bluuebunny/crossref_metadata_2025 huggingface dataset:
WE‐G‐204‐07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection
Authors: Wells J , Zhang L , Samei E
Published: June 2015

Purpose:

To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)]

Methods:

Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c‐means (FCM) clustering, and a clinically‐derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale‐ and neighborhood‐based pixel classification probabilities which are weighted by the clinically‐derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left‐right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity‐based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient‐based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel2 ROIs with and 1920 ROIs without hardware were manually selected.

Results:

Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively.

Conclusion:

Updated segmentation and centerline estimation methods in addition to new gradient‐based hardware detection software provide improved data integrity control and error‐checking for automated clinical chest image quality characterization across multiple radiography systems.
WE‐G‐204‐09: Medical Physics 2.0 in Practice: Automated QC Assessment of Clinical Chest Images
Authors: Willis C , Willis C , Nishino T , Wells J , Wilson J , Samei E
Published: June 2015

Purpose:

To determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in our clinical operation.

Methods:

Seventeen gridless AP Chest radiographs from a GE Optima portable digital radiography (DR) unit (Group 1), seventeen (routine) PA Chest radiographs from a GE Discovery DR unit (Group 2), and sixteen gridless (non‐routine) PA Chest radiographs from the same Discovery DR unit (Group 3) were chosen for analysis. Groups were selected to represent “sub‐standard” (Group 1), “standard‐of‐care” (Group 2), and images with a gross technical error (Group 3). Group 1 images were acquired with lower kVp (90 vs. 125), shorter source‐to‐image distance (127cm vs 183cm) and were expected to have lower quality than images in Group 2. Group 3 was expected to have degraded contrast versus Group 2.This evaluation was approved by the institutional Quality Improvement Assurance Board (QIAB). Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software previously described1 and validated2. Image quality for individual images was reported in terms of lung grey level(Lgl); lung noise(Ln); rib‐lung contrast(RLc); rib sharpness(Rs); mediastinum detail(Md), noise(Mn), and alignment(Ma); subdiaphragm‐lung contrast(SLc); and subdiaphragm area(Sa). Metrics were compared across groups.

Results:

Metrics agreed with published Quality Consistency Ranges with three exceptions: higher Lgl, lower RLc, and SDc. Higher bit depth (16 vs 12) accounted for higher Lgl values in our images. Values were most internally consistent for Group 2. The most sensitive metric for distinguishing between groups was Mn followed closely by Ln. The least sensitive metrics were Md and RLc.

Conclusion:

The software appears promising for objectively and automatically identifying substandard images in our operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.
WE‐G‐210‐01: Current Status of Supplementary Screening with Breast Ultrasound
Authors: Feig S.
Published: June 2015

This session focuses on recent developments in breast ultrasound imaging, covering clinical practice as well as clinical and pre‐clinical research. Current status of ultrasound screening will be reviewed, followed by discussions of emerging technologies at various stages of regulatory and technical development. The session strives to provide the attendee with a view of a continuum of imaging advances starting with the present and spanning the near and long term future.

Learning Objectives:

Understand current state of ultrasound screening for breast cancer

Learn about current availability of whole breast ultrasound

Appreciate the current barriers to stronger adoption of screening ultrasound

Learn about attempts to drive adoption of screening ultrasound (e.g. breast density laws)

Learn about ultrasound tomography as an emerging technique for breast imaging

Learn about the regulatory status of emerging techniques such as ultrasound tomography

Learn about the developmental status of combined 3‐D ultrasound and Mammography systems

Learn about photo‐acoustic imaging and its potential future role in breast imaging
TH‐AB‐304‐02: Fitting Grade>=2(2+) Acute Rectal Complication Rates in Prostate Cancer Patients to Lyman Kutcher Burman (LKB) and Logistic Regression NTCP Models Using Dosimetry and Patient Specific Characteristics
Authors: Liu X , Fatyga M , Li J , Schild M , Schild S , Vora S , Wong W , Wu T
Published: June 2015

Purpose:

Models of rectal toxicity which include dosimetry only are known to have a relatively low predictive power, at least as measured by the area under the ROC curve (AUC). It has been suggested that the predictive power of models can be improved by including non‐dosimetric patient specific characteristics.

Methods:

We compiled a database of 79 prostate patients who were treated with an IMRT technique to a dose of 77.4 Gy, 1.8Gy/fx, with an integrated boost to 81–83Gy in a sub‐volume of a prostate which was identified on a pre‐treatment MRI study. Acute grade 2+ rectal toxicities were graded according to CTCAE v4 by a physician who retrospectively reviewed patient's medical records. We modified the LKB model to include one patient specific variable at a time, and we also used an NTCP model based on logistic regression to perform multi‐variate analysis. We used patient specific variables available to us in a retrospective study: age, diabetes, hormonal treatment, Gleason Score, PSA, Statin use, prostate volume, boost volume and rectal volume.

