bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐C‐207A‐05: Feature Based Water Equivalent Path Length (WEPL) Determination for Proton Radiography by the Technique of Time Resolved Dose Measurement
Authors: Zhang R , Jee K , Sharp G , Flanz J , Lu H
Published: July 2016

Purpose:

Studies show that WEPL can be determined from modulated dose rate functions (DRF). However, the previous calibration method based on statistics of the DRF is sensitive to energy mixing of protons due to scattering through different materials (termed as range mixing here), causing inaccuracies in the determination of WEPL. This study intends to explore time‐domain features of the DRF to reduce the effect of range mixing in proton radiography (pRG) by this technique.

Methods:

An amorphous silicon flat panel (PaxScan™ 4030CB, Varian Medical Systems, Inc., Palo Alto, CA) was placed behind phantoms to measure DRFs from a proton beam modulated by a specially designed modulator wheel. The performance of two methods, the previously used method based on the root mean square (RMS) and the new approach based on time‐domain features of the DRF, are compared for retrieving WEPL and RSP from pRG of a Gammex phantom.

Results:

Calibration by T80 (the time point for 80% of the major peak) was more robust to range mixing and produced WEPL with improved accuracy. The error of RSP was reduced from 8.2% to 1.7% for lung equivalent material, with the mean error for all other materials reduced from 1.2% to 0.7%. The mean error of the full width at half maximum (FWHM) of retrieved inserts was decreased from 25.85% to 5.89% for the RMS and T80 method respectively. Monte Carlo simulations in simplified cases also demonstrated that the T80 method is less sensitive to range mixing than the RMS method.

Conclusion:

WEPL images have been retrieved based on single flat panel measured DRFs, with inaccuracies reduced by exploiting time‐domain features as the calibration parameter. The T80 method is validated to be less sensitive to range mixing and can thus retrieve the WEPL values in proximity of interfaces with improved numerical and spatial accuracy for proton radiography.
SU‐C‐207A‐07: Cumulative 18F‐FDG Uptake Histogram Relative to Radiation Dose Volume Histogram of Lung After IMRT Or PSPT and Their Association with Radiation Pneumonitis
Authors: Shusharina N , Liao Z , Mohan R , Choi N , Bortfeld T
Published: July 2016

Purpose:

To determine whether the difference in cumulative 18F‐FDG uptake histogram of lung treated with either IMRT or PSPT is associated with radiation pneumonitis (RP) in patients with inoperable stage II and III NSCLC.

Methods:

We analyzed 24 patients from a prospective randomized trial to compare IMRT (n=12) with vs. PSPT (n=12) for inoperable NSCLC. All patients underwent PET‐CT imaging between 35 and 88 days post‐therapy. Post‐treatment PET‐CT was aligned with planning 4D CT to establish a voxel‐to‐voxel correspondence between post‐treatment PET and planning dose images. 18F‐FDG uptake as a function of radiation dose to normal lung was obtained for each patient. Distribution of the standard uptake value (SUV) was analyzed using a volume histogram method. The image quantitative characteristics and DVH measures were correlated with clinical symptoms of pneumonitis.

Results:

Patients with RP were present in both groups: 5 in the IMRT and 6 in the PSPT. The analysis of cumulative SUV histograms showed significantly higher relative volumes of the normal lung having higher SUV uptake in the PSPT patients for both symptomatic and asymptomatic cases (VSUV=2: 10% for IMRT vs 16% for proton RT and VSUV=1: 10% for IMRT vs 23% for proton RT). In addition, the SUV histograms for symptomatic cases in PSPT patients exhibited a significantly longer tail at the highest SUV. The absolute volume of the lung receiving the dose >70 Gy was larger in the PSPT patients.

Conclusion:

18F‐FDG uptake – radiation dose response correlates with RP in both groups of patients by means of the linear regression slope. SUV is higher for the PSPT patients for both symptomatic and asymptomatic cases. Higher uptake after PSPT patients is explained by larger volumes of the lung receiving high radiation dose.
SU‐C‐206‐02: Estimating Coronary Artery Plaque Composition with a Combined Dual‐Energy and Single‐Energy QCT Optimization Model
Authors: Goodsitt M , Chan H , Watcharotone K , Nan B , Christodoulou E
Published: July 2016

Purpose:

A study was performed to evaluate the accuracy of a combined dual‐energy/single‐energy QCT optimization model for estimating the volume fractions of fat, cholesterol, protein, fibrous tissue, and calcium in coronary artery plaque.

