bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐T‐299: Small Fields Profiles Correction Through Detectors Spatial Response Functions and Field Size Dependence Analysis
Authors: Filipuzzi M , Garrigo E , Germanier A , Venencia C
Published: July 2014

Purpose:

To calculate the spatial response function of various radiation detectors, to evaluate the dependence on the field size and to analyze the small fields profiles corrections by deconvolution techniques.

Methods:

Crossline profiles were measured on a Novalis Tx 6MV beam with a HDMLC. The configuration setup was SSD=100cm and depth=5cm. Five fields were studied (200×200mm2,100×100mm2, 20×20mm2, 10×10mm2and 5×5mm2) and measured were made with passive detectors (EBT3 radiochromic films and TLD700 thermoluminescent detectors), ionization chambers (PTW30013, PTW31003, CC04 and PTW31016) and diodes (PTW60012 and IBA SFD). The results of passive detectors were adopted as the actual beam profile. To calculate the detectors kernels, modeled by Gaussian functions, an iterative process based on a least squares criterion was used. The deconvolutions of the measured profiles were calculated with the Richardson‐Lucy method.

Results:

The profiles of the passive detectors corresponded with a difference in the penumbra less than 0.1mm. Both diodes resolve the profiles with an overestimation of the penumbra smaller than 0.2mm. For the other detectors, response functions were calculated and resulted in Gaussian functions with a standard deviation approximate to the radius of the detector in study (with a variation less than 3%). The corrected profiles resolve the penumbra with less than 1% error. Major discrepancies were observed for cases in extreme conditions (PTW31003 and 5×5mm2 field size).

Conclusion:

This work concludes that the response function of a radiation detector is independent on the field size, even for small radiation beams. The profiles correction, using deconvolution techniques and response functions of standard deviation equal to the radius of the detector, gives penumbra values with less than 1% difference to the real profile. The implementation of this technique allows estimating the real profile, freeing from the effects of the detector used for the acquisition.
SU‐E‐T‐300: Spatial Variations of Multiple Off‐Axial Targets for a Single Isocenter SRS Treatment Plan in ExacTrac 6D Robotic Couch System
Authors: Kim S , Tseng T
Published: July 2014

Purpose:

To evaluate the spatial variations of multiple off‐axial targets for a single isocenter stereotactic radiosurgery (SRS) treatment plan in ExacTrac 6D robotic couch system (BrainLab AG).

Methods:

Five metallic ball bearing (BB) markers were placed sparsely in 3D off‐axial locations (non‐coplanar) inside a skull phantom as the representatives of multiple targets mimicking multiple brain metastases. The locations of the BB markers were carefully chosen to minimize overlapping of each other in a port imaging detector plane. The skull phantom was immobilized by a frameless mask and CT scanned with a BrainLab Head&Neck Localizer using a GE Optima MDCT scanner. The CT images were exported to iPlan software (BrainLab AG) and a multiple target PTV was drawn by combining all the contours of the BBs. The margin of the MLC opening was selected as 3 mm expansion outward. Two coplanar arc beams were placed to generate a single isocenter SRS plan to treat the PTV. The arc beams were delivered using Novalis Tx system with portal imaging acquisition mode per 10% temporal resolution. The locations of the BBs were visualized and analyzed with respect to the MLC aperture in the treatment plan similar to the Winston‐Lutz test.

Results:

All the BBs were clearly identified inside the MLC openings. The positional errors for the BBs were overall less than 1 mm along the rotational path of the two arcs.

Conclusion:

This study verified that the spatial deviations of multiple off‐axial targets for a single isocenter SRS treatment plan is within sub‐millimeter range in ExacTrac 6D robotic couch system. Accompanied with the Winston‐Lutz test, this test will quality‐assure the spatial accuracies of the isocenter as well as the positions of multiple off‐axial targets for the SRS treatment using a single isocenter multiple target treatment plan.
SU‐E‐T‐302: Spine SBRT Planning for Patients with Spinal Hardware
Authors: Meyer J , Hendrickson K , Bowen S , Nyflot M , Patel S , Halasz L
Published: July 2014

Purpose:

To investigate the effects of different imaging artifact correction strategies and susceptibility to setup errors for multiple spinal SBRT planning approaches in treating a patient with titanium hardware.

Methods:

Two‐step‐IMRT, IMRT and two arc VMAT plans were created for a C‐spine SBRT case with titanium hardware. The plans were normalized to the maximum point dose to the thecal sac. Density overrides for the planning CT were applied to mitigate imaging artifacts. All plans were recalculated on CTs with no density overrides and with the density of all structures set to 1g/cm^3. Setup errors up to 2mm toward the spinal cord were simulated.

Results:

The 2‐step IMRT plans achieved a mean dose to the cord of 6.5Gy compared to 8.0 and 8.7Gy for the IMRT and VMAT plans. The PTV coverage was comparable for both the 2‐step‐IMRT and the IMRT plan. The VMAT plan achieved a higher mean dose, but had hot spots >130%. If no density overrides were applied for the same MUs the increase in maximum dose to the cord was 0.3, 1.2 and 0.0% for 2‐step‐IMRT, IMRT and VMAT plans, respectively, compared to an increase of 2.3, 2.0 and 1.0% for unit density override.An isocenter shift of 2mm toward the cord resulted in an increase in maximum dose to the cord of 10.7, 8.0 and 5.0% for the 2‐step IMRT, IMRT and VMAT plan, respectively, with the absolute dose to the cord still being lowest for the 2‐step‐IMRT plan compared to the other plans without simulated setup errors.