Results:

Grade 2+ acute rectal toxicity occurred in 20% (16/79 patients). The LKB model with dosimetry alone gives AUC=0.65. Four variables, age, diabetes, PSA, Statin use, increase the AUC in LKB model to a maximum of 0.79. The same four variables in the logistic regression model increase AUC to 0.87. The most significant correlations are with PSA and with Statin use.

Conclusion:

Including patient specific variables in toxicity models can significantly increase apparent predictive power of a model. Somewhat surprising finding of a strong correlation between rectal toxicity and PSA in our dataset suggests that conclusions from each individual study should be treated with caution, until independently confirmed. Larger databases from prospective studies or meta‐analysis of multiple studies may be needed to find patient characteristics that are truly predictive.
TH‐AB‐304‐08: Computational Modeling of Risk of Radiation Pneumonitis
Authors: Logan J.V. , Trifiletti D.M. , Larner J.M. , Siebers J.V. , Watkins W.T.
Published: June 2015

Purpose:

Current normal tissue complication probability models estimate incidence of radiation pneumonitis (RP) in the treatment of lung cancer based on single parameters, e.g. mean lung dose or V20, and therefore do not capture specific variations contained in dose volume histograms (DVHs). We therefore developed an RP‐risk model which utilizes the entire DVH reasoning that such a model would be more robust for predicating radiation‐induced lung disease (RILD).

Methods:

A logistic regression model was developed to predict RP‐risk on each point of the cumulative DVH utilizing >1200 patient outcomes summarized in the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) lung study. Stepwise regression including linear, quadratic, and mixed terms in dose and volume was applied to the single dose‐volume parameter‐versus‐outcome data. The model computes a relative importance at each dose‐volume point and these values were linearly combined to estimate an overall RP‐risk. A set of 80 DVHs were used to validate the algorithm prediction by comparison with an independent predictor of RP‐risk using mean lung dose.

Results:

The logistic regression coefficients were all statistically significant (p<0.005). The proposed model predicted RP‐risk consistently in agreement with the QUANTEC‐RP estimates computed utilizing only mean dose (average difference 0.5%±1.8%) for the 80 DVH set. Most (>70%) of the model variability occurs at doses <30 Gy for DVH levels ranging from 30%‐100%. The model is most sensitive to DVH variations in the V5‐V30 range.

Conclusion:

The model's predictive power is comparable to estimates from QUANTEC which use mean dose as input. Since the model conveys the relative importance of each dose‐volume level it has the potential to better predict RILD than currently available models. In order to determine if our hypothesis is correct we will compare the model to established methods including complete DVH data and clinical outcomes.

This abstract was supported by the George Amorino Pilot Grants in Radiation Oncology from the Department of Radiation Oncology, Univeristy of Virginia.
TH‐AB‐304‐09: A Patient‐Specific Heterogeneous Radiobiological Model for Glioblastoma Multiforme
Authors: McGuinness C , Barani I , Noble R , Ng J
Published: June 2015

Purpose:

Gliomas tumors proliferate and invade healthy brain tissue rapidly, yielding short life expectancies. Radiation therapy is commonly used, but gliomas are known to be radio‐resistant, and almost always recur following treatment. Current radiotherapy protocols are based on the classic linear‐quadratic radiobiological model, assuming a homogeneous (one‐component) tumor. Gliomas are very heterogeneous, consisting of normoxic, hypoxic, and necrotic tissues, each responding differently to radiation. An enhanced linear‐quadratic model which takes into account the different responses of the heterogeneous tumor regions to radiation can guide treatment planning to optimize dose distributions to maximize the therapeutic effect.

Methods:

We used a set of differential equations to model the growth of glioma tumors. Our model expands on the one‐component model developed by (Rockne,2010) by including normoxic, hypoxic, and necrotic components and radiosensitivity values for each component. Proliferation and diffusion parameters are extracted by contouring the tumor on two sets of pre‐treatment MRIs and modeling it as a volume equivalent sphere.

Results:

We compared two examples of glioma tumors presented in (Rockne,2010) with our three component model and find better predictive capabilities for post‐RT tumor volumes using the three component model. In both cases, the one‐component model over predicts the effects of radiation on the tumor core, while the three‐component model more accurately fits the data by accounting for radioresistance of the hypoxic core. Additionally, we applied the model to a UCSF patient with a recurring tumor and use the model to predict time to fatal tumor burden.

Conclusion:

The three‐component model accurately matches tumor growth dynamics derived from MRIs for two example cases presented in literature, and one case from UCSF. Spatial information about hypoxic, necrotic, and normoxic cell densities are derived from the model providing information needed to more intelligently prescribe dose distributions tailored to a specific patient's tumor.[Rockne,et.al.,PhysMedBio,2010]
TH‐AB‐304‐12: Validation of a Morphological Xerostomia Prediction Model
Authors: Gabrys H , Buettner F , Schubert K , Mescher H , Debus J , Sterzing F , Bangert M
Published: June 2015

Purpose:

We present a validation study of a morphological NTCP model predicting dry mouth syndrome (xerostomia) based on the spatial distribution of the dose received by salivary the glands in head‐and‐neck cancer patients during radiation therapy.