Methods:

This model uses 80kVp‐140kVp CT# data to distinguish calcium, and Gaussian component analysis based on the 140kVp CT#'s of the pure materials to distinguish the other constituents. An experimental study was performed with a thorax section phantom (CIRS, Inc.) containing various mixtures of 0.8–3.2mm thick plastic slabs simulating plaque components. The Z(eff), ρ(g/cc) properties of the plaque components and plaque‐simulating plastics are: [fat(5.82,0.92), LDPE(5.44, 0.92)], [cholesterol(5.65,1.067), polystyrene(5.62,1.05)], protein(6.73,1.38), Delrin (7.07,1.42)], fibrous(7.51, 1.05), nylon(6.21, 1.14]). Two water‐equivalent plastics containing CaCO3 (13% and 22% CaCO3) were also used. Scans of 6.4‐mm‐thick slabs of each plastic alone within the phantom were acquired to determine the CT#'s of the pure materials. All scans were repeated 3 times under each condition and reconstructed with standard (STD) and BONE kernels on a GE CT750HD CT scanner using a 10‐cm reconstruction FOV.

Results:

The minimum RMS errors for the various plaque‐simulating slab mixtures were 2.2% (BONE kernel) and 4.2% (STD kernel) for a 1.6mm 13% CaCO3, 3.2mm polystyrene, 3.2mm delrin, 3.2mm nylon slab mixture (total=11.2mm). Maximum RMS errors were 7.5% (BONE) and 13.7% (STD) for a mixture containing 1.6mm slabs of 13% CaCO3, LDPE, delrin, and nylon (total=6.4mm).

Conclusion:

The accuracy of the method decreased as the thicknesses of the slabs decreased, due to volume averaging. Accuracy was better for the BONE kernel that has higher spatial resolution. Even for this kernel, the errors are substantial for a mixture that is about the size of the coronary artery lumen (2–6mm). Higher spatial resolution and more quantitative reconstruction methods are needed to improve the accuracy of the method.

NIH RO1 HL106545
SU‐C‐206‐06: Shift‐Invariant Projection and Backprojection for Helical CT Based On A Self‐Consistent Coordinate
Authors: Guo M , Gao H
Published: July 2016

Purpose:

Despite its promising utility, iterative CT image reconstruction is often time‐consuming, especially for helical CT, for which the X‐ray projections and backprojections are computationally dominant. To overcome the insufficient memory storage for projection and backprojection matrices, they are usually computed on‐the‐fly with parallel implementation via GPU. In this work, we develop an efficient method to compute projections and backprojections without repeated on‐the‐fly computations for helical CT.

Methods:

We design the shift‐invariant projections and backprojections based on a self‐consistent coordinate, so that the size of projection and backprojection matrices can be significantly reduced in order to be practically stored on GPU. For reconstruction accuracy, the projections and backprojections are based on two different strategies, i.e., ray‐driven and pixel‐driven algorithm respectively. Moreover the proposed method can be readily incorporated into standard iterative reconstruction algorithm. Rigorous mathematical analysis is carried out to establish the shift‐invariance for ray‐driven projections and pixel‐driven backprojections.

Results:

Numerical results using GPU parallelization suggest that the proposed shift‐invariant method has much improved computational efficiency from the conventional on‐the‐fly method, i.e.,by 3–6 fold acceleration for the projection and 3–16 fold acceleration for the backprojection using standard configuration of helical CT.

Conclusion:

We have proposed a shift‐invariant projection and backprojection method based on a self‐consistent coordinate so that the projection and backprojection matrices can be stored instead of being repeatedly computed on‐the‐fly. The proposed method can be conveniently utilized in standard iterative reconstruction method with minimal modification. Numerical results demonstrate that the proposed method can speed up the projection by 3 to 6 folds and the backprojection by 3 to 16 folds using standard configuration of helical CT.

The authors were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).
SU‐C‐206‐07: A Practical Sparse View Ultra‐Low Dose CT Acquisition Scheme for PET Attenuation Correction in the Extended Scan Field‐Of‐View
Authors: Miao J , Fan J , Gopinatha Pillai A
Published: July 2016

Purpose:

To further reduce CT dose, a practical sparse‐view acquisition scheme is proposed to provide the same attenuation estimation as higher dose for PET imaging in the extended scan field‐of‐view.