Conclusion:

The 2‐step‐IMRT plan achieved the clinically most acceptable dose distribution. The plan quality was relatively insensitive to density overrides. The 2‐step‐IMRT plan was the most sensitive to setup errors due to the steep gradient but this was offset by the initial improvement in sparing of the cord.
SU‐E‐T‐304: Study of Secondary Neutrons From Uniform Scanning Proton Beams
Authors: Islam M , Zheng Y , Benton E
Published: July 2014

Purpose:

Secondary neutrons are unwanted byproducts from proton therapy and exposure from secondary radiation during treatment could increase risk of developing a secondary cancer later in a patient's lifetime. The purpose of this study is to investigate secondary neutrons from uniform scanning proton beams under various beam conditions using both measurements and Monte Carlo simulations.

Methods:

CR‐39 Plastic Track Nuclear Detectors (PNTD) were used for the measurement. CR‐39 PNTD has tissue like sensitivity to the secondary neutrons but insensitive to the therapeutic protons. In this study, we devised two experimental conditions: a) hollow‐phantom; phantom is bored with a hollow cylinder along the direction of the beam so that the primary proton passes through the phantom without interacting with the phantom material, b) cylindrical‐phantom; a solid cylinder of diameter close to the beam diameter is placed along the beam path. CR‐39 PNTDs were placed laterally inside a 60×20×35 cm3 phantom (hollow‐phantom) and in air (cylindrical‐phantom) at various angles with respect to the primary beam axis. We studied for three different proton energies (78 MeV, 162 MeV and 226 MeV), using a 4 cm modulation width and 5cm diameter brass aperture for the entire experiment and simulation. A comparison of the experiment was performed using the Monte Carlo code FLUKA.

Results:

The measured secondary neutron dose equivalent per therapeutic primary proton dose (H/D) ranges from 2.1 ± 0.2 to 25.42 ± 2.3 mSv/Gy for the hollow phantom study, and 2.7 ± 0.3 to 46.4 ± 3.4 mSv/Gy for the cylindrical phantom study. Monte Carlo simulations predicated neutron dose equivalent from measurements within a factor of 5.

Conclusion:

The study suggests that the production of external neutrons is significantly higher than the production of internal neutrons.
SU‐E‐T‐306: Study of the Reduction Technique for the Secondary Cancer Risk Due to Cone Beam CT in Image Guided Radiotherapy
Authors: Sung J , Kim D , Kim D , Baek T , Lee H , Chung W , Yoon M
Published: July 2014

Purpose:

This study evaluated the effectiveness of a thin lead sheet based simple shielding method for imaging doses from cone beam computed tomography (CBCT) in image‐guided radiotherapy (IGRT).

Methods:

The entire body, except for the region scanned by CBCT, was shielded by wrapping in a 2 mm lead sheet. Reduction of secondary doses from CBCT was measured using a radio‐photoluminescence glass dosimeter (RPLGD) placed inside an anthropomorphic phantom and changes in secondary cancer risk due to the shielding effect were estimated using BEIR VII model.

Results:

Doses to out‐of‐field organs for head‐and‐neck, chest, and pelvis scans were decreased 15∼100 %, 23∼90 %, and 23∼98 %, respectively, and the average reductions in lifetime secondary cancer risk due to the 2 mm lead shielding were 1.61, 10.4, and 12.8 persons per 100,000, respectively.

Conclusion:

This study suggests that a simple thin lead sheet based shielding method results in a non‐negligible reduction of secondary doses to out‐of‐field regions for CBCT.
SU‐E‐T‐307: Susceptibility of SBRT to Delivery Uncertainties Using RapidArc Or Dynamic IMRT
Authors: Betzel G , Tejani M , Dhabaan A
Published: July 2014

Purpose:

To determine if SBRT is more susceptible to delivery uncertainties when using either RapidArc or dynamic IMRT.

Methods:

Systematic or random uncertainties in MLC leaf positions, gantry position and dose rate were incorporated into RapidArc (RA) and sliding‐window IMRT (SW) treatment plans for retrospective SBRT lung and liver cases, which also included the use of FFF or SRS energies and therefore higher dose rates. Initial plan comparisons included homogeneity and conformity indices. Dose distributions and dose‐value histograms of original and modified plans were compared using Gamma analysis and dose indices of planned treatment volumes (PTV) and organs at risk (OAR).

Results:

SBRT lung PTVs using RA were more susceptible to systematic MLC delivery errors than SW (∼6%/mm vs. ∼4%/mm), in contrast to a previous study examining hyperfractionated prostate and head‐and‐neck treatments; esophagus and cord Dmin and Dmax values changed proportionately for both techniques; RA plans were superior globally but not locally (PTV) using Gamma analyses. Uniformly distributed random variations of 2 mm for active MLC leaves had a negligible effect on all PTV dose distributions (Dmin, Dmax < 0.4%). PTV coverage was affected equally when incorporating systematic gantry errors.