Methods:

We examined a recently proposed model based on Bayesian multivariate logistic regression. It uses scale‐invariant moments of the three‐dimensional dose distribution to explicitly include information about the distribution of the dose within the parotid glands in different spatial directions. We tested the classification performance of this model on our independent patient cohort and compared it to routinely used clinical mean dose models. The predictive power of the models was evaluated using receiver operating characteristics (ROC).

Results:

The study comprised 105 head‐and‐neck cancer patients from varying tumor sites. Of these patients, 13 reported severe xerostomia between five and 48 months after radiation therapy. The AUC of the morphological model, the mean‐dose model based on the contralateral parotid gland, and the mean‐dose model based on both parotid glands were 0.79, 0.71, and 0.69 respectively. The morphological model performed better than the model based on the mean‐dose in the contralateral gland (p‐value=0.04) as well as the model based on the mean‐dose in both parotid glands (p‐value=0.05). Preliminary results regarding the underlying feature selection reconfirm the high predictive power of the second moment, i.e. the spread of the dose, in cranio‐caudal direction within the contralateral parotid gland.

Conclusion:

Our study confirms the benefit of spatial dose information over volumetric dose information for the prediction of xerostomia after radiation therapy. Incorporation of such morphological models during treatment planning could improve the evidence for clinical decision making and patients’ quality of life after treatment. In the future, we will explore the possibility to additionally incorporate parotid shrinkage, tumor regression, uncertainties stemming from imperfect delineations, patient positioning and organ motion.
TH‐AB‐BRD‐01: Basic Physics and Quality Assurance of Proton Therapy
Authors: Arjomandy B.
Published: June 2015

The goal of this session is to introduce the audience to the physics, dosimetry, treatment planning techniques, and quality assurances (QA) procedures used in proton therapy. The course material covers the 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 double scattering (DS), uniform scanning (US), and pencil beam scanning (PBS). The session covers the treatment planning strategies used in DS, US, and PBS 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. It will also include the methodology for proton beam dosimetry for clinical commissioning, beam calibration, and quality assurance checks.

Learning Objectives:

Gain knowledge on physics, dosimetry, and treatment planning for proton therapy including intensity modulated proton therapy.

Be acquainted with the procedures involved in machine and patient treatment field specific QA.

Understand the uncertainties associated with proton therapy and currently used strategies for their mitigation in treatment planning.
TH‐AB‐BRB‐01: Solving Larger IMRT Problems by Enhanced Reduced‐Order Constrained Optimization
Authors: Nourzadeh H , Radke R , Jackson A , Bakr S , Tuomaala S
Published: June 2015

Purpose:

To report the latest improvements in the pipeline of the reduced‐order constrained optimization (ROCO) method for fast IMRT planning.

Methods:

The ROCO method involves the characterization of the solution space of the underlying fluence map IMRT optimization problem in a compact form, which enables forming a clinically tractable constrained optimization problem. The offline part of the method consists of three stages: sampling, learning the effective mode space, and mode‐dose calculation. In the sampling stage, a set of unconstrained optimization problems is solved, each corresponding to a different choice of weights and dose‐volume limits. As opposed to previous implementations, we preprocess the samples, identify the effective beamlets with non‐zero variances, and use them to attain a compact representation of the fluence space using Principal Component Analysis (PCA). In the mode‐dose calculation stage, the dose corresponding to each mode is computed based on the underlying full dose‐calculation technique. We offer a new formulation for handling possible negative mode elements, exploiting linearity of the mode‐dose map. In the online phase, a constrained optimization problem with a minimal number of constraints is solved over the basis coefficients spanning the reduced‐size space.

Results:

The results suggest that for a typical prostate case, the PCA stage can be performed about 5 times faster. Moreover, the mode dose calculation performance is accelerated by a factor of 2. Apart from computational efficiency, we also observed significantly lower memory demand for ROCO model compared to optimization models relying on pre‐calculation of dose deposition coefficients (DDCs). The proposed method has been integrated into the Varian Eclipse version 13.5 treatment planning system as a stand‐alone application.

Conclusion:

The new implementation has led a substantial savings in memory requirements and computational effort, allowing larger IMRT problems with finer beamlet resolution to be addressed with the available memory resources in less time.
TH‐AB‐BRB‐08: Optimizing Global Liver Function in Liver SBRT Treatment Planning
Authors: Wu V , Epelman M , Romeijn E , Feng M , Cao Y , Wang H , Ten Haken R , Matuszak M
Published: June 2015

Purpose:

Liver SBRT patients have variable pre‐treatment liver function, which strongly influences liver toxicity risk. Previous studies include a “perfusion‐weighted” mean dose model as a surrogate for liver function in plan optimization. This work investigates the benefit of using a more sophisticated dose‐response model that incorporates two important dose thresholds: (i) based on pre‐treatment function, a significant low‐dose may need to be exceeded before noticeable damage is done, and (ii) a high‐dose saturation point, beyond which no additional damage is done.