Methods:

CT scans are often used for PET attenuation correction and can be acquired at very low CT radiation dose. Low dose techniques often employ low tube voltage/current accompanied with a smooth filter before backprojection to reduce CT image noise. These techniques can introduce bias in the conversion from HU to attenuation values, especially in the extended CT scan field‐of‐view (FOV). In this work, we propose an ultra‐low dose CT technique for PET attenuation correction based on sparse‐view acquisition. That is, instead of an acquisition of full amount of views, only a fraction of views are acquired. We tested this technique on a 64‐slice GE CT scanner using multiple phantoms. CT scan FOV truncation completion was performed based on the published water‐cylinder extrapolation algorithm. A number of continuous views per rotation: 984 (full), 246, 123, 82 and 62 have been tested, corresponding to a CT dose reduction of none, 4x, 8x, 12x and 16x. We also simulated sparse‐view acquisition by skipping views from the fully‐acquired view data.

Results:

FBP reconstruction with Q. AC filter on reduced views in the full extended scan field‐of‐view possesses similar image quality to the reconstruction on acquired full view data. The results showed a further potential for dose reduction compared to the full acquisition, without sacrificing any significant attenuation support to the PET.

Conclusion:

With the proposed sparse‐view method, one can potential achieve at least 2x more CT dose reduction compared to the current Ultra‐Low Dose (ULD) PET/CT protocol. A pre‐scan based dose modulation scheme can be combined with the above sparse‐view approaches, which can even further reduce the CT scan dose during a PET/CT exam.
SU‐C‐209‐07: Phantoms for Digital Breast Tomosynthesis Imaging System Evaluation
Authors: Jacobson D , Liu Y
Published: July 2016

Purpose:

Digital Breast Tomosynthesis (DBT) is gaining importance in breast imaging. There is a need for phantoms that can be used for image evaluation and comparison. Existing commercially available phantoms for DBT are expensive and may lack clinically relevant test objects. The purpose of this study is to develop phantoms for DBT evaluation.

Methods

Four phantoms have been designed and constructed to assess the image quality (IQ) of two DBT systems. The first contains a spiral of 0.3 mm SiC beads in gelatin to measure the tomographic slice thickness profile and uniformity of coverage in a series of tomographic planes. The second contains simulated tumors inclined with respect to the phantom base to assess tomographic image quality. The third has a tilted array of discs with varying contrast and diameter. This phantom was imaged alone and in a stack of TE slabs giving 2 to 10 cm thickness. The fourth has a dual wedge of glandular and adipose simulating materials. One wedge contains discs with varying diameter and thickness; the other supports a mass with six simulated spicules of varying size and a cluster of simulated calcifications. The simulated glandular tissue material varies between 35 and 100% of the total thickness (5.5 cm).

Results:

All phantoms were scanned successfully. The best IQ comparison was achieved with the dual wedge phantom as demonstrated by the spiculated mass and calcifications. Images were evaluated by two radiologists and one physicist. The projection images and corresponding set of tomographic planes were comparable and the synthesized projection images were inferior to the projection images for both systems.

Conclusion:

Four phantoms were designed, constructed and imaged on two DBT systems. They successfully demonstrated performance differences between two systems, and between true and synthesized projection images. Future work will incorporate these designs into a single phantom.
SU‐C‐207B‐02: Maximal Noise Reduction Filter with Anatomical Structures Preservation
Authors: Maitree R , Guzman G , Chundury A , Roach M , Yang D
Published: July 2016

Purpose:

All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post‐processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically.

Methods:

To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist.

Results:

Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages.

Conclusion:

According to both clinical evaluation (human expert ranking) and qualitative assessment, the proposed approach has superior noise reduction and anatomical structures preservation capabilities over existing noise removal methods.

Senior Author Dr. Deshan Yang received research funding form ViewRay and Varian.
SU‐C‐207B‐07: Deep Convolutional Neural Network Image Matching for Ultrasound Guidance in Radiotherapy
Authors: Zhu N , Najafi M , Hancock S , Hristov D
Published: July 2016

Purpose:

Robust matching of ultrasound images is a challenging problem as images of the same anatomy often present non‐trivial differences. This poses an obstacle for ultrasound guidance in radiotherapy. Thus our objective is to overcome this obstacle by designing and evaluating an image blocks matching framework based on a two channel deep convolutional neural network.