Conclusion:

RapidArc deliveries were found to be more sensitive to systematic errors in MLC leaf positions, in contrast to a previous study on hyperfractionated treatments. However, this is reasonable as both studies correlate average segmental field size or mean leaf gap with adverse dosimetric effects due to such uncertainties; mean leaf gaps for either technique in this study were similar. Gantry angle variations had similar effects on both techniques.
SU‐E‐T‐314: The Application of Cloud Computing in Pencil Beam Scanning Proton Therapy Monte Carlo Simulation
Authors: Wang Z , Gao M
Published: July 2014

Purpose:

Monte Carlo simulation plays an important role for proton Pencil Beam Scanning (PBS) technique. However, MC simulation demands high computing power and is limited to few large proton centers that can afford a computer cluster. We study the feasibility of utilizing cloud computing in the MC simulation of PBS beams.

Methods:

A GATE/GEANT4 based MC simulation software was installed on a commercial cloud computing virtual machine (Linux 64‐bits, Amazon EC2). Single spot Integral Depth Dose (IDD) curves and in‐air transverse profiles were used to tune the source parameters to simulate an IBA machine. With the use of StarCluster software developed at MIT, a Linux cluster with 2–100 nodes can be conveniently launched in the cloud. A proton PBS plan was then exported to the cloud where the MC simulation was run.

Results:

The simulated PBS plan has a field size of 10×10cm2, 20cm range, 10cm modulation, and contains over 10,000 beam spots. EC2 instance type m1.medium was selected considering the CPU/memory requirement and 40 instances were used to form a Linux cluster. To minimize cost, master node was created with on‐demand instance and worker nodes were created with spot‐instance. The hourly cost for the 40‐node cluster was .63 and the projected cost for a 100‐node cluster was $1.41. Ten million events were simulated to plot PDD and profile, with each job containing 500k events. The simulation completed within 1 hour and an overall statistical uncertainty of < 2% was achieved. Good agreement between MC simulation and measurement was observed.

Conclusion:

Cloud computing is a cost‐effective and easy to maintain platform to run proton PBS MC simulation. When proton MC packages such as GATE and TOPAS are combined with cloud computing, it will greatly facilitate the pursuing of PBS MC studies, especially for newly established proton centers or individual researchers.
SU‐E‐T‐315: The Change of Optically Stimulated Luminescent Dosimeters (OSLDs) Sensitivity by Accumulated Dose and High Dose
Authors: Han S , Choi S , Park S , Jung H , Kim M , Yi C , Yoo H , Ji Y , Kim K
Published: July 2014

Purpose:

The objective of this study is to evaluate radiation sensitivity of optical stimulated luminance dosimeters (OSLDs) by accumulated dose and high dose.

Methods:

This study was carried out in Co‐60 unit (Theratron 780, AECL, and Canada) and used InLight MicroStar reader (Landauer, Inc., Glenwood, IL) for reading. We annealed for 30 min using optical annealing system which contained fluorescent lamps (Osram lumilux, 24 W, 280 ∼780 nm). To evaluate change of OSLDs sensitivity by repeated irradiation, the dosimeters were repeatedly irradiated with 1 Gy. And whenever a repeated irradiation, we evaluated OSLDs sensitivity. To evaluate OSLDs sensitivity after accumulated dose with 5 Gy, We irradiated dose accumulatively (from 1 Gy to 5 Gy) without annealing. And OSLDs was also irradiated with 15, 20, 30 Gy to certify change of OSLDs sensitivity after high dose irradiation. After annealing them, they were irradiated with 1Gy, repeatedly.

Results:

The OSLDs sensitivity increased up to 3% during irradiating seven times and decreased continuously above 8 times. That dropped by about 0.35 Gy per an irradiation. Finally, after 30 times irradiation, OSLDs sensitivity decreased by about 7%. For accumulated dose from 1 Gy to 5 Gy, OSLDs sensitivity about 1 Gy increased until 4.4% after second times accumulated dose compared with before that. OSLDs sensitivity about 1 Gy decreased by 1.6% in five times irradiation. When OSLDs were irradiated ten times with 1Gy after irradiating high dose (10, 15, 20 Gy), OSLDs sensitivity decreased until 6%, 9%, 12% compared with it before high dose irradiation, respectively.

Conclusion:

This study certified OSLDs sensitivity by accumulated dose and high dose. When irradiated with 1Gy, repeatedly, OSLDs sensitivity decreased linearly and the reduction rate of OSLDs sensitivity after high dose irradiation had dependence on irradiated dose.
SU‐E‐T‐317: The Development of a DIBH Technique for Left Sided Breast Patients Undergoing Radiation Therapy Utilizing Varians RPM System in a Community Hospital
Authors: Hasson B , Young M , Workie D , Geraghty C
Published: July 2014

Purpose:

To develop and implement a Deep Inhalation Breath Hold program (DIBH) for treatment of patients with Left‐sided breast cancer in a community hospital.