Methods:

Voxel‐based liver perfusion from DCE‐MRI was obtained. Two optimization models subject to the same linear dose constraints (e.g., minimum target EUD, maximum critical structure dose) were compared: the original linear model, which use pre‐treatment perfusion‐weighted mean liver dose only, and a new model designed to directly optimize the predicted post‐treatment global liver function. The latter, a highly non‐linear non‐convex problem, was approximately solved by optimizing a piece‐wise linear approximation of the objective function with a customized mixed‐integer linear programming (MILP) based algorithm. 2D synthetic and 3D clinical cases were studied to assess the potential benefit of the new model.

Results:

Compared to those of the original model, dose distributions for the functional dose response model take advantage of voxels with saturated damage to deliver the required dose to the target instead of inflicting additional damage on other well‐functioning voxels. This leads to an improvement in predicted post‐treatment global liver function. The customized solution method also improves optimization efficiency compared to solving the MILP conventionally.

Conclusion:

Functional imaging such as DCE‐MRI can be used during treatment planning to maximize potential post‐treatment function. However, simple approximations such as minimization of perfusion‐weighted mean dose may not be sufficient. Alternatively, a dose‐response model that reflects response threshold and saturation effects was shown to improve the prediction of post‐treatment function in liver SBRT.

Supported by P01 CA 059827
TH‐AB‐BRA‐02: Generation of 3D Dosimetric Reference Datasets for Commissioning and Validation of 192Ir Brachytherapy Model‐Based Dose Calculation Software
Authors: Ma Y , Vijande J , Ballester F , Tedgren Asa Carlsson , Granero D , Haworth A , Mourtada F , Fonseca G , Zourari K , Papagiannis P , Rivard M , Siebert F , Sloboda R , Smith R , Thomson R , Verhaegen F , Beaulieu L
Published: June 2015

Purpose:

Previously, generic HDR 192Ir source and shielded applicator were designed by WG‐ MBDCA, the AAPM‐ESTRO‐ABG Working Group on model‐based dose calculation algorithms. TPS vendors have incorporated both into their TPS. A charge of WG‐MBDCA is to develop a limited number of well‐defined test cases and perform MBDCA dose calculations and comparisons. Here, we describe the test cases proposed, the 3D dosimetric data generation process and the 3D gamma analysis performed.

Methods:

A voxelized phantom of 512×512×512 voxels (HU=‐1024,Air), with each voxel being 1×1×1 mm3, containing a concentric cubic contour structure (HU=0,Water) of dimension 201×201×201 voxels, was created in the DICOM‐ RT format. Three configurations were tested: i) source located at the center with surrounding structure assigned to water to mimic TG‐43 conditions, ii) source displaced +7 cm along the × axis, iii) source located at the center with the shielded applicator in place. Advanced Collapsed‐cone Engine (ACE, Elekta) and AcurosBV (Varian) were benchmarked. Reference datasets were independently produced by different groups using BrachyDose (1010 photons), GEANT4(4×1010), Penelope (2×1010), MCNP5(2 x1010), MCNP6(1011), and ALGEBRA(4×1010) Monte Carlo (MC) dose calculation platforms.

Results:

For the three configurations, MC agreed within 1% for the volume bounded by the 1% isodose surface; MCNP6 data sets were designated as reference (statistical error < 0.2% across the phantom). A 2%/1mm 3D γ‐ index criteria was used, within the volume bounded by 5% isodose surface, to account for both dose difference and source placement errors in TPS. Voxel pass rates were 95.2%, 99.5% and 90% for ACE and 98%, 98.4% and 95.9% for AcurosBV, respectively.

Conclusion:

The evaluated test cases are suitable for MBDCA QA. DICOM‐RT files are being integrated into the joint AAPM/IROC Houston registry to support end‐user TPS software commissioning.
TH‐AB‐BRA‐06: Independent Automatic Catheter Digitization Algorithm for High Dose Rate (HDR) Brachytherapy Quality Improvement (QI) and Quality Assurance (QA)
Authors: Dong P , Cunha J
Published: June 2015

Purpose:

Individual‐user and inter‐user variability during manual catheter digitization for interstitial HDR brachytherapy can be significant since the process can time consuming and error prone. We present an algorithm and workflow for catheter digitization that can be used for independent verification of manual digitization (QA) or automatic digitization (QI).

Methods:

After DICOM CT images are imported into the TPS, this proximal end of each catheter is located and numbered on a single slice near the needle template. The DICOM RP and CT files are then exported for digitization. Each transverse CT slice is smoothed with a Gaussian filter, then a convolution kernel finds the Laplacian of the CT image. This process highlights regions of rapid density change. A sliding window filter is applied to the image afterwards to locate each catheter center. The catheter is traced and geometry digitized from the needle template to the tip slice by slice, similar to the manual procedure. All the contour points are written into the original DICOM RP file and imported back into the TPS for planning. The difference between the manual digitization and the automatic digitization was quantified with a RMS calculation of the deviation between the manual and automatic catheter geometries for 11 clinical cases.

