely used for patient setup in image guided radiation therapy (IGRT). The radiation dose from CBCT scans is a clinical concern. The purpose of this study is to quantitatively evaluate CBCT dose to brain cancer patients under IGRT using a GPU‐based Monte Carlo CBCT dose calculation package gCTD. Methods: We first performed dose measurements in a water phantom scanned under Varian OBI system and the measurement data are used to commission gCTD. Eight brain cancer patients under IGRT are studied. For each patient, dose distribution under a standard head scan protocol is computed, in which a 100 kVp x‐ray source rotates around the patient head in a 200‐degree range through the posterior side. Mean dose to key organs are computed. Mean dose to 2% voxels that have the highest dose is also computed to characterize dose inhomogeneity. Results: For each CBCT scan, dose to brain, brainstem, chiasm, eyes, and optical nerves ranges in 0.24–0.28cGy, 0.22–0.31cGy, 0.13–0.17cGy, 0.036–0.13cGy, and 0.061–0.17cGy, respectively, depending on the patient head size and isocenter position. The dose is not homogeneous and the mean doses to 2% voxels with the highest dose for these organs are 0.40‐0.58cGy, 0.30–0.43cGy, 0.18–1.02cGy, 0.063– 0.28cGy, and 0.093–0.83cGy, respectively. Although mean dose inside body is 0.26–0.41cGy, the maximum dose can be up to 3.3 cGy, which locates on the bone. It takes about 1 minutes on an Nvidia C2050 GPU card to simulate 1 billion source photons, yielding an average relative uncertainty less than 1%. Conclusion: For the standard head scan protocol, while average CBCT dose to each organ is relatively low, the dose distribution is not homogeneous and the maximum dose can be substantially high. gCTD provides an efficiency and accurate way to evaluate CBCT dose in IGRT.
TU‐G‐134‐03: MR‐Only‐Based Generation of Electron Density Maps and Digitally Reconstructed Radiographs of the Pelvis
Authors: Helle M , Schadewaldt N , Traughber M , Schulz H , Bystrov D , Vik T , Stehning C , Renisch S
Published: July 2013

Purpose: To generate electron density (ED) maps and digitally reconstructed radiographs (DRRs) of the pelvis based only on magnetic resonance imaging (MRI). Methods: A conventional 3D fast‐field dual‐echo sequence was used to acquire image data of 8 healthy subjects with a Philips 3.0T Ingenia TX system in approximately 1:50 min scan time per volunteer. Dixon reconstruction of the nearly out‐of‐phase (echo 1) and in‐phase images (echo 2) allowed for water and fat classification. A bone‐enhanced image was generated by automatically thresholding the noise level of the in‐phase image with subsequent background removal. ED maps were then produced by assigning known bulk electron densities to the classified bone and tissue fractions. A bone probability atlas derived from CT data was registered to the ED map in order to filter out misclassified voxels. Finally, DRRs were reconstructed from bone‐enhanced images as well as from ED maps. Results: The proposed MRI sequence with subsequent Dixon reconstruction and probabilistic filtering makes it possible to classify cortical bone, soft tissue and adipose tissue in the pelvis and yields ED maps and corresponding DRRs. Bowel content was misclassified as cortical bone or air and compromised the segmentation in some slices as well as in the DRRs. Automatic probabilistic atlas filtering can significantly reduce artifacts induced by bowel content without affecting pelvic bone structures markedly. In total, the artifact/bone fraction dropped from 1.7 before filtering to 0.2 after filtering. The average reduction of artifact volume is 87%, and the average bone preservation is 99%. Remaining artifacts are spatially close to the true bone in areas of positive bone probability. Conclusion: This study demonstrated the feasibility of generating realistic ED maps of the pelvis by using MRI only. The method has the potential to become an essential component of emerging applications such as MR‐only‐based radiation therapy planning. All authors have the following relevant financial interest or relationship to disclose with regard to the subject matter of this presentation: Company name: Philips Research; Type of relationship: Employee
TU‐G‐144‐01: Image‐Guided Focused Ultrasound Therapy: Advanced Modeling, Control and Treatment Strategies
Authors: Carol M , Christensen D , Neufeld E
Published: July 2013

