bluuebunny/crossref_metadata_2025 huggingface dataset:
WE‐AB‐209‐03: Adaptive SBRT Planning for Interfraction Motion
Authors: Wu V , Epelman M , Brock K , Feng M , Ten Haken R , Matuszak M
Published: July 2016

Purpose:

Interfraction changes in abdominal SBRT patient anatomy are challenging to address with image guidance alone because avoidance of an overlapping high‐priority gastrointestinal normal structure may come at the expense of tumor coverage. This work investigates the benefit of anticipating interfraction motion and exploiting favorable geometries at the time of treatment through dose adaptive planning.

Methods:

Beyond no change, six scenarios of duodenum shifts per fraction are generated (±1 cm XYZ) and sample five‐fraction paths (combinations of no and/or the indicated changes) are simulated. Under the baseline (nominal) plan, generated assuming no uncertainty, three scenarios result in overdosing the duodenum while the others result in safe or reduced dose. The nominal plan under‐doses the PTV (36.1 Gy‐EQD2) due to duodenum proximity. Three adaptive strategies are considered: i) Standard IGRT: for duodenum overdose, patient's isocenter is shifted to satisfy the duodenum dose tolerance; nominal plan is delivered otherwise; ii) Scaling: nominal plan scaled up/down in favorable/unfavorable patient geometries; iii) Mixed: shifting as in i) but boosting otherwise. If max duodenum dose (limit 6 Gy/Fx) is exceeded, dose distributions are shifted or scaled down, depending on the strategy considered. In all adaptive fractions, critical structure dose limits are satisfied, even if the PTVs are under‐dosed.

Results:

Generally, scaling dose allows better PTV dose (gEUD in Gy‐EQD2); however, in some scenarios, shifting is better than scaling down dose. In a path with 2 fractions of the duodenum not moving and 3 fractions of an unfavorable duodenum position, scaling dose results in the best coverage, with 36.8 Gy‐EQD2. In another path with 2 favorable and 3 unfavorable duodenum positions, the mixed strategy achieves the highest coverage with 41.0 Gy‐EQD2.

Conclusions:

Anticipating interfraction motion and exploiting favorable geometries through dose adaptive scaling can improve overall tumor coverage by compensating for conservative IGRT in unfavorable fractions.

NIH‐P01‐CA059827
WE‐AB‐209‐05: Development of an Ultra‐Fast High Quality Whole Breast Radiotherapy Treatment Planning System
Authors: Sheng Y , Li T , Yoo S , Yin F , Blitzblau R , Horton J , Palta M , Hahn C , Ge Y , Wu Q
Published: July 2016

Purpose:

To enable near‐real‐time (<20sec) and interactive planning without compromising quality for whole breast RT treatment planning using tangential fields.

Methods:

Whole breast RT plans from 20 patients treated with single energy (SE, 6MV, 10 patients) or mixed energy (ME, 6/15MV, 10 patients) were randomly selected for model training. Additional 20 cases were used as validation cohort. The planning process for a new case consists of three fully automated steps:1. Energy Selection. A classification model automatically selects energy level. To build the energy selection model, principle component analysis (PCA) was applied to the digital reconstructed radiographs (DRRs) of training cases to extract anatomy‐energy relationship.2. Fluence Estimation. Once energy is selected, a random forest (RF) model generates the initial fluence. This model summarizes the relationship between patient anatomy's shape based features and the output fluence. 3. Fluence Fine‐tuning. This step balances the overall dose contribution throughout the whole breast tissue by automatically selecting reference points and applying centrality correction. Fine‐tuning works at beamlet‐level until the dose distribution meets clinical objectives. Prior to finalization, physicians can also make patient‐specific trade‐offs between target coverage and high‐dose volumes.The proposed method was validated by comparing auto‐plans with manually generated clinical‐plans using Wilcoxon Signed‐Rank test.

Results:

In 19/20 cases the model suggested the same energy combination as clinical‐plans. The target volume coverage V100% was 78.1±4.7% for auto‐plans, and 79.3±4.8% for clinical‐plans (p=0.12). Volumes receiving 105% Rx were 69.2±78.0cc for auto‐plans compared to 83.9±87.2cc for clinical‐plans (p=0.13). The mean V10Gy, V20Gy of the ipsilateral lung was 24.4±6.7%, 18.6±6.0% for auto plans and 24.6±6.7%, 18.9±6.1% for clinical‐plans (p=0.04, <0.001). Total computational time for auto‐plans was < 20s.

Conclusion:

We developed an automated method that generates breast radiotherapy plans with accurate energy selection, similar target volume coverage, reduced hotspot volumes, and significant reduction in planning time, allowing for near‐real‐time planning.
WE‐AB‐209‐07: Explicit and Convex Optimization of Plan Quality Metrics in Intensity‐Modulated Radiation Therapy Treatment Planning
Authors: Engberg L , Eriksson K , Forsgren A , Hardemark B
Published: July 2016

Purpose:

To formulate objective functions of a multicriteria fluence map optimization model that correlate well with plan quality metrics, and to solve this multicriteria model by convex approximation.

