bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐T‐255: Optimized Supine Craniospinal Irradiation with Image‐Guided and Field Matched Beams
Authors: Jiang Z , Holupka E , Naughton J , Williams H , Galper S , Huang K
Published: July 2014

Purpose:

Conventional craniospinal irradiation (CSI) challenges include dose inhomogeneity at field junctions and position uncertainty due to the field divergence, particular for the two spinal fields. Here we outline a new supine CSI technique to address these difficulties.

Methods:

Patient was simulated in supine position. The cranial fields had isocenter at C2/C3 vertebral and were matched with 1st spinal field. Their inferior border was chosen to avoid the shoulder, as well as chin from the 1st spine field. Their collimator angles were dependent on asymmetry jaw setting of the 1st spinal field. With couch rotation, the spinal field gantry angles were adjusted to ensure, the inferior border of 1st and superior border of 2nd spinal fields were perpendicular to the table top. The radio‐opaque wire position for the spinal junction was located initially by the light field from an anterior setup beam, and was finalized by the portal imaging of the 1st spinal field. With reference to the spinal junction wire, the fields were matched by positioning the isocenter of the 2nd spinal field. A formula was derived to optimize supine CSI treatment planning, by utilizing the relationship among the Yjaw setting, the spinal field gantry angles, cranial field collimator angles, and the spinal field isocenters location. The plan was delivered with portal imaging alignment for the both cranial and spinal junctions.

Results:

Utilizing this technique with matching beams, and conventional technique such as feathering and forwarding planning, a homogenous dose distribution was achieved throughout the entire CSI treatment volume including the spinal junction. Placing the spinal junction wire visualized in both spinal portals, allows for precise determination and verification of the appropriate match line of the spine fields.

Conclusion:

This technique of optimization supine CSI achieved a homogenous dose distributions and patient localization accuracy with image‐guided and matched beams.
SU‐E‐T‐282: Remove Field‐Size Limitation Using the Combination of Collimator Rotation and Jaw Tracking with MLC Aperture
Authors: Shiu A , Chiu R , Cumming D , Chung E , Ragab O , Cui G
Published: July 2014

Purpose:

this study is to investigate the use of variable‐collimators and/or collimator‐rotation to overcome the field‐size limitation on TrueBeamSTX.

Methods:

The maximum static field‐size is 22‐ × 40‐cm for TrueBeamSTX and 40‐ × 40‐cm for Trilogy. Maximum displacement between adjacent leaf ends at a single carriage position is 15‐cm for TBSTX and 14‐cm for Trilogy. Our current practice for IMRT treatment at Trilogy is as follows, if MLC leaves travel more than 13.5 cm in the MLC traveling direction, the treatment fields will be split at isocenter to two groups of IMRT fields to cover the large tumors. However, TrueBeamSTX is not limited in this way because the jaws can track each segments of the MLC aperture. Radiation treatment plans for ten patients (different tumor sites) treated at Trilogy with one dimension of the field is greater than 22 cm and the other dimension is less than or equal to 22 cm. All the plans will be converted with jaws tracking each segment's MLC aperture. If necessary, the collimator rotation will also be applied for the planning. Comparison of the converted plans with the original clinical plans will be evaluated and based on the following criterion: (1) tumor coverage; (2) dose sparing to the critical structures; and (3) treatment time.

Results:

The tumor coverage is compatible between two plans. The dose of critical structures was evaluated in the cumulative DVH for spinal cord, parotids, and brainstem at V20 and V5. In all patients, the maximum reduction in V20 was never more than 5% and was typically less than 1%. In V5, the maximum reduction was 10% and 3% respectively. The significant difference can be found in using the variable collimator to shorten the treatment time.

Conclusion:

We conclude that we will use the variable‐collimator for all IMRT cases on TrueBeamSTX and as a potential backup machine for Trilogy.
SU‐E‐T‐301: Spectral Comparison of the Xoft and Zeiss 50 KVp X‐Ray Systems
Authors: Kelley L , Holt R , Rusch T
Published: July 2014

Purpose:

To compare x‐ray spectra of the 50 kVp Xoft Axxent™ and Zeiss INTRABEAM™ x‐ray sources after filtration by saline‐filled balloons applicators or spherical polymer applicators, respectively.

Methods:

Measurements were made for 3.5, 4.0 and 5.0 cm diameter applicators using an AmpTek model XR‐100T‐CdTe cadmium telluride spectrometer with 100 μm diameter collimating aperture and model PX4 digital pulse processor. Spectra were then corrected for escape processes using AmpTek XRF‐FP Escape software. Both Axxent and INTRABEAM sources were operated at 50 kV and 40 μA to eliminate pulse saturation. The balloon or spherical applicator was placed in a centering fixture in contact with the collimator cap. The distance through the collimator housing from the applicator surface to the spectrometer's beryllium entrance window was nominally 52mm. Approximately 500,000 counts were collected for each spectrum.

Results:

Measured spectra in all cases had a broad Bremsstrahlung continuum with subtle differences in characteristic low energy x‐rays lines from the different materials used for the anode thin films and substrates. After corrections for escape events average energies were calculated for spectra emerging from applicators. The average energies were 28.2 ±0.3 keV, 29.0±0.7 keV, and 31.7±0.9 keV for the 3.5, 4.0 and 5.0 cm diameter applicators, respectively. Differences in average energies ranged from 2.0 to 5.6% for these diameters. The mean energies of the spectra are more dependent on balloon size than on the delivery system used.