Methods:

All patients with left sided breast cancer underwent a screening free breathing CT. Evaluation of the conventional tangent treatment fields and the heart was conducted. If the heart would not be excluded using tangents, the patient then received DIBH breathe coaching. The patients returned for a 4D CT simulation. The patients breathing cycle was monitored using the Varian Real‐Time position ManagementTM (RPM) system to assess duration of DIBH, amplitude, phase and recovery time to normal breathing. Then a DIBH CT was obtained at the desired amplitude. Duplicate plans were developed for both free breathing and DIBH on the Eclipse planning system and comparison DVH's were created. The plan that provided the prescribed treatment coverage and the least doses to the OAR (heart, Lt. Lung) was determined. Those patients selected to receive treatment with DIBH were set up for treatment, and breathing was monitored using the RPM system. Practice trials were used to confirm that the amplitude, phase and recovery were consistent with findings from simulation.

Results:

10 patients have been treated using the DIBH procedure in our clinic. The DIBH patients had an average increase of 80% lung volume on DIBH, decreased lung volume receiving 50% of the dose, and decreases in the V20 dose. Significant reduction in the maximum and mean dose to the heart, as well as the dose to 1CC of the volume for the DIBH plans.

Conclusion:

Using the RPM system already available in the clinic, staff training, and patient coaching a simple DIBH program was setup. The use of DIBH has shown promise in reducing doses to the critical organs while maintaining PTV coverage for left sided breast treatments.
SU‐E‐T‐328: The Volume Effect Correction of Probe‐Type Dosimetric Detectors Derived From the Convolution Model
Authors: Looe HK , Poppe B , Harder D
Published: July 2014

Purpose:

To derive and introduce a new correction factor kV, the “volume effect correction factor”, that accounts for not only the dose averaging over the detector's sensitive volume but also the secondary electron generation and transport inclusive of the disturbance of the field of secondary electrons within the detector.

Materials and Methods:

Mathematical convolutions and Fourier's convolution theorem have been used. Monte Carlo simulations of photon pencil beams were performed using EGSnrc. Detector constructions were adapted from manufacturers' information.

Results:

For the calculation of kV, the three basic convolution kernels have to be taken into account: the dose deposition kernel KD(x) (fluence to dose), the photon fluence response kernel KM(x) (photon fluence to detector signal) and the “dose response kernel” K(x) (dose to detector signal). K(x) is calculated from FT[K(x)] = [1/sqrt(2”)]FT[KM(x)]/FT[KD(x)], where the magnitude of kV(x) can be thereby calculated for arbitrary photon beam profiles and the areanormalized K(x).

Conclusions:

n order to take into account for the dimensions of dosimetric detectors in narrow photon beams, the “volume effect correction factor” kV has been introduced into the fundamental equation of probe‐type dosimetry, and the convolution method has proven to be a method for the derivation of its numerical values. For narrow photon beams, whose width is comparable to the secondary electron ranges, kV can reach very high values, but it can be shown that the signals of small diamond detectors are well representing the absorbed dose to water averaged over the detector volume.
MO‐A‐BRD‐01: An Investigation of the Dynamic Response of a Novel Acousto‐Optic Liquid Crystal Detector for Full‐Field Transmission Ultrasound Breast Imaging
Authors: Rosenfield J.R. , Sandhu J.S. , P.J. La Riviere
Published: July 2014

Purpose:

To characterize the dynamic response of a novel acousto‐optic (AO) liquid crystal detector for high‐resolution transmission ultrasound breast imaging. Transient and steady‐state lesion contrast were investigated to identify optimal transducer settings for our prototype imaging system consistent with the FDA limits of 1 W/cm2 and 50 J/cm2 on the incident acoustic intensity and the transmitted acoustic energy flux density.

Methods:

We have developed a full‐field transmission ultrasound breast imaging system that uses monochromatic plane‐wave illumination to acquire projection images of the compressed breast. The acoustic intensity transmitted through the breast is converted into a visual image by a proprietary liquid crystal detector operating on the basis of the AO effect. The dynamic response of the AO detector in the absence of an imaged breast was recorded by a CCD camera as a function of the acoustic field intensity and the detector exposure time. Additionally, a stereotactic needle biopsy breast phantom was used to investigate the change in opaque lesion contrast with increasing exposure time for a range of incident acoustic field intensities.

Results:

Using transducer voltages between 0.3 V and 0.8 V and exposure times of 3 minutes, a unique one‐to‐one mapping of incident acoustic intensity to steady‐state optical brightness in the AO detector was observed. A transfer curve mapping acoustic intensity to steady‐state optical brightness shows a high‐contrast region analogous to the linear portion of the Hurter‐Driffield curves of radiography. Using transducer voltages between 1 V and 1.75 V and exposure times of 90 s, the lesion contrast study demonstrated increasing lesion contrast with increasing breast exposure time and acoustic field intensity. Lesion‐to‐background contrast on the order of 0.80 was observed.

Conclusion:

Maximal lesion contrast in our prototype system can be obtained using the highest acoustic field intensity and the longest breast exposure time allowable under FDA standards.