Results:

The RMS difference between the automatic and manual digitization was 0.50±0.04mm. Execution time for automatic digitization is ∼3 minutes (analysis ∼2 min; DICOM export/import ∼1 minute).

Conclusion:

This automatic catheter digitization method can be used as both a QA and QI tool. As a QI it is executed instead of manual catheter digitization. The algorithmic nature ensures reduced individual‐ and intra‐user variability and reduces digitization time. As a QA tool, the algorithm can be used to validate the manually generated catheter pattern to ensure digitization integrity.
TH‐AB‐BRA‐08: Impact of Approximating the Differential Photon Fluence On the Accuracy of the Spectroscopic Dose‐Rate Constant
Authors: Malin M , Bartol L , DeWerd L
Published: June 2015

Purpose:

To investigate why dose‐rate constants computed using the spectroscopic technique do not agree with those computed using standard Monte Carlo (MC) techniques. Published work shows that the disagreement between the two techniques is not attributable to any currently characterized source of uncertainty. This work examined if approximations to the differential photon fluence made by the spectroscopic technique are the cause of the disagreement. Specifically, approximations of the spatial, angular and energy distributions of photons leaving the source were studied.

Methods:

Phase spaces of the fluence leaving the encapsulation of 14 125‐I and 6 103‐Pd models were modified to reflect one of the approximated distributions while the other distributions were left unaltered. The dose‐rate constant was computed from the modified phase space using MC and compared to that computed using standard MC techniques. This process was repeated for each assumption with each source model.

Results:

Approximations to the differential photon fluence used in the spectroscopic technique cause offsets in the dose‐rate constant of up to 5.3% relative to standard MC techniques. The largest offsets were introduced by the approximated angular fluence distribution. Approximations of the spatial distribution leaving the encapsulation caused smaller discrepancies with an average discrepancy of −0.4% relative to standard MC techniques. Approximations to the energy distribution introduced an average discrepancy of 0.1% relative to standard MC techniques, which is within the uncertainty of the computations.

Conclusion:

Current approximations of the differential photon fluence used in the spectroscopic technique reduce the accuracy of the technique and are a source of uncertainty not currently considered with the technique. Either more accurate fluence distributions need to be used when computing the dose‐rate constant with the spectroscopic technique, or the additional uncertainties arising from the approximated distributions need to be included when using the current calculation technique.
TH‐AB‐BRA‐09: Radiation and Thermal Dosimetric Considerations of a Combination of Hyperthermia and Thermo‐Brachytherapy Seeds
Authors: Warrell G. , Shvydka D. , Parsai E.I.
Published: June 2015

Purpose:

A novel dual‐modality thermo‐brachytherapy (TB) seed invented by our group offers a potential means of adding hyperthermia to brachytherapy of prostate tumors, either simultaneously for dose enhancement or as a salvage therapy. To improve the minimum target temperature and temperature uniformity with this implant, we propose use of adjuvant hyperthermia‐only (HT‐only) seeds in unused spots within the needles used to implant the TB seeds. The impact on temperatures reached within the target, temperature uniformity, and the radiation interseed effect due to HT‐only seeds were studied.

Methods:

Previous patient plans were reproduced in a Monte Carlo (MCNP5) model to generate dose distributions of TB seeds, with and without additional HT‐ only seeds. Temperature distributions for these simulated implants were obtained with the finite element analysis solver COMSOL Multiphysics. In‐house software calculated dose‐volume and temperature‐volume histograms from these distributions.

Results:

Use of additional hyperthermia‐only seeds substantially improves the temperature uniformity of the implant. The power generation required of the seeds for adequate temperature coverage (90% of the PTV heated to 42 °C) increases exponentially with the blood perfusion rate (BPR) of the implanted tissue, but decreases with an increase in the seeds’ Curie temperature. Large blood vessels within the target decrease the volume of the PTV reaching 42 °C by ≤7%. The temperature heterogeneity coefficient was found to vary linearly with the BPR. The interseed and scatter effect on radiation dose (beyond that of a standard LDR implant) due to non‐radioactive HT‐only seeds in the target was found to reduce D90 by <10% and D50 by <7%.

Conclusion:

Additional HT‐ only seeds are useful in improving the quality of hyperthermia delivered by TB seeds. For future application of these seeds in the clinic, addition of HT‐only seeds is recommended, provided the planned D90 is adjusted to compensate for the incresed interseed effect.

This research is partially supported with NRC grant No. NRC‐HQ‐12‐G‐38‐0042.
TH‐CD‐201‐00: Clinical Networks: IT for Radiation Oncology
Authors: Siochi Ramon Alfredo
Published: June 2015

Radiation Oncology is a data intensive discipline. Data moves among various systems (imaging, treatment planning, image guidance and delivery) and the development of troubleshooting methods and appropriate QA and QC measures strongly depends on the medical physicists’ understanding of fundamental information technology concepts. This session will cover the concepts involved in networking, data transfer, DICOM and HL7 connectivity, database operations and QA for IT. Databases are at the heart of all modern radiotherapy clinics and are used for planning as well as treatment delivery, and they also drive the observed state of the data at various clients. Querying and reporting from the database is an important skill that medical physicists need to learn. Interactions between clients and databases need to be well understood to ensure that correct, synchronized, and up‐to‐date information is properly communicated amongst all stakeholders involved in the treatment management process. Entrance into the database via DICOM, HL7 and other proprietary transfer mechanisms must also be tested for accuracy of information storage and presentation. Issues with inter‐operability between systems must also be well understood during acceptance testing and commissioning activities. The ongoing efforts of IHE‐RO will be explained in the context of the need for increased engagement on the part of medical physicists.