Conclusion:

Energy spectra at the surfaces of 3.5, 4.0 and 5.0 cm diameter applicators were measured for the Axxent and INTRABEAM x‐ray systems were using a Cd‐Te spectrometer. The average energies of the two x‐ray systems for comparable applicator sizes were within 5.6%, and as little as 0.6 keV difference for the smaller applicator size.

Research sponsored by Xoft, a subsidiary of ICAD
SU‐E‐T‐305: Study of the Eclipse Electron Monte Carlo Algorithm for Patient Specific MU Calculations
Authors: Wang X , Qi S , Agazaryan N , DeMarco J
Published: July 2014

Purpose:

To evaluate the Eclipse electron Monte Carlo (eMC) algorithm based on patient specific monitor unit (MU) calculations, and to propose a new factor which quantitatively predicts the discrepancy of MUs between the eMC algorithm and hand calculations.

Methods:

Electron treatments were planned for 61 patients on Eclipse (Version 10.0) using the eMC algorithm for Varian TrueBeam linear accelerators. For each patient, the same treatment beam angle was kept for a point dose calculation at dmax performed with the reference condition, which used an open beam with a 15×15 cm2 size cone and 100 SSD. A patient specific correction factor (PCF) was obtained by getting the ratio between this point dose and the calibration dose, which is 1 cGy per MU delivered at dmax. The hand calculation results were corrected by the PCFs and compared with MUs from the treatment plans.

Results:

The MU from the treatment plans were in average (7.1±6.1)% higher than the hand calculations. The average MU difference between the corrected hand calculations and the eMC treatment plans was (0.07±3.48)%. A correlation coefficient of 0.8 was found between (1‐PCF) and the percentage difference between the treatment plan and hand calculations. Most outliers were treatment plans with small beam opening (< 4 cm) and low energy beams (6 and 9 MeV).

Conclusion:

For CT‐based patient treatment plans, the eMC algorithm tends to generate a larger MU than hand calculations. Caution should be taken for eMC patient plans with small field sizes and low energy beams. We hypothesize that the PCF ratio reflects the influence of patient surface curvature and tissue inhomogeneity to patient specific percent depth dose (PDD) curve and MU calculations in eMC algorithm.
SU‐E‐T‐323: The FLUKA Monte Carlo Code in Ion Beam Therapy
Authors: Rinaldi I ,   on behalf of the FLUKA collaboration
Published: July 2014

Purpose:

Monte Carlo (MC) codes are increasingly used in the ion beam therapy community due to their detailed description of radiation transport and interaction with matter. The suitability of a MC code demands accurate and reliable physical models for the transport and the interaction of all components of the mixed radiation field. This contribution will address an overview of the recent developments in the FLUKA code oriented to its application in ion beam therapy.

Methods:

FLUKA is a general purpose MC code which allows the calculations of particle transport and interactions with matter, covering an extended range of applications. The user can manage the code through a graphic interface (FLAIR) developed using the Python programming language.

Results:

This contribution will present recent refinements in the description of the ionization processes and comparisons between FLUKA results and experimental data of ion beam therapy facilities. Moreover, several validations of the largely improved FLUKA nuclear models for imaging application to treatment monitoring will be shown. The complex calculation of prompt gamma ray emission compares favorably with experimental data and can be considered adequate for the intended applications. New features in the modeling of proton induced nuclear interactions also provide reliable cross section predictions for the production of radionuclides. Of great interest for the community are the developments introduced in FLAIR. The most recent efforts concern the capability of importing computed‐tomography images in order to build automatically patient geometries and the implementation of different types of existing positron‐emission‐tomography scanner devices for imaging applications.

Conclusion:

The FLUA code has been already chosen as reference MC code in many ion beam therapy centers, and is being continuously improved in order to match the needs of ion beam therapy applications.

Parts of this work have been supported by the European FP7 project ENVISION (grant agreement no. 241851).
SU‐F‐BRF‐12: Investigating Dosimetric Effects of Inter‐Fraction Deformation in Lung Cancer Stereotactic Body Radiotherapy (SBRT)
Authors: Jia J , Tian Z , Gu X , Yan H , Jia X , Jiang S
Published: July 2014

Purpose:

We studied dosimetric effects of inter‐fraction deformation in lung stereotactic body radiotherapy (SBRT), in order to investigate the necessity of adaptive re‐planning for lung SBRT treatments.

Methods:

Six lung cancer patients with different treatment fractions were retrospectively investigated. All the patients were immobilized and localized with a stereotactic body frame and were treated under cone‐beam CT (CBCT) image guidance at each fraction. We calculated the actual delivered dose of the treatment plan using the up‐to‐date patient geometry of each fraction, and compared the dose with the intended plan dose to investigate the dosimetric effects of the inter‐fraction deformation. Deformable registration was carried out between the treatment planning CT and the CBCT of each fraction to obtain deformed planning CT for more accurate dose calculations of the delivered dose. The extent of the inter‐fraction deformation was also evaluated by calculating the dice similarity coefficient between the delineated structures on the planning CT and those on the deformed planning CT.