Methods:

In this study, objectives of a multicriteria model are formulated to explicitly either minimize or maximize a dose‐at‐volume measure. Given the widespread agreement that dose‐at‐volume levels play important roles in plan quality assessment, these objectives correlate well with plan quality metrics. This is in contrast to the conventional objectives, which are to maximize clinical goal achievement by relating to deviations from given dose‐at‐volume thresholds: while balancing the new objectives means explicitly balancing dose‐at‐volume levels, balancing the conventional objectives effectively means balancing deviations. Constituted by the inherently non‐convex dose‐at‐volume measure, the new objectives are approximated by the convex mean‐tail‐dose measure (CVaR measure), yielding a convex approximation of the multicriteria model.

Results:

Advantages of using the convex approximation are investigated through juxtaposition with the conventional objectives in a computational study of two patient cases. Clinical goals of each case respectively point out three ROI dose‐at‐volume measures to be considered for plan quality assessment. This is translated in the convex approximation into minimizing three mean‐tail‐dose measures. Evaluations of the three ROI dose‐at‐volume measures on Pareto optimal plans are used to represent plan quality of the Pareto sets. Besides providing increased accuracy in terms of feasibility of solutions, the convex approximation generates Pareto sets with overall improved plan quality. In one case, the Pareto set generated by the convex approximation entirely dominates that generated with the conventional objectives.

Conclusion:

The initial computational study indicates that the convex approximation outperforms the conventional objectives in aspects of accuracy and plan quality.
WE‐AB‐209‐10: Optimizing the Delivery of Sequential Fluence Maps for Efficient VMAT Delivery
Authors: Craft D , Balvert M
Published: July 2016

Purpose:

To develop an optimization model and solution approach for computing MLC leaf trajectories and dose rates for high quality matching of a set of optimized fluence maps to be delivered sequentially around a patient in a VMAT treatment.

Methods:

We formulate the fluence map matching problem as a nonlinear optimization problem where time is discretized but dose rates and leaf positions are continuous variables. For a given allotted time, which is allocated across the fluence maps based on the complexity of each fluence map, the optimization problem searches for the best leaf trajectories and dose rates such that the original fluence maps are closely recreated. Constraints include maximum leaf speed, maximum dose rate, and leaf collision avoidance, as well as the constraint that the ending leaf positions for one map are the starting leaf positions for the next map. The resulting model is non‐convex but smooth, and therefore we solve it by local searches from a variety of starting positions. We improve solution time by a custom decomposition approach which allows us to decouple the rows of the fluence maps and solve each leaf pair individually. This decomposition also makes the problem easily parallelized.

Results:

We demonstrate method on a prostate case and a head‐and‐neck case and show that one can recreate fluence maps to high degree of fidelity in modest total delivery time (minutes).

Conclusion:

We present a VMAT sequencing method that reproduces optimal fluence maps by searching over a vast number of possible leaf trajectories. By varying the total allotted time given, this approach is the first of its kind to allow users to produce VMAT solutions that span the range of wide‐field coarse VMAT deliveries to narrow‐field high‐MU sliding window‐like approaches.
WE‐AB‐207B‐05: Correlation of Normal Lung Density Changes with Dose After Stereotactic Body Radiotherapy (SBRT) for Early Stage Lung Cancer
Authors: Wu Q , Devpura S , Feghali K , Liu C , Ajlouni M , Movsas B , Chetty I
Published: July 2016

Purpose:

To investigate correlation of normal lung CT density changes with dose accuracy and outcome after SBRT for patients with early stage lung cancer.

Methods:

Dose distributions for patients originally planned and treated using a 1‐D pencil beam‐based (PB‐1D) dose algorithm were retrospectively recomputed using algorithms: 3‐D pencil beam (PB‐3D), and model‐based

Methods:

AAA, Acuros XB (AXB), and Monte Carlo (MC). Prescription dose was 12 Gy × 4 fractions. Planning CT images were rigidly registered to the followup CT datasets at 6–9 months after treatment. Corresponding dose distributions were mapped from the planning to followup CT images. Following the method of Palma et al .(1–2), Hounsfield Unit (HU) changes in lung density in individual, 5 Gy, dose bins from 5–45 Gy were assessed in the peri‐tumor region, defined as a uniform, 3 cm expansion around the ITV(1).