Therapeutic ultrasound is now a commercial reality with a growing number of potential clinical indications. The success of future applications will depend in part on our level of understanding of acoustic propagation within the human body and the effects ultrasound produces as interacts with various tissues and tissue interfaces. Therefore, advanced numerical/computer modeling will play a pivotal role in the development of treatment strategies, treatment planning (optimization) and real‐time treatment control. This symposium will focus on current advanced modeling efforts for various anatomical sites, patient‐specific modeling, modeling of effects on tissues (e.g., mechanical, thermal, non‐linear propagation), therapy‐specific modeling (e.g., hyperthermia, thermal ablation, drug delivery, BBB opening) as well as the associated software and hardware demands for high performance computing (e.g., real‐time imaging, parallelization). Furthermore, the technological development and clinical implementation of imaged‐guided high intensity focused ultrasound (HIFU) will be contrasted with those of stereotactic radiation therapy (SRT) highlighting the fact that many HIFU effects can be imaged in real‐time and used for feedback control.

Learning Objectives:

1. Appreciate similarities and differences between image‐guided HIFU therapy and SRT. What can be predicted in regard to HIFU based on the long clinical experience with SRT?

2. Understand the application of therapeutic ultrasound in various complex anatomical regions, including tradeoffs for clinicaleffectiveness. Learn what and how sound‐tissue interactions effects can be modeled today. Learn about ultrasound modeling approaches and treatment strategies for personalized HIFU therapy.

3. Learn how model prediction techniques can improve the speed of 3D‐MR temperature imaging. Understand how HIFU delivery can benefit from real‐time feedback control. Learn about the development and application of multiphysics models in therapeutic ultrasound.
WE‐A‐108‐05: Experimental and Computational Dosimetric Characterization of the Xoft AxxentTM Electronic Brachytherapy Source Within a Titanium Cervical Applicator
Authors: Simiele S , Pike T , Micka J , DeWerd L
Published: July 2013

Purpose: To provide a dosimetric characterization of the Xoft Axxent™ source within a titanium cervical applicator using liquid water dosimetry and Monte Carlo simulation methods. Methods: Absorbed dose to water measurements were performed using an annulus of Virtual Water™ (referred to as the Captains Wheel) that is mounted in a water phantom to allow measurements in liquid water. The Captains Wheel permits the precise placement of TLD‐100 microcubes around the applicator using aluminum radial gauges of radii 1, 2, 3, and 4 cm. This geometry allows determination of the dose‐rate constant, radial dose function, and azimuthal anisotropy. The air‐kerma strength was measured using the Attix Free‐Air Chamber at the UWADCL and well chamber measurements were completed using two Standard Imaging HDR1000 Plus well chambers. Measurements were performed using two Axxent™ sources calibrated at NIST. Monte Carlo simulations were also performed using MCNP5 based on specifications provided by the manufacturer to calculate the dose‐rate constant, radial dose function, and polar anisotropy. MCNP5 was used to generate the source spectrum exiting the titanium applicator for comparison to the experimental results. Results: The Monte Carlo dose‐rate constant was 3.6% higher than the experimentally determined dose‐rate constant. The radial dose value at 2 cm calculated for a 6 mm pullback distance was 3.5% lower when compared to the measured radial dose value at 2 cm. The azimuthal anisotropy measurements were normalized to the smallest TLD reading at a given distance, with a maximum anisotropy value of 1.27 at 2 cm. Conclusion: TG‐43 parameters were experimentally determined and compared to Monte Carlo simulations for the Axxent™ source within a titanium applicator. Conflict of Interest: Xoft Inc. provided financial support, sources, and applicators. Xoft Inc. provided financial support, sources, and applicators.
WE‐A‐108‐10: Using CT/MR Compatible Multichannel Vaginal Cylinder for Vaginal Cuff Treatment Can Reduce Dose to the Bladder and Rectum
Authors: Rybak M , Huang L , Blair H , Xia P
Published: July 2013