Results:

The average dice coefficients for PTV, spinal cord, esophagus were 0.87, 0.83 and 0.69, respectively. The volume of PTV covered by prescription dose was decreased by 23.78% on average for all fractions and all patients. For spinal cord and esophagus, the volumes covered by the constraint dose were increased by 4.57% and 3.83%. The maximum dose was also increased by 4.11% for spinal cord and 4.29% for esophagus.

Conclusion:

Due to inter‐fraction deformation, large deterioration was found in both PTV coverage and OAR sparing, which demonstrated the needs for adaptive re‐planning of lung SBRT cases to improve target coverage while reducing radiation dose to nearby normal tissues.
SU‐F‐18C‐01: Minimum Detectability Analysis for Comprehensive Sized Based Optimization of Image Quality and Radiation Dose Across CT Protocols
Authors: Smitherman C , Chen B , Samei E
Published: July 2014

Purpose:

This work involved a comprehensive modeling of task‐based performance of CT across a wide range of protocols. The approach was used for optimization and consistency of dose and image quality within a large multi‐vendor clinical facility.

Methods:

150 adult protocols from the Duke University Medical Center were grouped into sub‐protocols with similar acquisition characteristics. A size based image quality phantom (Duke Mercury Phantom) was imaged using these sub‐protocols for a range of clinically relevant doses on two CT manufacturer platforms (Siemens, GE). The images were analyzed to extract task‐based image quality metrics such as the Task Transfer Function (TTF), Noise Power Spectrum, and Az based on designer nodule task functions. The data were analyzed in terms of the detectability of a lesion size/contrast as a function of dose, patient size, and protocol. A graphical user interface (GUI) was developed to predict image quality and dose to achieve a minimum level of detectability.

Results:

Image quality trends with variations in dose, patient size, and lesion contrast/size were evaluated and calculated data behaved as predicted. The GUI proved effective to predict the Az values representing radiologist confidence for a targeted lesion, patient size, and dose. As an example, an abdomen pelvis exam for the GE scanner, with a task size/contrast of 5‐mm/50‐HU, and an Az of 0.9 requires a dose of 4.0, 8.9, and 16.9 mGy for patient diameters of 25, 30, and 35 cm, respectively. For a constant patient diameter of 30 cm, the minimum detected lesion size at those dose levels would be 8.4, 5, and 3.9 mm, respectively.

Conclusion:

The designed CT protocol optimization platform can be used to evaluate minimum detectability across dose levels and patient diameters. The method can be used to improve individual protocols as well as to improve protocol consistency across CT scanners.
SU‐F‐18C‐06: Prospective Patient Evaluation of Iterative Reconstruction in Radiation Oncology
Authors: Price R , Vance S , Cattaneo R , Schultz L , Elshaikh M , Chetty I , Glide‐Hurst C
Published: July 2014

Purpose:

This work incorporates iterative reconstruction (IR) into a dose reduction study to characterize image quality metrics, delineation, and dosimetric assessment, with the goal of reducing imaging dose in Radiation Oncology.

Methods:

Three‐dimensional noise power spectrum (NPS) analysis characterized noise magnitude/texture (120 kVp, 50–200 mAs, IR levels 1–6 yielding noise reduction of 0.89–0.55 compared to filtered backprojection (FBP)). Task‐specific Modulation Transfer Functions (MTFtask) were characterized across varied subject contrasts. A prospective dose reduction study (500 to 150 mAs) was conducted for 12 patients (43 inter‐fraction CTs) for high‐dose rate brachytherapy. Three physicians performed qualitative image assessment between full‐dose FBP (FD‐FBP, 500 mAs), low‐dose FBP (LD‐FBP, 150–250 mAs), and low‐dose IRL5‐6 (LD‐IR) scans for image noise, cuff/bladder interface detectability, spatial resolution, texture, and segmentation confidence. Comparisons between LD‐FBP and LD‐IR were conducted for the following metrics: delineation (bladder and rectum evaluated via overlap indices (OI) and Dice similarity coefficients (DSC)), noise, boundary changes, dose calculation, and digitally reconstructed radiographs (DRRs).

Results:

NPS showed ∼50% reduction in noise magnitude and ∼0.1 1/mm spatial frequency shift with IRL6. The largest MTFtask decrease between FBP and IR was 0.08 A.U. Qualitative patient image evaluation revealed LD‐IR was equivalent or slightly worse than FD‐FBP, and superior to LD‐FBP for all metrics except low contrast interface and texture. The largest CT number discrepancy from FBP occurred at a bone/tissue interface using IRL6 (−1.2 ± 4.9 HU (range: −17.6 – 12.5 HU)). No significant contour differences (OIs and DSCs = 0.85 – 0.95) and dose calculation discrepancy (<0.02%) were observed. DRRs preserved anatomical detail and demonstrated <2% difference in intensity between LD‐FBP and LD‐IRL6.

Conclusion:

While phantom analysis showed slight noise texture differences with IR, patient results revealed that image quality, contouring ability, and dosimetric parameters were not adversely affected, thus support integrating IR into treatment planning.

Research supported in part by a grant from Philips HealthCare.
SU‐F‐18C‐07: Automated CT QC Program with Analytics, Archival, and Notification Capabilities
Authors: Winslow J , Christianson O , Samei E
Published: July 2014

Purpose:

Tracking metrics over time is a well‐established means of establishing a quality control program. The number of metrics followed and testing frequency is limited by available resources. Automating the image analysis and data archival of a QC program enables objective and efficient tracking of performance metrics. The purpose of this study was to develop such a QC method and to assess its utility at a large clinical facility.