Results:

There is a 10–15% displacement of the high dose region (40–45 Gy) with the model‐based algorithms, relative to the PB method, due to the electron scattering of dose away from the tumor into normal lung tissue (Fig.1). Consequently, the high‐dose lung region falls within the 40–45 Gy dose range, causing an increase in HU change in this region, as predicted by model‐based algorithms (Fig.2). The patient with the highest HU change (∼110) had mild radiation pneumonitis, and the patient with HU change of ∼80–90 had shortness of breath. No evidence of pneumonitis was observed for the 3 patients with smaller CT density changes (<50 HU). Changes in CT densities, and dose‐response correlation, as computed with model‐based algorithms, are in excellent agreement with the findings of Palma et al. (1–2).

Conclusion:

Dose computed with PB (1D or 3D) algorithms was poorly correlated with clinically relevant CT density changes, as opposed to model‐based algorithms. A larger cohort of patients is needed to confirm these results.

This work was supported in part by a grant from Varian Medical Systems, Palo Alto, CA
WE‐AB‐207B‐08: Exploring and Refining the QUANTEC Guideline to Reduce Severe Hyposalivation Following IMRT for Head and Neck Cancer
Authors: Thor M , Owosho A , Rosenburg H , Yom S , Oh J , Riaz N , Tsai J , Lee N , Huryn J , Estilo C , Deasy J
Published: July 2016

Purpose:

The aim of this study was to investigate the usefulness of the QUANTEC guideline to prevent xerostomia after intensity‐modulated radiotherapy (IMRT) for head and neck cancer (HNC) with respect to follow‐up time. In addition, we explored alternative guidelines to further reduce xerostomia.

Methods:

The QUANTEC guideline suggests a mean dose to the contralateral (Dmeancontra) parotid<20 Gy, or Dmeancontra and Dmean to the ipsilateral parotid (Dmeanipsi)<25 Gy. Stimulated whole mouth saliva flow measurements (WMSFM) were conducted at a median of 11 (3–24) months for 63 patients treated with IMRT for HNC to a median dose of 70 Gy in 2006–2015. Severe hyposalivation/xerostomia was defined as WMSFM ≤25% post‐ relative to pre‐RT. Patients were stratified into a <6m (xerostomia: 27% (n=15)), and a 6–24m (xerostomia: 19% (n=10)) follow‐up group. Dose‐response modeling was performed using logistic regression including Dmeancontra, or Dmeancontra and Dmeanipsi. The area under the receiver‐operating characteristics curve (AUC) was used to assess discriminative ability, and the agreement between the estimated and observed rate of xerostomia was given by Spearman's rank correlation coefficient (Rs), and related p‐values.

Results:

Of the non‐xerostomia patients, 65% (<6m) and 56% (6–24m) fulfilled Dmeancontra<20 Gy. The estimated and observed rate of xerostomia agreed at <6m (AUC=0.78; Rs=0.46; p=0.001), and was 28% at Dmeancontra=20 Gy. A smaller number of non‐xerostomia patients fulfilled the two‐gland guideline (33% (<6m) and 26% (6–24m)), but the AUC was higher than using Dmeancontra only (<6m: AUC=0.90; Rs=0.63; p<0.0001; 6–24m: AUC=0.84; Rs=0.25; p=0.08), and the following amendment of the two‐gland guideline was suggested: (0.17*Dmeancontra+0.11*Dmeanipsi‐8.13)<‐1.60 (<6m), and (0.05*Dmeancontra+0.02*Dmeanipsi‐3.10)<‐1.60 (6–24m).

Conclusion:

The QUANTEC guideline is effective to prevent xerostomia <6m post‐RT, but its usefulness is reduced at later follow‐up times. The suggested amendment to the two‐gland QUANTEC guideline should be further investigated in an independent cohort of HNC patients treated with IMRT.
WE‐AB‐207B‐10: On Spinal Nerve Toxicity from Single‐Session SAbR in Pigs and the Translation of Small Animal NTCP Models
Authors: Hrycushko B , Medin P
Published: July 2016

Purpose:

The incidence of peripheral neuropathy has risen with increased utilization of SAbR. There is no consensus regarding the dose‐tolerance of the peripheral nervous system. In 2015, we commenced an investigation to test the hypotheses that single‐session irradiation to the pig spinal nerves exhibit a similar dose‐tolerance as that of the spinal cord and that a dose‐length effect exists. This work evaluates the direct application of small animal NTCP models to both large animal spinal cord and preliminary peripheral nerve data.

Methods:

To date, 16 of 25 Yucatan minipigs have received single‐session SAbR to a 1.5cm length and 4 of 25 have received irradiation to a 0.5cm length of left‐sided C6‐C8 spinal nerves. Toxicity related gait change has been observed in 13 animals (9 from the long length group and 4 from the short). This preliminary data is overlaid on several dose‐response models which have been fit to rodent spinal cord tolerance experiments. Model parameters define a toxicity profile between a completely serial or parallel behaving organ. Adequacy of model application, including how length effects are handled, to published minipig spinal cord dose‐response data and to preliminary peripheral nerve response data was evaluated through residual analysis.