Purpose: To compare treatment plans using CT/MR multichannel vaginal cylinder (MC) with traditional plans using a single channel cylinder (SC) for vaginal cuff HDR treatment. Methods: Seven patients with 21 clinical treatment sessions were planned and treated with 6–9 multiple channels. On the CT scan of each session, the bladder and rectum were manually contoured and the planning treatment volume (PTV) was defined as 0.5cm expansion from the cylinder surface with 4cm treatment length. The MC applicators were identified based on a reconstructed model from the treatment planning system. An inverse planning optimization (IPSA) was used for MC plans to achieve the desired objectives. For comparison, a series of corresponding SC plans were generated to achieve a similar PTV coverage as MC plans. The treatment goals were to deliver at least 95% prescription dose (Rx) to 98% (D98%) of the PTV volume for both MC and SC plans, and to limit the maximum dose (D0.1cc) and the maximum dose of 2cc volumes (D2cc) for the rectum and bladder (OARs). Descriptive statistic was used for analysis. Results: When normalized to 95% Rx, D98% for the PTV was 97.7±1.8% and 101±2.3% for MC and SC plans, respectively. For the bladder, the average D0.1cc was significantly lower in MC plans, 88.2±11.8% versus 99.2±13.4% (P <0.05) in SC plans. The average D2cc was also significantly lower in MC plans, 67.5±12.7% versus 75.6±12.4% in SC plans. For the rectum, the average D0.1cc was significantly lower in MC plans, 99.3±3.6% versus 114.0±13.0% (P<0.05) for SC plans. The average D2cc of the rectum was 80.9±5.7% and 90.2±8.5% (p<0.05), respectively. Treatment planning time for MC plans was comparable to that of SC plans. Conclusions: Compared to the traditional SC plans, MC plans significantly reduce the doses to the OARs with adequate dose coverage to the PTVs.
WE‐A‐134‐08: Modeling Cardiac Induced Lung Tissue Motion for a Quantitative Breathing Motion Model
Authors: White B , Thomas D , Lamb J , Jani S , Gaudio S , Min Y , Srinivasan S , Ennis D , Santhanam A , Low D
Published: July 2013

Purpose: To improve the accuracy of a quantitative breathing motion model by developing a cardiac‐induced lung tissue motion model from MRI data. Methods: 10 healthy volunteers were imaged on a 1.5T MR‐scanner. A total of 24 short‐axis and 18 radial views were acquired during a series of 12–15s breath‐holds. The planar views were combined to create a 3D view of the anatomy. Each view contained 30 equal‐partitioned frames beginning with the end‐diastolic cardiac phase. A single‐level 3D optical flow deformable image registration algorithm was used to measure the difference in tissue position between the end‐diastolic image and the remaining phases. The maximum displacement magnitude and direction obtained in this manner was defined as g(X0), the cardiac‐induced lung tissue motion. The motion model was assumed to be linear and the motion trajectory a product of g(X0) and h, where h was a phase‐dependent scalar that had a value of 0 at end‐diastole and 1 at the maximum tissue displacement phase. The model was evaluated by comparing the cardiac‐induced lung tissue motion, using a lower motion threshold of 0.3mm, with the residual model error. Results: The deformable image registration algorithm was found to be highly accurate. Lung tissue near the myocardium was observed to have motion as large as 5mm. The average relative error for the model was 36.5% for sub‐millimeter voxel motion. The average relative error decreased for greater voxel motion to 5.6% for >3mm voxel motion. The overall average model residual error was 0.19±0.18mm. Conclusion: The magnitude of cardiac‐induced lung tissue displacement was enough to degrade the accuracy of quantitative lung tissue motion modeling. The use of a single location‐independent phase dependent term provided suitable model accuracy. Introducing a cardiac motion term has the potential to reduce the error in breathing motion models caused by uncompensated cardiac‐induced lung tissue motion. This work supported in part by NIH R01CA096679 and R01CA116712
WE‐B‐137‐01: Plastic Scintillation Detectors: Present Status and Their Application for Quality Assurance and in Vivo Dosimetry
Authors: Beddar S , Beaulieu L
Published: July 2013