Methods:

The QC program at our institution is based on the acquisition of daily water‐phantom scans, and biweekly ACR‐phantom scans for each CT system. We developed a QC program to analyze these data. The QC software operates on the images sent directly to our server. The relevant information from DICOM headers was extracted, data analyzed, and a database was populated. The measurements performed on the waterphantom included water CT‐number, uniformity, noise, and artifact. The measurements performed on the ACR‐phantom included the MTF, NPS, detectability, artifact, uniformity, CNR, and the CT‐numbers for water, polyethylene, bone, air, and acrylic. Email notifications and criteria limits were directly based upon ACR accreditation requirements and developing threshold values.

Results:

Across ten clinical CT scanners, the daily water CT‐number was −0.2+/−1.4 HU(mean+/‐standard deviation). The corresponding numbers for 10%MTF, uniformity, CNR squared normalized to CTDI, and detectability squared normalized to CTDI were 0.69+/−0.01 (1/mm), 0.93+/−0.29, 0.06+/−0.02 (1/mGy), and 3.3+/−0.7 (1/mGy), respectively. For all ACR‐phantom inserts, the largest standard deviation for any individual scanner was 1.9 HU. Artifact analysis triggers successfully identified problematic images.

Conclusion:

Automating image analysis allows one to frequently track meaningful metrics that would be impractical to follow otherwise. System inconsistencies are more likely to be identified and corrected earlier. Much tighter system specific criteria limits are possible.
SU‐F‐18C‐11: Diameter Dependency of the Radial Dose Distribution in a Long Polyethylene Cylinder
Authors: Bakalyar D , Feng W , McKenney S
Published: July 2014

Purpose:

The radial dose distribution in the central plane of a long cylinder following a long CT scan depends upon the diameter and composition of the cylinder. An understanding of this behavior is required for determining the spatial average of the dose in the central plane. Polyethylene, the material for construction of the TG200/ICRU phantom (30 cm in diameter) was used for this study. Size effects are germane to the principles incorporated in size specific dose estimates (SSDE); thus diameter dependency was explored as well.

Method:

ssuming a uniform cylinder and cylindrically symmetric conditions of irradiation, the dose distribution can be described using a radial function. This function must be an even function of the radial distance due to the conditions of symmetry. Two effects are accounted for: The direct beam makes its weakest contribution at the center while the contribution due to scatter is strongest at the center and drops off abruptly at the outer radius. An analytic function incorporating these features was fit to Monte Carlo results determined for infinite polyethylene cylinders of various diameters. A further feature of this function is that it is integrable.

Results:

Symmetry and continuity dictate a local extremum at the center which is a minimum for the larger sizes. The competing effects described above can Resultin an absolute maximum occurring between the center and outer edge of the cylinders. For the smallest cylinders, the maximum dose may occur at the center.

Conclusion:

An integrable, analytic function can be used to characterize the radial dependency of dose for cylindrical CT phantoms of various sizes. One use for this is to help determine average dose distribution over the central cylinder plane when equilibrium dose has been reached.
MO‐A‐18C‐01: Proton Therapy I: Basics of Proton Therapy
Authors: Arjomandy B , Sahoo N , Pankuch M
Published: July 2014

The goal of this session is to introduce the audience to the physics, dosimetry and treatment planning procedures used in proton therapy. The course material covers the basic physics of proton interaction with matter and physical characteristics of clinical proton beams. It will provide information on proton delivery systems and beam delivery techniques for scattered and scanning proton beams. It will include the requirements for dosimetry measurements and present the equipment needed for commissioning of proton beams for clinical use and quality assurance checks as well as methods used for proton beam calibration and dose verification of patient treatment fields. The session covers the treatment planning strategies for various anatomical sites, methods to address uncertainties in proton therapy and uncertainty mitigation to generate robust treatment plans. Challenges involved in the motion management in proton therapy will also be discussed.

Learning Objectives:

Gain knowledge on physics, dosimetry, treatment planning and quality assurance for proton therapy.

Understand the uncertainties associated with proton therapy and currently used strategies for their mitigation in treatment planning.
MO‐A‐16A‐01: QA Procedures and Metrics: In Search of QA Usability
Authors: Sathiaseelan V , Thomadsen B
Published: July 2014

Radiation therapy has undergone considerable changes in the past two decades with a surge of new technology and treatment delivery methods. The complexity of radiation therapy treatments has increased and there has been increased awareness and publicity about the associated risks. In response, there has been proliferation of guidelines for medical physicists to adopt to ensure that treatments are delivered safely. Task Group recommendations are copious, and clinical physicists' hours are longer, stretched to various degrees between site planning and management, IT support, physics QA, and treatment planning responsibilities.Radiation oncology has many quality control practices in place to ensure the delivery of high‐quality, safe treatments. Incident reporting systems have been developed to collect statistics about near miss events at many radiation oncology centers. However, tools are lacking to assess the impact of these various control measures. A recent effort to address this shortcoming is the work of Ford et al (2012) who recently published a methodology enumerating quality control quantification for measuring the effectiveness of safety barriers. Over 4000 near‐miss incidents reported from 2 academic radiation oncology clinics were analyzed using quality control quantification, and a profile of the most effective quality control measures (metrics) was identified. There is a critical need to identify a QA metric to help the busy clinical physicists to focus their limited time and resources most effectively in order to minimize or eliminate errors in the radiation treatment delivery processes. In this symposium the usefulness of workflows and QA metrics to assure safe and high quality patient care will be explored. Two presentations will be given:Quality Metrics and Risk Management with High Risk Radiation Oncology ProceduresStrategies and metrics for quality management in the TG‐100 Era

Learning Objectives:

Provide an overview and the need for QA usability metrics: Different cultures/practices affecting the effectiveness of methods & metrics.