Department of Defense (DOD) Breast Cancer Research Program IDEA Award W81XWH‐11‐1‐0332; National Institutes of Health (NIH) Grant T32 EB002103‐21 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB)
MO‐A‐BRD‐04: Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non‐Small Cell Lung Cancer
Authors: Fried D , Tucker S , Zhou S , Liao Z , Mawlawi O , Ibbott G , Court L
Published: July 2014

Purpose:

To determine whether pretreatment CT texture features can improve patient risk stratification beyond conventional prognostic factors (CPFs) in stage III non‐small cell lung cancer (NSCLC).

Methods:

We retrospectively reviewed 91 patients with stage III NSCLC treated with definitive chemoradiation. All patients underwent a pretreatment diagnostic contrast enhanced CT (CE‐CT) followed by a 4D‐CT for treatment simulation. We used the average (average‐CT) and expiratory (T50‐CT) images from the 4D‐CT along with the CE‐CT for texture extraction. Histogram, gradient, co‐occurrence, gray‐tone difference, and filtration based techniques were used for texture feature extraction. Penalized Cox regression implementing cross‐validation was used for covariate selection and modeling. Models incorporating texture features from the 3 image types and CPFs were compared to models incorporating CPFs alone for overall survival (OS), local‐regional control (LRC), and freedom from distant metastases (FFDM). Predictive Kaplan‐Meier curves were generated using leave‐one‐out cross‐validation. Patients were stratified based on their predicted outcome being above/below the median. Reproducibility of texture features was evaluated using test‐retest scans from independent patients and quantified using concordance correlation coefficients (CCC). We compared models incorporating the reproducibility seen on test‐retest scans to our original models and determined the classification accuracy.

Results:

Models incorporating both texture features and CPFs demonstrated a significant improvement in risk stratification compared to models using CPFs alone in terms of OS (p=0.046), LRC (p=0.01), and FFDM (p=0.005). The average CCC was 0.89, 0.91, and 0.67 for texture features extracted from the average‐CT, T50‐CT, and CE‐CT, respectively. Incorporating reproducibility within our models yielded 80.4 (SD=3.7), 78.3 (SD=4.0), and 78.8 (SD=3.9) percent classification accuracy in terms of OS, LRC, and FFDM, respectively.

Conclusions:

Pretreatment tumor texture may provide prognostic information beyond what is obtained from CPFs. Reproducibility of CE‐CT appears inferior to average‐CT and T50‐CT; however model classification accuracy rates of approximately 80% were still achieved.
MO‐A‐BRD‐07: Feasibility of X‐Ray Acoustic Computed Tomography as a Tool for Calibration and In Vivo  Dosimetry of Radiotherapy Electron and Photon Beams
Authors: Hickling S , Hobson M , El Naqa I
Published: July 2014

Purpose:

This work simulates radiation‐induced acoustic waves to assess the feasibility of x‐ray acoustic computed tomography (XACT) as a dosimeter. XACT exploits the phenomenon that acoustic waves with amplitude proportional to the dose deposited are induced following a radiation pulse. After detecting these acoustic waves with an ultrasound transducer, an image of the dose distribution can be reconstructed in realtime.

Methods:

Monte Carlo was used to simulate the dose distribution for monoenergetic 6 MeV photon and 9 MeV electron beams incident on a water tank. The dose distribution for a prostate patient planned with a photon 4‐field box technique was calculated using clinical treatment planning software. All three dose distributions were converted into initial pressure distributions, and transportation of the induced acoustic waves was simulated using an open‐source toolkit. Ideal transducers were placed around the circumference of the target to detect the acoustic waves, and a time reversal reconstruction algorithm was used to obtain an XACT image of the dose for each radiation pulse.

Results:

For the photon water tank relative dosimetry case, it was found that the normalized acoustic signal amplitude agreed with the normalized dose at depths from 0 cm to 10 cm, with an average percent difference of 0.5%. For the reconstructed in‐plane dose distribution of an electron water tank irradiation, all pixels passed a 3%–3 mm 2D gamma test. The reconstructed prostate dose distribution closely resembled the plan, with 89% of pixels passing a 3%–3 mm 2D gamma test. For all situations, the amplitude of the induced acoustic waves ranged from 0.01 Pa to 1 Pa.

Conclusion:

Based on the amplitude of the radiation‐induced acoustic waves and accuracy of the reconstructed dose distributions, XACT is a feasible technique for dosimetry in both calibration and in vivo environments for photon and electron beams and merits further investigation.

Funding from NSERC, CIHR and McGill University. SH acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)
MO‐B‐19A‐01: MOC: A How‐To Guide
Authors: Ibbott G , Seibert J , Allison J , Frey G
Published: July 2014

Medical physicists who were certified in 2002 or later, as well as those who become certified in the future, are enrolled in Maintenance of Certification. Many physicists with life‐time certificates have voluntarily enrolled in MOC, as have physicists who volunteer their time to participate in the ABR exam development and administration processes.