Results:

No rodent‐derived dose‐response models were directly applicable to all pig data for the different lengths irradiated. Several models fit the long‐length irradiated spinal cord data well, with the more serial‐like models fitting best. Preliminary data on the short‐length irradiation suggests no length effect exists, disproving our hypothesis.

Conclusion:

Direct application of small‐animal NTCP models to pig data suggests dose‐length effect predictions from small animal data may not translate clinically. However, the small animal models used have not considered dose heterogeneity and it is expected that including the low‐to‐mid dose levels in the penumbral region will improve this match.

This work was funded by the Cancer Prevention Research Institute of Texas (CPRIT).
WE‐AB‐207B‐12: Prospective Study of the Relationship Between Dose‐Volume Clinical Toxicity and Patient Reported Outcomes in Lung Cancer Patients Treated with SBRT
Authors: Mayyas E , Vance S , Brown S , Liu J , Kim J , Zhen S , Devpura S , Ajlouni M , Salim S , Chetty I , Movsas B
Published: July 2016

Purpose:

To determine in a prospective study, the correlation between radiation dose/volume, clinical toxicities and patient‐reported, quality of life (QOL) resulting from lung SBRT.

Methods:

For 106 non‐small cell lung cancer (NSCLC) patients receiving SBRT (12 Gy × 4), symptoms including cough, dyspnea, fatigue and pneumonitis were measured at baseline (before treatment), after treatment and 3, 6, and 12 months post‐treatment. Toxicity was graded from zero to five. Dosimetric parameters such as the MLD, D10%, D20%, and lung subvolumes (V10 and V20) were obtained from the treatment plan. Dosimetric parameters and number of patients demonstrating toxicity ≥ grade 2 were tabulated. Linear regression analysis was used to calculate correlations between MLD and D10, D20, V10 and V20.

Results:

The percentages of patients with > grade 2 pneumonitis, fatigue, cough, and dyspnea over 3 to 12 months increased from 0.0% to 3.5%, 3.2% to 10.5%, 4.3% to 8.3%, and 10.8% to 18.8%, respectively. Computed dose indices D10%, D20% were 7.9±4.8 Gy and 3.0±2.3 Gy, respectively. MLD ranged from 0.34 Gy up to 9.9 Gy with overall average 3.0±1.7 Gy. The averages of the subvolumes V10 and V20 were respectively 8.9±5.3% and 3.0±2.4%. The linear regression analysis showed that V10 and D10 demonstrated the strongest correlation to MLD; R2= 0.92 and 0.87, respectively. V20, and D20 were also strongly correlated with MLD; R2 = 0.81 and 0.84 respectively. A correlation was also found to exist between MLD > 2 Gy and ≥ grade 2 cough and dyspnea. Subvolume values for 2Gy MLD were 5.3% for V10 and 2% for V20.

Conclusion:

Dosimetric indices: MLD ≥ 2Gy, D10 ≥ 5Gy and V10 ≥ 5% of the total lung volume were predictive of > grade 2 cough and dyspnea QOL data. The QOL results are a novel component of this work.

acknowledgement of the Varian grant support.
WE‐D‐204‐03: CAMPEP Residencies in a Canadian Context: Comprehensive Cancer Centers and Integrated Learning Environments
Authors: Parker W.
Published: July 2016

Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices.

Learning Objectives:

To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs.

To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
WE‐D‐204‐04: Learning the Ropes: Clinical Immersion in the First Month of Residency
Authors: Dieterich S.
Published: July 2016

Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices.

Learning Objectives:

To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs.

To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
WE‐DE‐201‐01: BEST IN PHYSICS (THERAPY): A Fast Multi‐Target Inverse Treatment Planning Strategy Optimizing Dosimetric Measures for High‐Dose‐Rate (HDR) Brachytherapy
Authors: Guthier C , Damato A , Viswanathan A , Hesser J , Cormack R
Published: July 2016

Purpose:

Inverse treatment planning (ITP) for interstitial HDR brachytherapy of gynecologic cancers seeks to maximize coverage of the clinical target volumes (tumor and vagina) while respecting dose‐volume‐histogram related dosimetric measures (DMs) for organs at risk (OARs). Commercially available ITP tools do not support DM‐based planning because it is computationally too expensive to solve. In this study we present a novel approach that allows fast ITP for gynecologic cancers based on DMs for the first time.

Methods:

This novel strategy is an optimization model based on a smooth DM‐based objective function. The smooth approximation is achieved by utilizing a logistic function for the evaluation of DMs. The resulting nonconvex and constrained optimization problem is then optimized with a BFGS algorithm. The model was evaluated using the implant geometry extracted from 20 patient treatment plans under an IRB‐approved retrospective study. For each plan, the final DMs were evaluated and compared to the original clinical plans. The CTVs were the contoured tumor volume and the contoured surface of the vagina. Statistical significance was evaluated with a one‐sided paired Wilcoxon signed‐rank test.