Show examples of quality assurance workflows, Statistical process control, that monitor the treatment planning and delivery process to identify errors.

To learn to identify and prioritize risks and QA procedures in radiation oncology.

Try to answer the question: Can a quality assurance program aided by quality assurance metrics help minimize errors and ensure safe treatment delivery. Should such metrics be institution specific.
MO‐A‐BRD‐02: Noise Suppression for Dual‐Energy CT Through Entropy Minimization
Authors: Petrongolo M , Niu T , Zhu L
Published: July 2014

Purpose:

In dual energy CT (DECT), noise amplification during signal decomposition significantly limits the utility of basis material images. Since clinically relevant objects contain a limited number of materials, we propose to suppress noise in decomposed images through entropy minimization within a 2D transformation space. Distinct from other noise suppression techniques, the entropy minimization method does not estimate and suppress noise based on spatial variations of signals and thus maximally preserves image spatial resolution.

Methods:

From decomposed images, we first generate a 2D plot of scattered data points, using basis material densities as coordinates. Data points representing the same material generate a cluster with a highly asymmetric shape. We orient an axis by minimizing the entropy in a 1D histogram of these points projected onto the axis. To suppress noise, we replace the pixel values of decomposed images with center‐of‐mass values in the direction perpendicular to the optimized axis. The proposed method's performance is assessed using a Catphan 600 phantom and an anthropomorphic head phantom. Electron density calculations are used to quantify its accuracy. Our results are compared to those without noise suppression, with a filtering method, and with a recently developed iterative method.

Results:

On both phantoms, the proposed method reduces noise standard deviations of the decomposed images by at least on order of magnitude. In the Catphan study, this method retains the spatial resolution of the CT images and increases the accuracy of electron density calculations. In the head phantom study, the proposed method outperforms the others in retaining fine, intricate structures.

Conclusion:

This work shows that the proposed method of noise suppression through entropy minimization for DECT suppresses noise without loss of spatial resolution while increasing electron density calculation accuracy. Future investigations will analyze possible bias and incorporate spatial information into the technique for further noise reduction.

The work is partially supported by the NIH under the grant number R21EB012700.
MO‐A‐BRD‐05: Evaluation of Composed Lung Ventilation with 4DCT and Image Registration
Authors: Du K , Reinhardt J , Christensen G , Ding K , Zhao B , Bayouth J
Published: July 2014

Purpose:

Regional pulmonary function can be derived using fourdimensional computed tomography (4DCT) combined with deformable image registration. However, only peak inhale and exhale phases have been used thus far while the lung ventilation during intermediate phases is not considered. In our previous work, we have investigated the spatiotemporal heterogeneity of lung ventilation and its dependence on respiration effort. In this study, composed ventilation is introduced using all inspiration phases and compared to direct ventilation. Both methods are evaluated against Xe‐CT derived ventilation.

Methods:

Using an in‐house tissue volume preserving deformable image registration, unlike the direct ventilation method, which computes from end expiration to end inspiration, Jacobian ventilation maps were computed from one inhale phase to the next and then composed from all inspiration steps. The two methods were compared in both patients prior to RT and mechanically ventilated sheep subjects. In addition, they wereassessed for the correlation with Xe‐CT derived ventilation in sheep subjects. Annotated lung landmarks were used to evaluate the accuracy of original and composed deformation field.

Results:

After registration, the landmark distance for composed deformation field was always higher than that for direct deformation field (0IN to 100IN average in human: 1.03 vs 1.53, p=0.001, and in sheep: 0.80 vs0.94, p=0.009), and both increased with longer phase interval. Direct and composed ventilation maps were similar in both sheep (gamma pass rate 87.6) and human subjects (gamma pass rate 71.9),and showed consistent pattern from ventral to dorsal when compared to Xe‐CT derived ventilation. Correlation coefficient between Xe‐CT and composed ventilation was slightly better than the direct method but not significant (average 0.89 vs 0.85, p=0.135).

Conclusion:

More strict breathing control in sheep subjects may explain higher similarity between direct and composed ventilation. When compared to Xe‐CT ventilation, no significant difference was found for the composed method.

NIH Grant: R01 CA166703
MO‐A‐9A‐01: Innovation in Medical Physics Practice: 3D Printing Applications
Authors: Ehler E , Perks J , Rasmussen K , Bakic P
Published: July 2014

3D printing, also called additive manufacturing, has great potential to advance the field of medicine. Many medical uses have been exhibited from facial reconstruction to the repair of pulmonary obstructions. The strength of 3D printing is to quickly convert a 3D computer model into a physical object. Medical use of 3D models is already ubiquitous with technologies such as computed tomography and magnetic resonance imaging. Thus tailoring 3D printing technology to medical functions has the potential to impact patient care. This session will discuss applications to the field of Medical Physics. Topics discussed will include introduction to 3D printing methods as well as examples of real‐world uses of 3D printing spanning clinical and research practice in diagnostic imaging and radiation therapy. The session will also compare 3D printing to other manufacturing processes and discuss a variety of uses of 3D printing technology outside the field of Medical Physics.