Development of plastic scintillation detector (PSD) systems for dosimetry has been evolving for two decades now. The subject area has been a topic of a keen research interest that has generated lot of publications lately. PSD systems are being introduced in the market place. Numerous PSD systems for External Beam Radiation Therapy and Brachytherapy have been proposed, most times differentiating from the original systems by slight changes in one or more components, such as the photodetector. However, few major technological and engineering innovations have enabled this new technology to be embraced by the community which helped launching these systems for commercialization (Standard Imaging, Radiadyne, etc.). Scintillation materials (plastic, scintillating fibers and liquid) have many properties that make them ideal for dosimetry including water equivalence and energy independence for MV photons, linearity with dose, dose rate independence, high spatial resolution and nanosecond fast response time. Therefore, these detectors do not require the usual conversion and/or correction factors used for other commonly used detectors to convert the dosimeter reading to absorbed dose. This evolution started with point detectors and has led to matrix arrays to respond to the ever‐increasing complexity of radiotherapy treatment fields such as IMRT, small field dosimetry or incorporating them within special patient applicators to monitor the dose in real time. This lecture will provide a brief overview of the dosimetric characteristics and properties of PSDs when exposed to high‐energy photon beams. We will present few recent applications of PSD systems as applied for EBRT and Brachytherapy (quality assurance and in vivo dosimetry applications). We will briefly present the available and soon to come commercial products related to this type of dosimetry.

Learning Objectives:

1. Review the underlying physics of organic scintillation materials and the properties of plastic scintillation detectors used in radiation dosimetry.

2. Understand the principles of the methods associated with recent innovations of PSDs in EBRT and Brachytherapy.

3. Identify potential applications that could benefit from these PSD systems.
WE‐C‐108‐07: Optimal Parameters for Variance Reduction in Monte Carlo Simulations for Proton Therapy
Authors: Ramos‐Mendez J , Perl J , Schuemann J , Shin J , Faddegon B , Paganetti H
Published: July 2013

Purpose: To determine the optimal parameters when using variance reduction techniques for the generation of treatment head phase space files for proton therapy dose calculations. To validate the doses generated from these phase spaces with respect to reference simulations. Methods: The treatment nozzles at the Francis H Burr Proton Therapy Center (setting 1) and at the University of California at San Francisco (setting 2) were modeled with the TOPAS Monte Carlo application. The geometry‐based particle splitting technique (GPST) and the secondary production cut value (SPCV) were studied. For both settings, two particle‐splitting planes were implemented. For setting 1 with a SPCV of 50 mm, the first plane was located upstream of the second scatterer, the other upstream of the aperture; the particles were split by a factor of 8 per plane. For setting 2 with a SPCV of 0.05 mm, the first plane was located upstream the water tank, the other upstream of the aperture and the split was by a factor of 16. Phase spaces in IAEA format were recorded and the normalized computational efficiency (NCE) was calculated. Full dose distributions in voxelized water phantoms for the reference and variance‐reduced simulations were analyzed. In addition, dose calculation in a patient was simulated to compare the performance. Results: NCE values of 20 and 36 for the setting 1 and 2 were reached respectively. In all cases, percentage difference between dose profiles with and without variance reduction is within the statistical precision of 2%, 1 standard deviation. For the patient case, a NCE of 78 was reached while the 97.8% of the voxels passed the 3%/3mm gamma test. Conclusion: By considering the GPST in combination with the SPCV, considerable gain in efficiency can be achieved without compromising the dosimetric accuracy. NIH/NCI under R01 CA 140735‐01. CONACyT
WE‐C‐WAB‐10: Estimation of 3D Fluoroscopic Treatment Images Using a Single Cine EPID Image
Authors: Mishra P , Li R , Williams C , Berbeco R , Lewis J
Published: July 2013