MOC consists of four components: Part 1, Professional standing; Part 2, Lifelong learning and self‐assessment; Part 3, Cognitive expertise; and Part 4, Practice quality improvement. These four components together evaluate six competencies: Medical knowledge, patient care and procedural skills, interpersonal and communication skills, professionalism, practice‐based learning and improvement, and systems‐based practice.

Parts 1, 2, and 3 of MOC are fairly straightforward, although many participants have questions about the process for attesting to professional standing, the opportunities for obtaining self‐assessed continuing education, and the timing of the cognitive exam. MOC participants also have questions about Part 4, Practice Quality Improvement. PQI projects are powerful tools for improving the quality and safety of the environments in which we practice medical physics. In the current version of MOC known as “Continuous Certification” a medical physicist must have completed a PQI project within the previous three years, at the time of the ABR's annual look‐back each March. For the first “full” annual look‐back in March 2016, diplomates will be given an additional year, so that a PQI project completed in 2012, 2013, 2014, or 2015 will fulfill this requirement.

Each component of MOC will be addressed, and the specifics of interest to medical physicists will be discussed.

Learning Objectives:

Understand the four components and six competencies evaluated by MOC.

Become familiar with the annual requirements of Continuous Certification.

Learn about opportunities for Practice Quality Improvement projects.

Understand refinements occurring in the MOC program.
MO‐C‐17A‐01: BEST IN PHYSICS (JOINT IMAGING‐THERAPY) – A Method to Estimate Deformable Dose Accumulation Errors in Patients with Cancers of the Head and Neck
Authors: Pukala J , Langen K , Bova F , Staton R , Manon R , Kelly P , Meeks S
Published: July 2014

Purpose:

The dosimetric uncertainties of deformable image registration (DIR) dose accumulation are not well understood. A clinically relevant method to estimate the dosimetric error of dose accumulation for head and neck (H&N) cancer patients is presented.

Methods:

Ten virtual H&N phantoms with known deformations were registered with a commercial DIR algorithm to determine the spatial errors that might be observed when performing DIR. The spatial error distributions obtained from the phantoms were then applied to 10 clinical H&N patients to simulate the potential errors that could occur. These errors were simulated by selecting ROI‐specific error vectors at random from the phantom error distributions and adding them to the deformation vector fields (DVFs) of the patient registrations. This method of error simulation was evaluated for its ability to accurately represent real DVF errors. Finally, dose was deformed using the DVFs of the patient registrations without any added errors (nonperturbed DVFs) and compared to the dose deformed using the simulated error DVFs.

Results:

The mean error introduced by the error simulation method was less than 1% for all evaluated DVH endpoints. For the patient cases, the following ranges were observed for the median simulated DIR dosimetric errors: brainstem D2%: 0% to 2%, cord D2%: 0% to 0.5%, mandible D2%: 0% to 0.5%, left parotid Dmean: 0.5% to 4.2%, right parotid Dmean: 0.2% to 1.3%. Maximum dosimetric errors were: brainstem D2%: 6.5%, cord D2%: 1.3%, mandible D2%: 1.1%, left parotid Dmean: 13%, right parotid Dmean: 35%.

Conclusions:

A method for simulating DIR errors for dose accumulation in clinical cases was presented and evaluated. The sample DIR algorithm assessed with this method showed that large dosimetric errors are possible when dose accumulation is performed and that DIR should be used with caution.

This work was partially supported by a research grant from Accuray Inc.
MO‐C‐17A‐03: A GPU‐Based Method for Validating Deformable Image Registration in Head and Neck Radiotherapy Using Biomechanical Modeling
Authors: Neylon J , Min Y , Qi S , Kupelian P , Santhanam A
Published: July 2014

Purpose:

Deformable image registration (DIR) plays a pivotal role in head and neck adaptive radiotherapy but a systematic validation of DIR algorithms has been limited by a lack of quantitative high‐resolution groundtruth. We address this limitation by developing a GPU‐based framework that provides a systematic DIR validation by generating (a) model‐guided synthetic CTs representing posture and physiological changes, and (b) model‐guided landmark‐based validation.

Method:

The GPU‐based framework was developed to generate massive mass‐spring biomechanical models from patient simulation CTs and contoured structures. The biomechanical model represented soft tissue deformations for known rigid skeletal motion. Posture changes were simulated by articulating skeletal anatomy, which subsequently applied elastic corrective forces upon the soft tissue. Physiological changes such as tumor regression and weight loss were simulated in a biomechanically precise manner. Synthetic CT data was then generated from the deformed anatomy. The initial and final positions for one hundred randomly‐chosen mass elements inside each of the internal contoured structures were recorded as ground truth data. The process was automated to create 45 synthetic CT datasets for a given patient CT. For instance, the head rotation was varied between +/− 4 degrees along each axis, and tumor volumes were systematically reduced up to 30%. Finally, the original CT and deformed synthetic CT were registered using an optical flow based DIR.

Results:

Each synthetic data creation took approximately 28 seconds of computation time. The number of landmarks per data set varied between two and three thousand. The validation method is able to perform sub‐voxel analysis of the DIR, and report the results by structure, giving a much more in depth investigation of the error.