Learning Objectives:

Understand the technologies available for 3D Printing

Understand methods to generate 3D models

Identify the benefits and drawbacks to rapid prototyping / 3D Printing

Understand the potential issues related to clinical use of 3D Printing
MO‐A‐12A‐01: Joint Imaging Education – Quantitative Imaging Symposium: Genomics and Image‐Omics for Medical Physicists
Authors: Giger M , Cowperthwaite M , Markey M
Published: July 2014

With the growth of quantitative imaging and quantitative image analysis, image‐based biomarkers (i.e. image‐based phenotypes) are becoming potential descriptors in personalized medicine, as well as in cancer discovery and neuroscience research. Thus, there is a need to understand (i) the quantitation and standardization involved with image‐based phenotypes and (ii) the aspects of datamining and association studies involved in “conventional” phenotype‐genotype studies. Imaging scientists and genomic scientists need to be able to speak the same language in this common era of Big Data.

Learning Objectives:

Understand what are image‐based phenotypes and their potential medical significance.

Appreciate the complexity of Big Data.

Learn about genetic association studies.
MO‐B‐18C‐01: Proton Therapy II: Proton Stereotactic Radiotherapy
Authors: Winey B , Daartz J
Published: July 2014

Proton stereotactic radiotherapy shares fundamental principles with general proton therapy physics, specifically range uncertainties and broad beam measurement techniques. Significant differences emerge when treating with smaller field sizes that suffer lateral disequilibrium and when fractions are reduced. This session will explore the history and scope of proton stereotactic radiotherapy in clinical practice. Uncertainties and treatment planning methods specific to stereotactic treatments will be discussed. The session will include an overview of the physical properties of small proton fields and resulting needs for accurate measurements and modeling of dose distributions for radiosurgery treatment planning.

Learning Objectives:

Understand the clinical rationale for proton radiosurgery.

Understand the similarities and differences from general proton therapy.

Understand the similarities and differences from photon stereotactic radiosurgery.

Understand the basic physics and clinical physics methods for measuring and commissioning a radiosurgery program.
MO‐B‐16A‐01: Memorial to Donald D. Tolbert – Memorial Lecture
Authors: Morin R
Published: July 2014

The Medical Physics community lost one of its prominent leaders in April, 2013 with the passing of Donald D. Tolbert, PhD. He received his Doctorate at the University of Kansas followed by post Doctoral training at Florida State University and the University of Wisconsin. He was Chief of Radiation Therapy Medical Physics at the University of Wisconsin Hospital for 7 years before relocating to Honolulu Hawaii, where he founded the consulting group Mid‐Pacific Medical Physics.

Don was a leader in both the AAPM and the ACR, chairing the Professional Council and the Commission on Medical Physics. He was active on the AAPM Board of Directors and a member of the ACR Board of Chancellors. Dr. Tolbert's approach to the difficult problems of the times was admired and respected by colleagues in Medical Physics, Radiation Oncology, and Diagnostic Radiology. He always rose above the heated political rhetoric and led the discussion to higher ground. His wisdom was continually sought to solve complicated problems.

Following retirement, he returned to homes in Kansas and Colorado, devoting his time to writing about coping with diabetes and providing support for Seniors in Beloit Kansas.

Don is survived by his wife, Mattie, his 3 children and 5 grandchildren. He will be greatly missed.
MO‐C‐BRE‐01: The WMIS‐AAPM Joint Symposium: Advances in Molecular Imaging
Authors: Contag C , Pogue B , Lewis J
Published: July 2014

This joint symposium of the World Molecular Imaging Society (WMIS) and the AAPM includes three luminary speakers discussing work in new paradigms of molecular imaging in cancer (Contag), applications of optical imaging technologies to radiation therapy (Pogue) and an update on PET imaging as a surrogate biomarker for cancer progression and response to therapy.

Learning Objectives:

Appreciate the current trends in molecular and systems imaging.

Understand how optical imaging technologies, and particularly Cerenkov detectors, can be used in advancing radiation oncology.

Stay current on new PET tracers – and targets – of interest in cancer treatment.
MO‐C‐17A‐04: Forecasting Longitudinal Changes in Oropharyngeal Tumor Morphology Throughout the Course of Head and Neck Radiation Therapy
Authors: Yock A , Rao A , Dong L , Beadle B , Garden A , Kudchadker R , Court L
Published: July 2014

Purpose:

To generate, evaluate, and compare models that predict longitudinal changes in tumor morphology throughout the course of radiation therapy.

Methods:

Two morphology feature vectors were used to describe the size, shape, and position of 35 oropharyngeal GTVs at each treatment fraction during intensity‐modulated radiation therapy. The feature vectors comprised the coordinates of the GTV centroids and one of two shape descriptors. One shape descriptor was based on radial distances between the GTV centroid and 614 GTV surface landmarks. The other was based on a spherical harmonic decomposition of these distances. Feature vectors over the course of therapy were described using static, linear, and mean models. The error of these models in forecasting GTV morphology was evaluated with leave‐one‐out cross‐validation, and their accuracy was compared using Wilcoxon signed‐rank tests. The effect of adjusting model parameters at 1, 2, 3, or 5 time points (adjustment points) was also evaluated.