Results:

As did the clinical plans, all generated plans fulfilled the defined DMs for OARs. The proposed strategy showed a statistically significant improvement (p<0.001) in coverage of the tumor and vagina, with absolute improvements of related DMs of (6.9 +/− 7.9)% and (28.2 +/− 12.0)%, respectively. This was achieved with a statistically significant (p<0.01) decrease of the high‐dose‐related DM for the tumor. The runtime of the optimization was (2.3 +/− 2.0) seconds.

Conclusion:

We demonstrated using clinical data that our novel approach allows rapid DM‐based optimization with improved coverage of CTVs with fewer hot spots. Being up to three orders of magnitude faster than the current clinical practice, the method dramatically shortens planning time.
WE‐DE‐201‐04: Cross Validation of Knowledge‐Based Treatment Planning for Prostate LDR Brachytherapy Using Principle Component Analysis
Authors: Roper J , Ghavidel B , Chanyavanich V , Godette K , Schreibmann E
Published: July 2016

Purpose:

To validate a knowledge‐based algorithm for prostate LDR brachytherapy treatment planning.

Methods:

A dataset of 100 cases was compiled from an active prostate seed implant service. Cases were randomized into 10 subsets. For each subset, the 90 remaining library cases were registered to a common reference frame and then characterized on a point by point basis using principle component analysis (PCA). Each test case was converted to PCA vectors using the same process and compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best‐matched library case. The seed arrangement was extracted from the best‐matched case and used as a starting point for planning the test case. Any subsequent modifications were recorded that required input from a treatment planner to achieve V100>95%, V150<60%, V200<20%. To simulate operating‐room planning constraints, seed activity was held constant, and the seed count could not increase.

Results:

The computational time required to register test‐case contours and evaluate PCA similarity across the library was 10s. Preliminary analysis of 2 subsets shows that 9 of 20 test cases did not require any seed modifications to obtain an acceptable plan. Five test cases required fewer than 10 seed modifications or a grid shift. Another 5 test cases required approximately 20 seed modifications. An acceptable plan was not achieved for 1 outlier, which was substantially larger than its best match. Modifications took between 5s and 6min.

Conclusion:

A knowledge‐based treatment planning algorithm for prostate LDR brachytherapy is being cross validated using 100 prior cases. Preliminary results suggest that for this size library, acceptable plans can be achieved without planner input in about half of the cases while varying amounts of planner input are needed in remaining cases. Computational time and planning time are compatible with clinical practice.
WE‐DE‐201‐07: Measurement of Real‐Time Dose for Tandem and Ovoid Brachytherapy Procedures Using a High Precision Optical Fiber Radiation Detector
Authors: Belley MD , Faught A , Moore B , Subashi E , Langloss B , Therien MJ , Yoshizumi TT , Chino JP , Craciunescu O
Published: July 2016

Purpose:

Development of a novel on‐line dosimetry tool is needed to move toward patient‐specific quality assurance measurements for Ir‐192 HDR brachytherapy to verify accurate dose delivery to the intended location. This work describes the development and use of a nano‐crystalline yttrium oxide inorganic scintillator based optical‐fiber detector capable of acquiring real‐time high‐precision dose measurements during tandem and ovoid (T&O) gynecological (GYN) applicator Ir‐192 HDR brachytherapy procedures.

Methods:

An optical‐fiber detector was calibrated by acquiring light output measurements in liquid water at 3, 5, 7, and 9cm radial source‐detector‐distances from an Ir‐192 HDR source. A regression model was fit to the data to describe the relative light output per unit dose (TG‐43 derived) as a function of source‐detector‐distance. Next, the optical‐fiber detector was attached to a vaginal balloon fixed to a Varian Fletcher‐Suit‐Delclos‐style applicator (to mimic clinical setup), and localized by acquiring high‐resolution computed tomography (CT) images. To compare the physical point dose to the TPS calculated values (TG‐43 and Acuros‐BV), a phantom measurement was performed, by submerging the T&O applicator in a liquid water bath and delivering a treatment template representative of a clinical T&O procedure. The fiber detector collected scintillation signal as a function of time, and the calibration data was applied to calculate both real‐time dose rate, and cumulative dose.

Results:

Fiber cumulative dose values were 100.0cGy, 94.3cGy, and 348.9cGy from the tandem, left ovoid, and right ovoid dwells, respectively (total of 443.2cGy). A plot of real time dose rate during the treatment was also acquired. The TPS values at the fiber location were 458.4cGy using TG‐43, and 437.6cGy using Acuros‐BV calculated as Dm,m (per TG‐186).

Conclusion:

The fiber measured dose value agreement was 3% vs TG‐43 and −1% vs Acuros‐BV. This fiber detector opens up new possibilities for performing patient‐specific quality assurance for Ir‐192 HDR GYN procedures.