Purpose: A novel algorithm for volumetric time‐varying image estimation and 3D tumor localization from a single cine EPID image. Methods: Generating a volumetric fluoroscopic (time‐varying) image from a single EPID image is a two‐step process. In the first step a patient‐specific motion model is constructed and in the second step the motion model is updated according to the information in the EPID image. The patient‐specific motion model is a compact representation of lung motion represented in displacement vector fields (DVFs). DVFs are calculated through deformable image registration of a reference 4DCT image (typically peak‐exhale) to the 4DCT images corresponding to different phases of a breathing cycle. The salient characteristics in the DVFs are captured in a compact representation through Principal Component Analysis (PCA). PCA decouples the spatial and temporal components of the DVFs. Spatial information is represented in eigenvectors and the temporal information is represented by eigen‐coefficients. To generate a new volumetric image, the eigen coefficients are updated via cost function optimization based on DRRs and projection images. The updated eigen‐coefficients are then used to obtain updated DVFs that in turn give the volumetric image corresponding to the EPID image. Results: The algorithm was tested on 1) XCAT digital phantom data based on 8 irregular patient breathing patterns; and 2) patient cine EPID images acquired during SBRT. The root‐mean‐squared vector tumor localization error is (0.73 ± 0.63) mm for the XCAT data and (1.9 ± 1.6) mm for the patient data. Conclusions: We introduced a novel method of estimating volumetric time‐varying images from a single cine EPID image. This is the first method to estimate volumetric time‐varying images from MV cine EPID, and has the potential to provide 3D information with no additional imaging dose to the patient. The project described was supported, in part, by an RSNA Research Scholar Grant and Award Number R21CA156068 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA, National Cancer Institute or the National Institutes of Health.
WE‐C‐WAB‐11: Improved the Accuracy of Prostate Delineation for Ultrasound‐Guided CT‐Based Treatment Planning in Prostate HDR Brachytherapy: A Pilot Study with MRI Validation
Authors: Yang X , Rossi P , Ogunleye T , Curran W , Liu T
Published: July 2013

Purpose: In CT‐based prostate High‐Dose‐Rate (HDR) brachytherapy, catheter (needle) placement is commonly performed under the guidance of intra‐operative transrectal ultrasound (TRUS), while treatment planning is based on post‐operative CT images. The main challenge for CT‐based treatment planning is to accurately define target (prostate) volume in CT images due to the poor soft‐tissue contrast. To overcome this limitation, we propose a novel approach that integrates intra‐operative ultrasound‐based prostate volume into treatment planning through TRUS‐CT fusion based on the catheter locations. Methods: Our approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1–3 minutes. These TRUS images are then used to create prostate contours. The HDR catheters are reconstructed from the intra‐operative TRUS and post‐operative CT images, and subsequently used as landmarks for the TRUS‐CT image fusion using a fuzzy‐to‐deterministic algorithm. After TRUS‐CT fusion, the TRUS‐based prostate volume is deformed to the CT images for treatment planning. This technique was evaluated through two studies: a prostate‐phantom study and a pilot study with 9 patients undergoing HDR treatment for prostate cancer. The accuracy of our approach is assessed through the locations of 3 implanted fudicial (gold) markers, as well as previous T2‐weighted MR images of patients. Results: For the phantom study, the mean gold‐marker displacement was 0.41±0.11 mm. For the 9 patients, the mean gold‐marker displacement of was 1.09±0.25mm; the prostate volume difference between our approach and the MRI‐based volume was 6.99±0.68%, and the prostate volume Dice Overlap Coefficient was 92.66±1.17%. Conclusion: We have developed a novel approach to improve prostate contours utilizing intra‐operative TRUS‐based prostate volume in CT‐based prostate HDR treatment planning, demonstrated its clinical feasibility, and validated its accuracy with MRIs. This technique will improve prostate delineation, enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.
WE‐C‐103‐06: An Automated CT Quality Control Program
Authors: Christianson O , Winslow J , Solomon J , Samei E
Published: July 2013