Results:

The addition of a single adjustment point to the static model decreased the median error in forecasting the position of GTV surface landmarks by 1.2 mm (p<0.001). Additional adjustment points further decreased forecast error by about 0.4 mm each. The linear model decreased forecast error compared to the static model for feature vectors based on both shape descriptors (0.2 mm), while the mean model did so only for those based on the inter‐landmark distances (0.2 mm). The decrease in forecast error due to adding adjustment points was greater than that due to model selection. Both effects diminished with subsequent adjustment points.

Conclusion:

Models of tumor morphology that include information from prior patients and/or prior treatment fractions are able to predict the tumor surface at each treatment fraction during radiation therapy. The predicted tumor morphology can be compared with patient anatomy or dose distributions, opening the possibility of anticipatory re‐planning.

American Legion Auxiliary Fellowship; The University of Texas Graduate School of Biomedical Sciences at Houston
MO‐C‐17A‐06: Online Adaptive Re‐Planning to Account for Independent Motions Between Multiple Targets During Radiotherapy of Lung Cancer
Authors: Liu F , Tai A , Ahunbay E , Gore E , Johnstone C , Li X
Published: July 2014

Purpose:

To quantify interfractional independent motions between multiple targets in radiotherapy (RT) of lung cancer, and to study the dosimetric benefits of an online adaptive replanning method to account for these variations.

Methods:

Ninety five diagnostic‐quality daily CTs acquired for 9 lung cancer patients treated with IGRT using an in‐room CT (CTVision, Siemens) were analyzed. On each daily CT set, contours of the targets (GTV, CTV, or involved nodes) and organs at risk were generated by populating the planning contours using an auto‐segmentation tool (ABAS, Elekta) with manual editing. For each patient, an IMRT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2Gy fractions. Three plans were generated and compared for each daily CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes and segment weights of the original plan to conform to the daily anatomy, and a new fully re‐optimized plan based on the daily CT using a planning system (Panther, Prowess).

Results:

The daily deviations of the distance between centers of masses of the targets from the plans varied daily from ‐10 to 8 mm with an average −0.9±4.1 mm (one standard deviation). The average CTV V100 are 99.0±0.7%, 97.9±2.8%, 99.0±0.6%, and 99.1±0.6%, and the lung V20 Gy 928±332 cc, 944±315 cc, 917±300 cc, and 891±295 cc for the original, repositioning, adaptive, and re‐optimized plans, respectively. Wilcoxon signed‐rank tests show that the adaptive plans are statistically significantly better than the repositioning plans and comparable with the reoptimized plans.

Conclusion:

There exist unpredictable, interfractional, relative volume changes and independent motions between multiple targets during lung cancer RT which cannot be accounted for by the current IGRT repositioning but can be corrected by the online adaptive replanning method.
MO‐C‐17A‐07: Building Atlas for Automatic Prostate IMRT Planning: Anatomical Feature Parameterization and Classification
Authors: Sheng Y , Li T , Zhang Y , Yin F , Ge Y , Wu Q
Published: July 2014

Purpose:

To establish and evaluate a prostate IMRT plan atlas to provide accurate dose guidance for dose‐guided automatic planning (DAP) technique.

Methods:

25 prostate cancer patients treated with IMRT were studied. The anatomy of prostate cancer cases was parameterized and quantified into two measures: the percent distance‐to‐prostate (PDP) and the concaveness angle. Patients were represented by points at a 2D space spanned by the two anatomical measures. The k‐medoids algorithm was used to elect k cases from 25 points to compose the atlas. To determine the optimal size k, the average silhouette width for each k‐medoids scheme was calculated and the k corresponding to higher average silhouette width was chosen as the atlas size. The other cases were used as query cases to evaluate the atlas efficacy. The DAP technique, which links atlas and query case using deformable image registration, was used to generate the automatic IMRT plan. Dosimetric comparison between DAP plans and clinical plans were performed.

Results:

Atlases of sizes ranging from 2 to 9 are established. The average silhouette width for each atlas of the size from 2 to 9 is 0.651, 0.558, 0.455, 0.563, 0.620, 0.580, 0.636, and 0.695, respectively. The size of 6 is preliminary chosen as the optimal size for robustness test. Dosimetric similarities are observed between two plans (p>0.05): homogeneity indices are 6.7±1.2% and 7.3±1.6%; rectum‐gEUDs are 40.6±1.8 Gy and 40.6±2.1 Gy, for DAP plans and clinical plans, respectively. Improved conformity index and bladder‐gEUD are observed in the DAP plan (p=0.036, 0.009 respectively).

Conclusion:

An atlas with 6 expert prostate IMRT plans was identified as the optimal size. DAP plans generated with the dose guidance from the atlas show comparable plan quality with clinical plans. The atlas can cover various anatomical configurations and provide satisfying dose guidance when implemented with the DAP technique.
MO‐C‐17A‐09: Deformation Image Registration Using a Spatial‐Context Regularization Filter
Authors: Zhang Y , Yang J , Zhang L , Balter P , Dong L , Court L
Published: July 2014

Purpose:

Intensity‐based deformable registration with spatial‐invariant regularization will generally fail when distinct motion exists across the interface of different type of tissues. The purpose of this work was to develop a new regularization scheme for deformable registration that is adaptive to motion discontinuities along different types of tissue.