Methods:

We extend to 3D an algorithmic structure previously introduced for 2D image feature learning [1]. To obtain the similarity between two 3D image blocks A and B, the 3D image blocks are divided into 2D patches Ai and Bi. The similarity is then calculated as the average similarity score of Ai and Bi. The neural network was then trained with public non‐medical image pairs, and subsequently evaluated on ultrasound image blocks for the following scenarios: (S1) same image blocks with/without shifts (A and A_shift_x); (S2) non‐related random block pairs; (S3) ground truth registration matched pairs of different ultrasound images with/without shifts (A_i and A_reg_i_shift_x).

Results:

For S1 the similarity scores of A and A_shift_x were 32.63, 18.38, 12.95, 9.23, 2.15 and 0.43 for x=ranging from 0 mm to 10 mm in 2 mm increments. For S2 the average similarity score for non‐related block pairs was −1.15. For S3 the average similarity score of ground truth registration matched blocks A_i and A_reg_i_shift_0 (1≤i≤5) was 12.37. After translating A_reg_i_shift_0 by 0 mm, 2 mm, 4 mm, 6 mm, 8 mm, and 10 mm, the average similarity scores of A_i and A_reg_i_shift_x were 11.04, 8.42, 4.56, 2.27, and 0.29 respectively.

Conclusion:

The proposed method correctly assigns highest similarity to corresponding 3D ultrasound image blocks despite differences in image content and thus can form the basis for ultrasound image registration and tracking.[1] Zagoruyko, Komodakis, “Learning to compare image patches via convolutional neural networks”, IEEE CVPR 2015,pp.4353–4361.
SU‐D‐204‐02: BED Consistent Extrapolation of Mean Dose Tolerances
Authors: Perko Z , Bortfeld T , Hong T , Wolfgang J , Unkelbach J
Published: July 2016

Purpose:

The safe use of radiotherapy requires the knowledge of tolerable organ doses. For experimental fractionation schemes (e.g. hypofractionation) these are typically extrapolated from traditional fractionation schedules using the Biologically Effective Dose (BED) model. This work demonstrates that using the mean dose in the standard BED equation may overestimate tolerances, potentially leading to unsafe treatments. Instead, extrapolation of mean dose tolerances should take the spatial dose distribution into account.

Methods:

A formula has been derived to extrapolate mean physical dose constraints such that they are mean BED equivalent. This formula constitutes a modified BED equation where the influence of the spatial dose distribution is summarized in a single parameter, the dose shape factor. To quantify effects we analyzed 14 liver cancer patients previously treated with proton therapy in 5 or 15 fractions, for whom also photon IMRT plans were available.

Results:

Our work has two main implications. First, in typical clinical plans the dose distribution can have significant effects. When mean dose tolerances are extrapolated from standard fractionation towards hypofractionation they can be overestimated by 10–15%. Second, the shape difference between photon and proton dose distributions can cause 30–40% differences in mean physical dose for plans having the same mean BED. The combined effect when extrapolating proton doses to mean BED equivalent photon doses in traditional 35 fraction regimens resulted in up to 7–8 Gy higher doses than when applying the standard BED formula. This can potentially lead to unsafe treatments (in 1 of the 14 analyzed plans the liver mean dose was above its 32 Gy tolerance).

Conclusion:

The shape effect should be accounted for to avoid unsafe overestimation of mean dose tolerances, particularly when estimating constraints for hypofractionated regimens. In addition, tolerances established for a given treatment modality cannot necessarily be applied to other modalities with drastically different dose distributions.
SU‐D‐204‐06: Integration of Machine Learning and Bioinformatics Methods to Analyze Genome‐Wide Association Study Data for Rectal Bleeding and Erectile Dysfunction Following Radiotherapy in Prostate Cancer
Authors: Oh J , Kerns S , Ostrer H , Rosenstein B , Deasy J
Published: July 2016

Purpose:

We investigated whether integration of machine learning and bioinformatics techniques on genome‐wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation‐induced late rectal bleeding and erectile dysfunction in prostate cancer patients.