Conclusions:

We presented a GPU based high‐resolution biomechanical head and neck model to validate DIR algorithms by generating CT equivalent 3D volumes with simulated posture changes and physiological regression.
MO‐C‐17A‐05: A Three‐Dimensional Head‐And‐Neck Phantom for Validation of Kilovoltage‐ and Megavoltage‐Based Deformable Image Registration
Authors: Kirby N , Singhrao K , Pouliot J
Published: July 2014

Purpose:

To develop a three‐dimensional (3D) deformable head‐and‐neck (H&N) phantom with realistic tissue contrast for both kilovoltage and megavoltage computed tomography and use it to objectively evaluate deformable image registration (DIR) algorithms.

Methods:

The phantom represents H&N patient anatomy. It is constructed from thermoplastic, which becomes pliable in boiling water, and hardened epoxy resin. Using a system of additives, the Hounsfield unit (HU) values of these materials were tuned to mimic anatomy for both kilovoltage (kV) and megavoltage (MV) imaging. The phantom opened along a sagittal midsection to reveal nonradiopaque markers, which were used to characterize the phantom deformation. The deformed and undeformed phantom was scanned with kV and MV computed tomography. Additionally, a calibration curve was created to change the HUs of the MV scans to be similar to kV HUs, (MC). The extracted ground‐truth deformation was then compared to the results of two commercially available DIR algorithms, from Velocity Medical Solutions and MIM Software.

Results:

The phantom produced a 3D deformation, representing neck flexion, with a magnitude of up to 8 mm and was able represent tissue HUs for both kV and MV imaging modalities. The two tested deformation algorithms yielded vastly different results. For kV‐kV registration, MIM made the lowest mean error, and Velocity made the lowest maximum error. For MV‐MV, kV‐MV, and kV‐MC Velocity produced both the lowest mean and lowest maximum errors.

Conclusion:

The application of DIR across different imaging modalities is particularly difficult, due to differences in tissue HUs and the presence of imaging artifacts. For this reason, DIR algorithms must be validated specifically for this purpose. The developed H&N phantom is an effective tool for this purpose.
MO‐C‐17A‐11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images
Authors: Zhen X , Chen H , Yan H , Zhou L , Mell L , Yashar C , Jiang S , Jia X , Gu X , Cervino L
Published: July 2014

Purpose:

To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high‐dose‐rate (HDR) gynecological brachytherapy.

Method:

We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point‐matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi‐automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the random walks algorithm. In the subsequent point‐matching step, a feature‐based thin‐plate spline‐robust point matching (TPS‐RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B‐spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR‐free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions.

Results:

The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

Conclusions:

We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation.

This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940).
MO‐D‐BRF‐01: Pediatric Treatment Planning II: The PENTEC Report On Normal Tissue Complications
Authors: Constine L , Hodgson D , Bentzen S
Published: July 2014

With advances in multimodality therapy, childhood cancer cure rates approach 80%. However, both radiotherapy and chemotherapy may cause debilitating or even fatal ‘late effects’ that are critical to understand, mitigate, or prevent. QUANTEC identified the uncertainties relating to side‐effects of adult treatments, but this is more complicated for children in whom a mosaic of tissues develops at different rates and temporal sequences. Childhood cancer survivors have long life expectancy and may develop treatmentinduced secondary cancers and severe organ/tissue injury decades after treatment. Collaborative long‐term observational studies and clinical research programs for survivors of pediatric and adolescent cancer provide some dose‐response data for follow‐up periods exceeding 40 years. Data analysis is challenging due to the influence of both therapeutic and developmental variables. PENTEC is a group of radiation oncologists, pediatric oncologists, subsepcialty physicians, medical physicists, biomathematic modelers/statisticians, and epidemiologists charged with conducting a critical synthesis of existing literature aiming to: critically analyze radiation dose‐volume effects on normal tissue tolerances as a function of age/development in pediatric cancer patients in order to inform treatment planning and improve outcomes for survivors; describe relevant physics issues specific to pediatric radiotherapy; propose dose‐volumeoutcome reporting standards to improve the knowledge base to inform future treatment guidelines. PENTEC has developed guidelines for systematic literature reviews, data extraction tolls and data analysis.

This education session will discuss:1. Special considerations for normal tissue radiation response of children/adolescents, e.g. the interplay between development and radiotherapy effects.2. Epidemiology of organ/tissue injuries and secondary cancers.3. Exploration of dose‐response differences between children and adults4. Methodology for literature review, data mining of outcomes databases, and NTCP or longitudinal modeling of doseresponse. 5. PENTEC goals and timetable.

Learning Objectives:

Understand important differences between normal tissue effects of radiation therapy in pediatric and adult patients.

Be able to identify situations where there is ‘interplay’ between organ development and radiation‐induced complications.

Identify methods to systematically extract quantitative dose‐volumeresponse relationships from existing outcomes databases.

Provide guidance for the medical physicist to properly understand, implement, guide and control contemporary technology and applications in pediatric radiation oncology.
MO‐E‐16A‐01: Preparing for the ABR Therapy Medical Physics Exam
Authors: Galavis P , Chinsky B , Becker S
Published: July 2014

Preparing for all three parts of the Therapy ABR Physics boards is more than just studying as much material as possible. There will always be material that is missed and gaps in one's knowledge. Therefore it is crucial to understand how the materials relate to each other and to clinical experiences in order to fill those gaps.