Learning Objectives:

Understand the basic IT infrastructure required to run a radiotherapy department

Become familiar with database concepts to improve troubleshooting skills

Learn the principles of designing a QA program tailored to one's specific clinical IT environment.

Curan: Funding from AAPM for travel and expenses DICOM and Funding from ASTRO for travel and expenses IHE RO
TH‐CD‐201‐01: Radiation Therapy Databases
Authors: Balter P.
Published: June 2015

Radiation Oncology is a data intensive discipline. Data moves among various systems (imaging, treatment planning, image guidance and delivery) and the development of troubleshooting methods and appropriate QA and QC measures strongly depends on the medical physicists’ understanding of fundamental information technology concepts. This session will cover the concepts involved in networking, data transfer, DICOM and HL7 connectivity, database operations and QA for IT. Databases are at the heart of all modern radiotherapy clinics and are used for planning as well as treatment delivery, and they also drive the observed state of the data at various clients. Querying and reporting from the database is an important skill that medical physicists need to learn. Interactions between clients and databases need to be well understood to ensure that correct, synchronized, and up‐to‐date information is properly communicated amongst all stakeholders involved in the treatment management process. Entrance into the database via DICOM, HL7 and other proprietary transfer mechanisms must also be tested for accuracy of information storage and presentation. Issues with inter‐operability between systems must also be well understood during acceptance testing and commissioning activities. The ongoing efforts of IHE‐RO will be explained in the context of the need for increased engagement on the part of medical physicists.

Learning Objectives:

Understand the basic IT infrastructure required to run a radiotherapy department

Become familiar with database concepts to improve troubleshooting skills

Learn the principles of designing a QA program tailored to one's specific clinical IT environment.

Curan: Funding from AAPM for travel and expenses DICOM and Funding from ASTRO for travel and expenses IHE RO
TH‐CD‐201‐03: QA for Radiotherapy IT
Authors: Siochi R.
Published: June 2015

Radiation Oncology is a data intensive discipline. Data moves among various systems (imaging, treatment planning, image guidance and delivery) and the development of troubleshooting methods and appropriate QA and QC measures strongly depends on the medical physicists’ understanding of fundamental information technology concepts. This session will cover the concepts involved in networking, data transfer, DICOM and HL7 connectivity, database operations and QA for IT. Databases are at the heart of all modern radiotherapy clinics and are used for planning as well as treatment delivery, and they also drive the observed state of the data at various clients. Querying and reporting from the database is an important skill that medical physicists need to learn. Interactions between clients and databases need to be well understood to ensure that correct, synchronized, and up‐to‐date information is properly communicated amongst all stakeholders involved in the treatment management process. Entrance into the database via DICOM, HL7 and other proprietary transfer mechanisms must also be tested for accuracy of information storage and presentation. Issues with inter‐operability between systems must also be well understood during acceptance testing and commissioning activities. The ongoing efforts of IHE‐RO will be explained in the context of the need for increased engagement on the part of medical physicists.

Learning Objectives:

Understand the basic IT infrastructure required to run a radiotherapy department

Become familiar with database concepts to improve troubleshooting skills

Learn the principles of designing a QA program tailored to one's specific clinical IT environment.

Curan: Funding from AAPM for travel and expenses DICOM and Funding from ASTRO for travel and expenses IHE RO
TH‐E‐304‐03: Absorbed Dose Standards for Brachytherapy
Authors: Sarfehnia A.
Published: June 2015

It is fair to say that the clinical determination of absorbed dose in a high‐energy photon beam is the only situation where there is currently a direct chain of traceability from the standards laboratory to the end‐user and the protocol requires only ‘simple’ conversion/ correction factors. For all other modalities the primary standard is in a different quantity (e.g., air kerma) or in the right quantity but a different radiation beam (e.g., for proton dosimetry) from what is required for the clinical dose delivery. Complex conversion factors are often then required in applying protocols to give the desired final quantity. Such conversion factors can be from kerma to dose (in the case of kilovoltage x‐rays or brachytherapy) or from one type of radiation beam to another (in the case of electron or proton beams). Even non‐standard MV photon beams (e.g. non‐standard geometry, presence of a magnetic field) can have a significant impact on the procedure that must be followed compared to what is found in TG‐51. While national and international bodies, such as the AAPM and the IAEA, primarily address the end‐user's needs with protocols and guidelines, calibration laboratories are developing new measurement standards and approaches that should simplify the calibration chain and therefore lead to simpler, more robust procedures to yield absorbed dose in the beam of interest in the radiation therapy clinic.