Purpose: Daily image quality assessment is an essential part of a rigorous quality control program. Time constraints; however, make it difficult to fully analyze image quality daily. Therefore, the goal of this work was to develop a robust automated CT quality control program to extract meaningful image quality metrics including artifact analysis, noise texture measurement, and the calculation of a detectability index. Methods: The ACR CT phantom was scanned on five different CT scanners. The automated algorithm was used to calculate the standard metrics including HU accuracy, CNR, noise, and uniformity as well as more advanced metrics including the MTF, NPS, detectability index, and artifact detection. To validate the automated program, the HU accuracy, CNR, and noise metrics were compared to measurements conducted by a human observer. Additionally, the 10th percentile of the MTF (MTF 10) was compared the high‐contrast resolution and the detectability index was compared to the low‐contrast detectability recorded by the human observer using the Spearman rank sum correlation. Results: The HU, CNR, and noise determined by the automated algorithm agreed well with the human observer measurements (0.12%, 5.1%, and 7.1% difference for the HU, CNR, and noise respectively). Further, there was a strong correlation between MTF10 and the observer high‐contrast resolution as well as the detectability index and the low‐contrast detectability (rho=1 in both cases). Conclusion: There was a strong agreement between the results of the automated quality control program and the human observer measurements. Further, the Fourier based MTF and detectability index were found to correlate strongly with observer image quality assessment. Therefore, this automated quality control program offers a viable alternative for routine image quality assessment of the ACR phantom.
WE‐C‐103‐08: Automated Tool for Determining Pulmonary Nodule Elasticity to Distinguish Malignant Nodules
Authors: Negahdar M , Loo B , Diehn M , Tian L , Fleischmann D , Maxim P
Published: July 2013

Purpose: To develop and validate an automated method of determining pulmonary nodule (PN) elasticity against a manual contouring method, and preliminarily assess its ability to distinguish malignant tissue by comparing the elasticities of malignant PNs treated with stereotactic ablative radiotherapy (SABR) with those of the lung. Methods: We analyzed breath‐hold images of 30 patients with malignant PNs who underwent SABR in our department. A parametric nonrigid transformation model based on multilevel B‐spline guided by Sum of Squared Differences similarity metric was applied on breath‐hold images to determine the deformation map. The Jacobian of the calculated deformation map, which is directly related to the volume changes between the two respiratory phases, was calculated. Next, elasticity parameter will be derived by calculating the ratio of the Jacobian of the PN to the Jacobian of a 1cm region of lung tissue surrounding the tumor (E‐ROI) as well as the Jacobian of the whole lung (E‐Lung). Results: For the first group of 15 patients we evaluated the volumetric changes of PNs and the lung from the maximum exhale phase to the maximum inhale phase, whereas the reverse was done for the second group of 15 patients. For the first group, mean and standard deviation for E‐ROI and E‐Lung were 0.91±0.09 and 0.86±0.18, respectively, which was verified by the manual method. For the second group, E‐ROI and E‐Lung were 1.34±0.27 and 1.57±0.51, respectively. These results demonstrate that the elasticity of the PNs was less than that of the surrounding lung (p<0.0037). Conclusion: We developed an automated tool to determine the elasticity of PNs based on deformable image registration of breath‐hold images. The tool was validated against manual contouring. Preliminarily, PN elastometry distinguishes proven malignant PNs from normal tissue of lung, suggesting its potential utility as a non‐invasive diagnostic tool to differentiate malignant from benign PN. This Study is suuported by DoD LCRP 2011, Grant# W81XWH‐12‐1‐0286
WE‐C‐103‐09: Investigation of Demons Deformable Registration‐Based Methods to Measure Lung CT Texture Change Over Time
Authors: Cunliffe A , Armato S , Fei X , Tuohy R , Al‐Hallaq H
Published: July 2013