Funding from Coulter Foundation, Duke Bio‐medical Engineering. Company is being created around the detector technology. Duke holds patents on the technology.
WE‐D‐BRB‐01: Basic Physics of Proton Therapy
Authors: Arjomandy B.
Published: July 2016

The goal of this session is to review the physics of proton therapy, treatment planning techniques, and the use of volumetric imaging 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. It introduces the audience to the current status of image guided proton therapy and clinical applications of CBCT for proton therapy. It outlines the importance of volumetric imaging in proton therapy.

Learning Objectives:

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

The current state of volumetric image guidance equipment in proton therapy.

Clinical applications of CBCT and its advantage over orthogonal imaging for proton therapy.

B. Teo, B.K Teo had received travel funds from IBA in 2015.
WE‐D‐BRB‐02: Proton Treatment Planning and Beam Optimization
Authors: Pankuch M.
Published: July 2016

The goal of this session is to review the physics of proton therapy, treatment planning techniques, and the use of volumetric imaging 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. It introduces the audience to the current status of image guided proton therapy and clinical applications of CBCT for proton therapy. It outlines the importance of volumetric imaging in proton therapy.

Learning Objectives:

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

The current state of volumetric image guidance equipment in proton therapy.

Clinical applications of CBCT and its advantage over orthogonal imaging for proton therapy.

B. Teo, B.K Teo had received travel funds from IBA in 2015.
WE‐DE‐BRC‐00: Learning the New Approaches of TG‐100 and Beyond
Authors: Dunscombe Peter
Published: July 2016

This SAMs Therapy Educational Course will introduce participants to the tools recommended by TG 100 for use in the development of a risk‐based Quality Management Program. The Course will start with an Overview of the background and rationale behind the TG 100 initiative, from which its charge was developed. After setting the scene, four 15 minute presentations will introduce the principal components of the soon to be published Report of TG 100. These are Process Mapping, Failure Modes and Effects Analysis, Fault Tree Analysis and the development of a QA/QM risk‐based Program. There will be time for two short exercises based on Failure Modes and Effects Analysis and a Discussion before the SAMs questions which will conclude the session.

Learning Objectives:

To appreciate the underlying philosophy of the TG 100 initiative.

To gain a brief overview of the principal risk‐based tools recommended by TG 100.

In an informal workshop format, to explore, at an introductory level, the use of risk analysis as proposed by TG 100 as a prelude to the development of a Quality Management Program.
WE‐DE‐BRC‐03: Failure Mode and Effect Analysis
Authors: Dunscombe P.
Published: July 2016

This SAMs Therapy Educational Course will introduce participants to the tools recommended by TG 100 for use in the development of a risk‐based Quality Management Program. The Course will start with an Overview of the background and rationale behind the TG 100 initiative, from which its charge was developed. After setting the scene, four 15 minute presentations will introduce the principal components of the soon to be published Report of TG 100. These are Process Mapping, Failure Modes and Effects Analysis, Fault Tree Analysis and the development of a QA/QM risk‐based Program. There will be time for two short exercises based on Failure Modes and Effects Analysis and a Discussion before the SAMs questions which will conclude the session.

Learning Objectives:

To appreciate the underlying philosophy of the TG 100 initiative.

To gain a brief overview of the principal risk‐based tools recommended by TG 100.

In an informal workshop format, to explore, at an introductory level, the use of risk analysis as proposed by TG 100 as a prelude to the development of a Quality Management Program.
WE‐DE‐BRA‐11: A Study of Motion Tracking Accuracy of Robotic Radiosurgery Using a Novel CCD Camera Based End‐To‐End Test System
Authors: Wang L , Nelson B , M Yang Y
Published: July 2016

Purpose:

A novel end‐to‐end test system using a CCD camera and a scintillator based phantom (XRV‐124, Logos Systems Int'l) capable of measuring the beam‐by‐beam delivery accuracy of Robotic Radiosurgery (CyberKnife) was developed and reported in our previous work. This work investigates its application in assessing the motion tracking (Synchrony) accuracy for CyberKnife.

Methods:

A QA plan with Anterior and Lateral beams (with 4 different collimator sizes) was created (Multiplan v5.3) for the XRV‐124 phantom. The phantom was placed on a motion platform (superior and inferior movement), and the plans were delivered on the CyberKnife M6 system using four motion patterns: static, Sine‐ wave, Sine with 15° phase shift, and a patient breathing pattern composed of 2cm maximum motion with 4 second breathing cycle. Under integral recording mode, the time‐averaged beam vectors (X, Y, Z) were measured by the phantom and compared with static delivery. In dynamic recording mode, the beam spots were recorded at a rate of 10 frames/second. The beam vector deviation from average position was evaluated against the various breathing patterns.