This session will provide a broad overview of the worldwide developments currently in progress, and also planned for the near future, of absorbed dose standards for a range of beam modalities including: Proton beams

HDR and LDR brachytherapy

Non‐standard photon fields (small fields, impact of magnetic fields, etc)

Learning Objectives:

Understand the present traceability chain for all beam modalities in regular use in the radiation therapy clinic.

Understand the current and planned development of absorbed dose standards for modalities other than linac photon beams.

Understand the physical basis of these standards and the potential impact on end‐user dosimetry.

Understand the likely impact of these standards developments on dosimetry protocols in the coming 5–10 years.
TH‐EF‐BRD‐01: Fluence Map Optimization Using a Voxel‐Based Reduced‐Order Constrained Optimization Model
Authors: Nourzadeh H , Radke R , Jackson A
Published: June 2015

Purpose:

To develop a method for adaptively imposing dose constraints on individual voxels and structures in IMRT planning, such that a sequence of computationally tractable, low‐dimensional constrained optimization problems is solved that leads to plans satisfying dose‐volume and other clinical constraints.

Methods:

The previously proposed Reduced Order Constrained Optimization (ROCO) algorithm is applied to parameterize an IMRT problem by a small number of coefficients on learned principal components, instead of a large number of independent beamlet fluences. A sequence of constrained optimization problems is solved over the set of coefficients in which the importance of each voxel and structure is adaptively changed in the objective function. Instead of manually investigating good settings for these parameters, an adaptive gain scheduling rule based on control theory automatically adjusts the weights, quickly converging to a plan satisfying the clinical constraints. In particular, we apply a decentralized proportional‐integral controller that adjusts structure weights using dose‐volume constraint (DVC) violation feedback. We compare our method against a previous approach in which subsets of voxels were iteratively selected and hard‐constrained in an attempt to satisfy DVCs.

Results:

The technique was tested on five different non‐nodal prostate cases, each resulting in clinically acceptable plans in less than 2.5 minutes on average. The quality of plans is far superior to the alternate method, significantly reducing OARs doses while meeting PTV V95 goals.

Conclusion:

Compared to alternative optimization schemes in the full intensity space, the propose scheme offers fast convergence rate, demands significantly less memory resources and computational effort, which allows larger IMRT problems to be addressed.
TH‐EF‐BRD‐04: A Fast 4D IMRT/VMAT Planning Method Based On Segment Aperture Morphing
Authors: Klawikowski S , Tai A , Ates O , Ahunbay E , Li X
Published: June 2015

Purpose:

To investigate dosimetric advantages of a 4D IMRT/VMAT planning method employing a segment aperture morphing (SAM) algorithm accounting for respiration‐induced target motion and deformation. The minimal required number of calculated SAM phases was tested to minimize plan creation time.

Methods:

The new technique involves generating 4D step‐and‐shoot IMRT or VMAT plans based on 10‐phase 4DCT in the following steps: (1) optimizing a flattening‐filter‐free IMRT/VMAT plan based on the 50% phase CT dataset (2) warping contours from the 50%‐phase CT to the remaining phases via deformable image registration (DIR) and manual editing (3) morphing segment apertures of the 50%‐phase plan to each phase CT using SAM that adjusts each aperture shape based on the change of the beam's‐eye‐view of the target contour between the two phases (4) calculating the dose distribution for each phase with the morphed apertures and same MUs as in the 50%‐phase plan, and (5) generating the 4D plan by accumulating the dose from all nine SAM‐corrected phases and the original 50% optimized phase using DIR. 4D plans based on 10, 7, 5 and 3 SAM‐corrected phases were generated for representative lung and pancreatic cancer cases, and were compared using DVH analysis against the gold‐standard 4D plan where all 10 phases are individually planned and optimized. A SAM‐corrected 4D plan was also compared to a simpler phase‐by‐phase isocenter‐shifted plan to assess accounting for tumor deformation and translation opposed to only tumor translation.

Results:

Plan DVH quality for the 10‐phase and 7‐phase 4D SAM plans were comparable with the fully‐optimized 4D plans (PTV‐D95's within 3% of fully‐optimized plans). SAM based algorithms out‐performed simpler isocenter‐shifted‐only approaches (∼1.5% PTV‐D95 improvement). SAM‐based 4D planning greatly reduces planning time verses individually optimized phase planning techniques.

Conclusion:

SAM with DIR provides a fast and accurate way to create 4D IMRT/VMAT plans.
TH‐EF‐BRD‐05: A New Intensity Modulation Radiation Therapy (IMRT) Optimizer Solution with Robust Fluence Maps for MLC Segmentation
Authors: Nguyen D , O'Connor D , Yu V , Ruan D , Cao M , Low D , Sheng K
Published: June 2015

Purpose:

Converting theoretical fluence maps to deliverable MLC segments while maintaining plan quality has posed a significant challenge for highly complex IMRT plans involving many fields. Rather than simplifying individual fluence maps, we have formulated the problem as a global dose domain fluence map optimization with anisotropic total variation regularization (FMOATVR) problem and then solve the large scale optimization problem.