Purpose: To compare three demons registration‐based methods to identify spatially matched regions in serial computed tomography (CT) scans for use in texture analysis. Methods: Two normal thoracic CT scans were collected from 27 patients. Over 1,000 regions of interest (ROIs) were randomly placed in the lungs of each baseline scan. Anatomically matched ROIs in the corresponding follow‐up scan were placed by mapping the baseline scan ROI center pixel to (1) the original follow‐up scan, (2) the follow‐up scan resampled to match the baseline scan voxel size, and (3) the follow‐up scan aligned to the baseline scan through affine registration. Mappings used the vector field obtained through demons deformable registration of each follow‐up scan variant to the baseline scan. 140 texture features were calculated in all ROIs. Feature value differences between paired ROIs were evaluated using Bland‐Altman analysis. For each feature, (1) the mean feature value change and (2) the distance spanned by the 95% limits of agreement were normalized to the mean feature value to obtain, respectively, normalized bias (nBias) and normalized range of agreement (nRoA). Paired Student's t‐tests were used to compare nBias across the three methods and nRoA across the three methods. Results: For 20 features with low variability (nRoA<20%), significant differences in nBias existed among the three methods. For every feature, the lowest nBias was achieved when feature values were calculated on original follow‐up scans. nRoA was not significantly increased, indicating low variability in feature value change. Conclusion: Three methods to facilitate texture analysis of serial CT scans were evaluated. The bias in feature value change between matched ROIs was minimized with original follow‐up scans, using demons deformation to identify corresponding ROIs between scans. This approach could facilitate future measurement of pathologic change between CT scans without necessitating calculation of feature values on deformed scans. This work was supported, in part, by The Coleman Endowment through The University of Chicago Comprehensive Cancer Center, National Science Foundation Research Experience for Undergraduates (NSF REU) Award No. 1062909, and National Institutes of Health (NIH) Grant Nos. S10 RR021039, P30 CA14599, and T32 EB002103‐23.
WE‐C‐103‐12: Individually Optimized Contrast Injection in CT Angiography for the Diagnosis of Pulmonary Thromboembolic Disease
Authors: Xue M , Zhang H , Kligerman S , D' Souza W , Klahr P , Lu W
Published: July 2013

Purpose: To improve the diagnostic quality of CT angiography (CTPA) for pulmonary thromboembolic disease by individually optimizing a bi‐phasic contrast injection function to achieve uniform target contrast enhancement. To compare the results against a previously reported discrete Fourier transform (DFT) approach. Methods: This simulation study uses a retrospective dataset of 27 consecutive patients. We developed an optimization approach consists of two steps: 1. Compute the impulse enhancement function (IEF) based on the test bolus scan; 2. Optimize the contrast injection function using the IEF in order to achieve uniform target enhancement. We chose a bi‐phasic contrast injection function in which the injection rates and durations are optimized by minimizing the difference between its contrast enhancement curve and the uniform target enhancement curve. The contrast volume is limited either to the clinical standard of 65 mL or to the same amount used in the DFT approach. The optimization approach is compared against the DFT approaches in terms of the root mean square errors (RMSE) from the uniform target contrast enhancement curve and the contrast volume used. Results: When the contrast volume is limited to 65 mL, the optimization approach produces contrast enhancement significantly (p<.00001) closer to the uniform target contrast enhancement (mean RMSE 17 HU) than the DFT approach (mean RMSE 56 HU), for which the contrast volume is not even limited. When the contrast volume is limited to be the same amount used in the DFT approach, the optimization approach still generate significantly improved contrast enhancement than the DFT approach (RMSE 44 HU vs. 56 HU, p<.0099). Conclusion: The optimization approach generates individually optimized bi‐phasic injection functions yielding significantly improved contrast enhancement than the DFT approach. Its implementation in clinic has great potential to improve the diagnostic quality of CTPA. This work is supported in part by Philips Healthcare, Inc.
WE‐C‐134‐01: The Art of the Image: The Identification and Remediation of Image Artifacts in Projection Radiography
Authors: Walz‐Flannigan A , Geiser W
Published: July 2013

When it comes to diagnostic image quality, the radiologist may the ultimate arbiter of quality for the purposes of deciding whether an image meets diagnostic criteria, but in the clinical environment the medical physicist is the primary expert on imaging science and technology and as such serves an important role as “image detective” when it comes to identifying the root causes of image artifacts that can mimic or mask diagnostic features in medical images. In this course examples of common and interesting artifacts linked to various imaging system components in general radiography and mammography clinical imaging systems will be reviewed. These will include but are not limited to, artifacts associated with screen‐film, digital imaging receptors, and artifacts introduced by image processing. Appropriate solutions to proactively or re‐actively address image artifacts will be discussed.Note: At the end of the session an “Image Clinic” will be held wherein attendees can bring their “problem” images for diagnosis by the experts.

Learning Objectives:

1. Have a renewed appreciation for the variety of causes and presentation of imaging artifacts in general radiography and mammography.

2. Learn techniques to assist in the investigation, remediation and prevention of imaging artifacts.

3. Discover online educational resources to continue their education on image artifact presentation and remediation.