Results:

The average beam position of the six deliveries with no motion and three deliveries with Synchrony tracking on ideal motion (sinewave without phase shift) all agree within −0.03±0.00 mm, 0.10±0.04, and 0.04±0.03 in the X, Y, and X directions. Radiation beam width (FWHM) variations are within ±0.03 mm. Dynamic video record showed submillimeter tracking stability for both regular and irregular breathing pattern; however the tracking error up to 3.5 mm was observed when a 15 degree phase shift was introduced.

Conclusion:

The XRV‐124 system is able to provide 3D and 4D targeting accuracy for CyberKnife delivery with Synchrony. The experimental results showed sub‐millimeter delivery in phantom with excellent correlation in target to breathing motion. The accuracy was degraded when irregular motion and phase shift was introduced.
WE‐DE‐206‐01: MRI Signal in Biological Tissues ‐ Proton, Spin, T1, T2, T2*
Authors: Gorny K.
Published: July 2016

Magnetic resonance imaging (MRI) has become an essential part of clinical imaging due to its ability to render high soft tissue contrast. Instead of ionizing radiation, MRI use strong magnetic field, radio frequency waves and field gradients to create diagnostic useful images. It can be used to image the anatomy and also functional and physiological activities within the human body. Knowledge of the basic physical principles underlying MRI acquisition is vitally important to successful image production and proper image interpretation.

This lecture will give an overview of the spin physics, imaging principle of MRI, the hardware of the MRI scanner, and various pulse sequences and their applications. It aims to provide a conceptual foundation to understand the image formation process of a clinical MRI scanner.

Learning Objectives:

Understand the origin of the MR signal and contrast from the spin physics level.

Understand the main hardware components of a MRI scanner and their purposes

Understand steps for MR image formation including spatial encoding and image reconstruction

Understand the main kinds of MR pulse sequences and their characteristics.
WE‐FG‐207B‐04: Noise Suppression for Energy‐Resolved CT Via Variance Weighted Non‐Local Filtration
Authors: Harms J , Zhu L
Published: July 2016

Purpose:

The photon starvation problem is exacerbated in energy‐resolved CT, since the detected photons are shared by multiple energy channels. Using pixel similarity‐based non‐local filtration, we aim to produce accurate and high‐resolution energy‐resolved CT images with significantly reduced noise.

Methods:

Averaging CT images reconstructed from different energy channels reduces noise at the price of losing spectral information, while conventional denoising techniques inevitably degrade image resolution. Inspired by the fact that CT images of the same object at different energies share the same structures, we aim to reduce noise of energy‐resolved CT by averaging only pixels of similar materials ‐ a non‐local filtration technique. For each CT image, an empirical exponential model is used to calculate the material similarity between two pixels based on their CT values and the similarity values are organized in a matrix form. A final similarity matrix is generated by averaging these similarity matrices, with weights inversely proportional to the estimated total noise variance in the sinogram of different energy channels. Noise suppression is achieved for each energy channel via multiplying the image vector by the similarity matrix.

Results:

Multiple scans on a tabletop CT system are used to simulate 6‐channel energy‐resolved CT, with energies ranging from 75 to 125 kVp. On a low‐dose acquisition at 15 mA of the Catphan©600 phantom, our method achieves the same image spatial resolution as a high‐dose scan at 80 mA with a noise standard deviation (STD) lower by a factor of >2. Compared with another non‐local noise suppression algorithm (ndiNLM), the proposed algorithms obtains images with substantially improved resolution at the same level of noise reduction.

Conclusion:

We propose a noise‐suppression method for energy‐resolved CT. Our method takes full advantage of the additional structural information provided by energy‐resolved CT and preserves image values at each energy level.

Research reported in this publication was supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB019597. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
WE‐G‐204‐02: So You Want to Do Research: Two Approaches to Beginning a Career in Medical Physics Research
Authors: Deasy J.
Published: July 2016

While many indicators for academic medical physics are distressing – jobs are tight, demands on clinical time are high (and getting worse) and national funding has been flat for several years (meaning less money in reality) the present is perhaps one of the most exciting times in cancer research history, and medical physicists have an opportunity to make a difference.

Many of us predict the impact of medical physics on cancer research over the next decade to be more significant than ever. Why is that? First, medical imaging is used for every cancer patient in developed countries. Every improvement in the acquisition, processing or analysis of radiological images has the potential to impact patients. The use of radiation therapy is at an all‐time high – and virtually cannot be performed without medical physics. Many of the advances in both biomedical imaging and radiation oncology are the result of the hard work of academic medical physicists who are thinking of the next generation of technologies that will be used against cancer or an even broader spectrum of diseases.

A career in academic medical physics is demanding, particularly for those with clinical responsibilities. As the demands for justification of their clinical effort become increasingly metricized, the ability to do “unfunded research” will become even more difficult. This means that many will have to generate external salary support to justify their efforts in research and development. This comes at a time when funding for research is compressed and harder to obtain. Generally speaking, if you are not contributing 50% or more of your effort to research, you are competing at a disadvantage and it is very unlikely you will get an NIH/NCI/NIBIB grant.