Methods:

The 4π planning platform was used to select and optimize 20 non‐coplanar beams for 2 GBM, 2 head & neck, and 2 lung patients with varying prescription doses, including H&N case #2 that had 3 prescription dose levels of 54 Gy, 59.4 Gy, and 69.96 Gy. The plans were re‐run using FMOATVR and the same 20 4π beams. The L2 norm of dose deviation from the ideal prescription with zero dose to OARs was used as the objective function, which was subjected to anisotropic total variation regularization to encourage piecewise smoothness in the fluence maps. The fluences were stratified into equal step sizes to create 300 MLC segments. The dose distributions were normalized so the prescription dose covered cover 95% of the PTV. H&N case #2 was normalized to cover the 59.4 Gy PTV, which was included in the statistics.

Results:

Compared to the directly stratified 4π plans (4π DS), the 4π FMOATVR method reduced the average mean and max dose to the OARs by 3.54% and 4.15%, respectively, of the prescription dose. Dose coverage was also improved, evidenced by an average D98 and D99 increase of 1.63% and 2.74% of the prescription dose, respectively. The average PTV homogeneity was increased from 0.833 to 0.905.

Conclusion:

The global dose domain fluence map optimization formula and its solver provides a straightforward yet potent method to attain high quality while remaining robust to MLC segmentation for complex IMRT.

This work is supported in part by Varian Medical Systems, Inc.
TH‐EF‐BRD‐06: Rapid Direct Aperture Optimization Via Piecewise Linear Dose Calculation Based On the Dose Influence Matrix
Authors: Zeng X , Guo M , Gao H
Published: June 2015

Purpose:

Direct aperture optimization (DAO) considers the direct optimization of aperture leaf positions and weights, instead of a two‐step procedure for intensity modulated radiation therapy (IMRT), i.e. first fluence map optimization and then leaf sequencing algorithm. However, the use of the Monte Carlo (MC) method in DAO is often time‐consuming. This work aims to develop a rapid DAO algorithm.

Methods:

The proposed method is based on the dose influence matrix. That is the leaf position variable is a piecewise linear function of the dose influence matrix. Specifically, the piecewise linear dose calculation model with respect to left and right leaf positions of a aperture consists of three terms: (i) the total dose contribution from the bixels that are fully between the left and right leaf positions; (ii) the partial dose contribution from the leftmost bixel that the left leaf falls into; (iii) the partial dose contribution from the rightmost bixel that the right leaf falls into. As a Result, this model is piecewise linear with respect to the leaf positions.Then instead of MC, this piecewise linear dose calculation engine based on the dose influence matrix is incorporated into the simulated annealing algorithm for DAO. As a Result of rapid dose calculation based on the dose influence matrix, the speed of DAO is improved significantly.

Results:

The proposed algorithm was tested on the TG119 phantom from the MGH public dataset.

Conclusion:

Rapid DAO algorithm is developed with the dose calculation engine that replaces MC by the piecewise linear dose calculation based on the dose influence matrix.

The authors were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500).
TH‐EF‐BRD‐10: Spatiotemporal Dose Shaping to Achieve Uniform Fractionation in Healthy Tissues Along with Hypo‐Fractionation in Targets
Authors: Unkelbach J , Bussiere M , Shih H , Chapman P , Loeffler J
Published: June 2015

Purpose:

Fractionation decisions in radiotherapy face the tradeoff between increasing the number of fractions to spare normal tissues, and increasing the total dose to achieve the same effect in the target volume. In that regard, the ideal treatment would fractionate in normal tissues while simultaneously hypo‐fractionating in the target. Interestingly, this is possible to a limited degree by delivering distinct dose distributions in different fractions. The dose distributions have to be designed such that similar doses are delivered to normal tissues while delivering high single fraction doses to parts of the target. We demonstrate that this concept may lead to an improved therapeutic ratio for rotation therapy treatments using conventional photon beams.

Methods:

Fractionation effects are modeled via the biologically equivalent dose (BED) model. Treatment plan optimization is performed using objective functions evaluated for the cumulative BED delivered at the end of treatment. This allows for simultaneously optimizing multiple distinct treatment plans for different fractions.

Results:

The concept is demonstrated for large cerebral arteriovenous malformations (AVM) treated in two fractions. It is shown that the optimal treatment delivers a large dose to the center of the AVM in the first fraction. The second fraction delivers the missing dose to the rim of the target volume. The target is divided into center and rim in such a way that the dose received by normal brain adjacent to the AVM is approximately equal in both fractions.

Conclusion:

The BED in the target can be increased by hypo‐fractionating the central core. Since the fractionation effect is exploited in the normal tissue, this leads to an improved therapeutic ratio overall. The approach may potentially be beneficial for large AVMs or tumors embedded in a dose‐limiting normal tissue treated with stereotactic regimens.