Methods:

Our approach was built upon a multi‐resolution Demons‐type framework, and used the prior knowledge defined in the image context as an additional constraint to regularize deformation vector fields, which leads to a spatialcontext vector smoothing filter. This filter is spatial‐variant and anisotropic, and can be reduced to the product of a Gaussian function of spatial locations and an additional Gaussian function of image intensity difference. This additional regularization favors the motion vectors within the same tissue type, but penalized the motions vectors from different tissues. Five lung patient cases from a benchmark data set, each with 300 landmark pairs delineated by a physician, were adopted for the validation of our algorithm. Our approach was also compared against a state‐of‐the‐art implementation of dual‐force Demons algorithm. We also included one difficult lung case with large sliding motion and one head‐neck cases with large motion in air cavity into our validatio

Results:

For five lung cases, the mean and standard deviation of the landmark displacements were 1.3±0.8(mm) using our approach, but this number went up to 2.3±2.9(mm) using Demons algorithm. Particularly for a case with largest initial displacement of 15± 9(mm), our approach reduced the mean displacement to 1.3±1.1(mm), but Demons can only achieve 3.6±5.9(mm). We also found that, for the two difficult cases in which the original intensity‐based registration failed the registration accuracy was drastically improved using the proposed algorithm.

Conclusion:

We have demonstrated the effectiveness of an algorithm using adaptive regularization in addressing large distinct motion across different types of tissue.

This work is partially supported by the CPRIT (Cancer Prevention Research Institute of Texas) grant RP110732
MO‐C‐17A‐10: Comparison of Dose Deformable Accumulation by Using Parallel and Serial Approaches
Authors: Gao Z , Li M , Wong J
Published: July 2014

Purpose:

The uncertainty of dose accumulation over multiple CT datasets with deformable fusion may have significant impact on clinical decisions. In this study, we investigate the difference of two dose summation approaches involving deformable fusion.

Methods:

Five patients, four external beam and one brachytherapy(BT), were chosen for the study. The BT patient was treated with CT‐based HDR. The CT image sets acquired in the imageguidance process (8‐11 CTs/patient) were used to determine the dose delivered to the four external beam patients. (prostate, pelvis, lung and head&neck). For the HDR patient (cervix), five CT image sets and the corresponding BT plans were used. In total 44 CT datasets and RT dose/plans were imported into the image fusion software MiM (6.0.4) for analysis.For each of the five clinical cases, the dose from each fraction was accumulated into the primary CT dataset by using both Parallel and Serial approaches. The dose‐volume histogram (DVH) for CTV and selected organs‐at‐risks (OAR) were generated. The D95(CTV), OAR(mean) and OAR(max) for the four external beam cases the D90(CTV), and the max dose to bladder and rectum for the BT case were compared.

Results:

For the four external beam patients, the difference in D95(CTV) were <1.2% PD between the parallel and the serial approaches. The differences of the OAR(mean) and the OAR(max) range from 0 to 3.7% and <1% PD respectively. For the HDR patient, the dose difference for D90 is 11% PD while that of the max dose to bladder and rectum were 11.5% and 23.3% respectively.

Conclusion:

For external beam treatments, the parallel and serial approaches have <5% difference probably because tumor volume and OAR have less changes from fraction to fraction. For the brachytherapy case, >10% dose difference between the two approaches was observed as significant volume changes of tumor and OAR were observed among treatment fractions.
MO‐C‐12A‐01: Quantitative Imaging Initiatives: Why, Who, What, and How?
Authors: Sullivan D , Jackson E , Clarke L , Petrick N , Russek S
Published: July 2014

Over the past decade, there has been an increasing focus on quantitative imaging (QI), which, according to one definition, is “the extraction of quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal” (www.rsna.org/QIBA). To achieve the goals of QI requires the development and standardization of data acquisition, data analysis, and data display techniques, as well as appropriate reporting structures. As such, successful implementation of QI relies heavily on expertise from the fields of medical physics, radiology, statistics, and informatics as well as collaboration from vendors of imaging acquisition, analysis, and reporting systems. When successfully implemented, QI techniques will provide image‐derived metrics with known bias and variance that can be validated with anatomically and physiologically relevant measures, including treatment response, and the heterogeneity of that response, and outcome. Such non‐invasive measures can then be used effectively in clinical and translational research as well as patient care. In addition to modality‐specific QI efforts implemented by individual scientific organizations, national and international organizations, including the NCI, RSNA, FDA, and NIST, appreciating the tremendous potential of QI but also understanding the associated challenges, have become increasingly involved. This symposium session will focus on 1) introducing QI and illustrating why it is important, even though challenging, in both research and clinical applications, and 2) providing overviews of QI efforts from national and international organizations, including the RSNA, NCI, FDA, and NIST.

Learning Objectives:

Understand the importance and potential of QI in research and clinical applications.

Understand key challenges of QI and current barriers to implementation.

Understand the current QI efforts of several national and international agencies and organizations, including the FDA, NCI, NIST, and RSNA.