Furthermore, in the ongoing effort to improve patient care and safety, we have developed credentialing pathways that now require at least two‐years of residency training. This full‐time clinical training creates a gap in the research trajectory of graduate students who aspire to academic positions with an expectation for extramural funding. To address this, several residency programs have created hybrid programs where the two‐years of clinical training is combined with one or two years of research effort to allow candidates to further establish an academic identity and to ensure adequate academic productivity to compete for a beginning faculty position.

In conclusion, while the path to a successful career in academic medical physics is steep and sometimes hard to follow, reaching the apex is worth the journey. Different paths to a career in medical physics are available, you just have to decide which one is right for you. If improving cancer care is your goal as a physicist, then academic medical physics is the job for you!
WE‐H‐BRC‐05: Catastrophic Error Metrics for Radiation Therapy
Authors: Murphy S , Molloy J
Published: July 2016

Purpose:

Intuitive evaluation of complex radiotherapy treatments is impractical, while data transfer anomalies create the potential for catastrophic treatment delivery errors. Contrary to prevailing wisdom, logical scrutiny can be applied to patient‐specific machine settings. Such tests can be automated, applied at the point of treatment delivery and can be dissociated from prior states of the treatment plan, potentially revealing errors introduced early in the process.

Methods:

Analytical metrics were formulated for conventional and intensity modulated RT (IMRT) treatments. These were designed to assess consistency between monitor unit settings, wedge values, prescription dose and leaf positioning (IMRT). Institutional metric averages for 218 clinical plans were stratified over multiple anatomical sites. Treatment delivery errors were simulated using a commercial treatment planning system and metric behavior assessed via receiver‐operator‐characteristic (ROC) analysis. A positive result was returned if the erred plan metric value exceeded a given number of standard deviations, e.g. 2. The finding was declared true positive if the dosimetric impact exceeded 25%. ROC curves were generated over a range of metric standard deviations.

Results:

Data for the conventional treatment metric indicated standard deviations of 3%, 12%, 11%, 8%, and 5 % for brain, pelvis, abdomen, lung and breast sites, respectively. Optimum error declaration thresholds yielded true positive rates (TPR) between 0.7 and 1, and false positive rates (FPR) between 0 and 0.2. Two proposed IMRT metrics possessed standard deviations of 23% and 37%. The superior metric returned TPR and FPR of 0.7 and 0.2, respectively, when both leaf position and MUs were modelled. Isolation to only leaf position errors yielded TPR and FPR values of 0.9 and 0.1.

Conclusion:

Logical tests can reveal treatment delivery errors and prevent large, catastrophic errors. Analytical metrics are able to identify errors in monitor units, wedging and leaf positions with favorable sensitivity and specificity.

In part by Varian
WE‐H‐BRA‐03: Development of a Model to Include the Evolution of Resistant Tumor Subpopulations Into the Treatment Optimization Process for Schedules Involving Targeted Agents in Chemoradiation Therapy
Authors: Grassberger C , Paganetti H
Published: July 2016

Purpose:

To develop a model that includes the process of resistance development into the treatment optimization process for schedules that include targeted therapies. Further, to validate the approach using clinical data and to apply the model to assess the optimal induction period with targeted agents before curative treatment with chemo‐radiation in stage III lung cancer.

Methods:

Growth of the tumor and its subpopulations is modeled by Gompertzian growth dynamics, resistance induction as a stochastic process. Chemotherapy induced cell kill is modeled by log‐cell kill dynamics, targeted agents similarly but restricted to the sensitive population. Radiation induced cell kill is assumed to follow the linear‐quadratic model. The validation patient data consist of a cohort of lung cancer patients treated with tyrosine kinase inhibitors that had longitudinal imaging data available.

Results:

The resistance induction model was successfully validated using clinical trial data from 49 patients treated with targeted agents. The observed recurrence kinetics, with tumors progressing from 1.4–63 months, result in tumor growth equaling a median volume doubling time of 92 days [34–248] and a median fraction of pre‐existing resistance of 0.035 [0–0.22], in agreement with previous clinical studies. The model revealed widely varying optimal time points for the use of curative therapy, reaching from ∼1m to >6m depending on the patient's growth rate and amount of pre‐existing resistance. This demonstrates the importance of patient‐specific treatment schedules when targeted agents are incorporated into the treatment.

Conclusion:

We developed a model including evolutionary dynamics of resistant sub‐populations with traditional chemotherapy and radiation cell kill models. Fitting to clinical data yielded patient specific growth rates and resistant fraction in agreement with previous studies. Further application of the model demonstrated how proper timing of chemo‐radiation could minimize the probability of resistance, increasing tumor control significantly.