Each part of the board exam presents its own difficulties:

Part I: Determining what material to study, most of which have been learned a long time ago.

Part II: Solving a vast array of clinical calculations rapidly by hand

Part III: Gaining crucial clinical experiences and being able to explain orally how they are performed and what they mean.

All three require different skill sets and preparation, from memorizing vast amounts of material, to rapidly recognizing and solving calculations, to being able to easily and confidently respond to oral questions about all aspects of working in the clinic and why those clinical methods and procedures are performed that way. This symposium is not a comprehensive review of all required study material. Instead it focuses on the previously mentioned problems and required skill sets that are needed for each part of the board exam. Expert Medical Physicists will share their experiences and methods to help students best prepare for the challenges of each individual exam.

Learning Objectives:

How to Prepare for Part 1 by determining study material

How to Prepare for Part 2 by learning to solve clinical calculations rapidly

How to Prepare for Part 3 by determining critical clinical experiences and learning how to answer questions orally
MO‐E‐BRF‐01: Research Opportunities in Technology for Innovation in Radiation Oncology (Highlight of ASTRO NCI 2013 Workshop)
Authors: Hahn S , Jaffray D , Chetty I , Benedict S
Published: July 2014

Radiotherapy is one of the most effective treatments for solid tumors, in large part due to significant technological advances associated with, for instance, the ability to target tumors to very high levels of accuracy (within millimeters). Technological advances have played a central role in the success of radiation therapy as an oncologic treatment option for patients. ASTRO, AAPM and NCI sponsored a workshop “Technology for Innovation in Radiation Oncology” at the NCI campus in Bethesda, MD on June 13–14, 2013. The purpose of this workshop was to bring together expert clinicians and scientists to discuss the role of disruptive technologies in radiation oncology, in particular with regard to how they are being developed and translated to clinical practice in the face of current and future challenges and opportunities. The technologies discussed encompassed imaging and delivery aspects, along with methods to enable/facilitate application of them in the clinic. Measures for assessment of the performance of these technologies, such as techniques to validate quantitative imaging, were reviewed. Novel delivery technologies, incorporating efficient and safe delivery mechanisms enabled by development of tools for process automation and the associated field of oncology informatics formed one of the central themes of the workshop. The discussion on disruptive technologies was grounded in the need for evidence of efficacy. Scientists in the areas of technology assessment and bioinformatics provided expert views on different approaches toward evaluation of technology efficacy. Clinicians well versed in clinical trials incorporating disruptive technologies (e.g. SBRT for early stage lung cancer) discussed the important role of these technologies in significantly improving local tumor control and survival for these cohorts of patients. Recommendations summary focused on the opportunities associated with translating the technologies into the clinic and assessing their efficacy, and provided a glimpse into the future.

Learning Objectives:

To understand the impact of technology on the field of radiation therapy

To learn about the trends of technology development for the field of radiation oncology

To understand the opportunities for in innovative technology research
MO‐E‐17A‐03: Monte Carlo CT Dose Calculation: A Comparison Between Experiment and Simulation Using ARCHER‐CT
Authors: Liu T , Du X , Su L , Gao Y , Ji W , Zhang D , Shi J , Liu B , Kalra M , Xu X
Published: July 2014

Purpose:

To compare the CT doses derived from the experiments and GPU‐based Monte Carlo (MC) simulations, using a human cadaver and ATOM phantom.

Methods:

The cadaver of an 88‐year old male and the ATOM phantom were scanned by a GE LightSpeed Pro 16 MDCT. For the cadaver study, the Thimble chambers (Model 10×5−0.6CT and 10×6−0.6CT) were used to measure the absorbed dose in different deep and superficial organs. Whole‐body scans were first performed to construct a complete image database for MC simulations. Abdomen/pelvis helical scans were then conducted using 120/100 kVps, 300 mAs and a pitch factor of 1.375:1. For the ATOM phantom study, the OSL dosimeters were used and helical scans were performed using 120 kVp and x, y, z tube current modulation (TCM). For the MC simulations, sufficient particles were run in both cases such that the statistical errors of the results by ARCHER‐CT were limited to 1%.

Results:

For the human cadaver scan, the doses to the stomach, liver, colon, left kidney, pancreas and urinary bladder were compared. The difference between experiments and simulations was within 19% for the 120 kVp and 25% for the 100 kVp. For the ATOM phantom scan, the doses to the lung, thyroid, esophagus, heart, stomach, liver, spleen, kidneys and thymus were compared. The difference was 39.2% for the esophagus, and within 16% for all other organs.

Conclusion:

In this study the experimental and simulated CT doses were compared. Their difference is primarily attributed to the systematic errors of the MC simulations, including the accuracy of the bowtie filter modeling, and the algorithm to generate voxelized phantom from DICOM images. The experimental error is considered small and may arise from the dosimeters.

R01 grant (R01EB015478) from National Institute of Biomedical Imaging and Bioengineering