Methods:

We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning‐based predictive model of late radiation‐induced toxicities: rectal bleeding and erectile dysfunction. The model building process was performed using 2/3 of samples (training) and the predictive model was tested with 1/3 of samples (validation). To identify important single nucleotide polymorphisms (SNPs), we computed the SNP importance score, resulting from our random forest regression model. We performed gene ontology (GO) enrichment analysis for nearby genes of the important SNPs.

Results:

After univariate analysis on the training dataset, we filtered out many SNPs with p>0.001, resulting in 749 and 367 SNPs that were used in the model building process for rectal bleeding and erectile dysfunction, respectively. On the validation dataset, our random forest regression model achieved the area under the curve (AUC)=0.70 and 0.62 for rectal bleeding and erectile dysfunction, respectively. We performed GO enrichment analysis for the top 25%, 50%, 75%, and 100% SNPs out of the select SNPs in the univariate analysis. When we used the top 50% SNPs, more plausible biological processes were obtained for both toxicities. An additional test with the top 50% SNPs improved predictive power with AUC=0.71 and 0.65 for rectal bleeding and erectile dysfunction. A better performance was achieved with AUC=0.67 when age and androgen deprivation therapy were added to the model for erectile dysfunction.

Conclusion:

Our approach that combines machine learning and bioinformatics techniques enabled designing better models and identifying more plausible biological processes associated with the outcomes.
SU‐D‐201‐02: A Systematic Planning Quality Monitoring Program for VMAT Treatment
Authors: Zeng G , Robertson M , Murphy J , Lamey M , Wang Y
Published: July 2016

Purpose:

To demonstrate a systematic tracking program for planning quality; to advocate that commercial treatment planning systems develop tools to monitor population‐based quality in order to justify technology change in respect to clinical outcome.

Methods:

The DVH (Dose Volume Histogram) files of VMAT patients since 2011 have been exported from Varian Eclipse treatment planning system. The nomenclature of structures has been standardized and the planning process has been semi‐automated for clinical sites where such efforts bring clinical and operational benefits, such as GU. An in‐house program was developed in C++ to extract the dose volume points of targets and organs at risk from the exported DVH files. The control points were determined by QUANTEC (Quantitative Analyses of Normal Tissue Effects in the Clinic) organ specific dose volume recommendations for toxicity control. Monitor units were used as the indication of treatment efficiency. Control charts for dose volume points and monitor units were created to monitor planning quality dynamically.

Results:

The figures in the supporting document show some examples of prostate 78Gy plans over the most recent 30 months. The contoured CTV, rectum and bladder are statistically stable at 74±27 cm3, 77±28cm3 and 366±165cm3, respectively, reflecting consistence in target delineation, bowel and bladder preparation. Rectum V50, V60, V65, V70 and V75 remain at 24±6 %, 18±5%, 16±5%, 13±4 % and 9±4%, respectively, well below QUANTEC recommendations of 50%, 30%, 25%, 20% and 15%. The stability is also observed in targets and other OARs’ doses. However, monitor units have increased by 11%, from 533±42MU to 594±40MU, after Eclipse version 11 upgrade in which the optimization algorithm was changed.

Conclusion:

Monitoring population‐wise quality provides an assessment of technology changes and clinical outcomes, which can help guide future development. We therefore recommend these types of tools be incorporated in commercial treatment planning systems.
SU‐D‐201‐03: During‐Treatment Delivery Monitoring System for TomoTherapy
Authors: Chen Q , Read P
Published: July 2016

Purpose:

Multiple error pathways can lead to delivery errors during the treatment course that cannot be caught with pre‐treatment QA. While in vivo solutions are being developed for linacs, no such solution exists for tomotherapy. The purpose of this study is to develop a near real‐time system for tomotherapy that can monitor the delivery and dose accumulation process during the treatment‐delivery, which enable the user to assess the impact of delivery variations and/or errors and to interrupt the treatment if necessary.

Methods:

A program running on a tomotherapy planning station fetches the raw DAS data during treatment. Exit detector data is extracted as well as output, gantry angle, and other machine parameters. For each sample, the MLC open‐close state is determined. The delivered plan is compared with the original plan via a Monte Carlo dose engine which transports fluence deviations from a pre‐treatment Monte Carlo run. A report containing the difference in fluence, dose and DVH statistics is created in html format. This process is repeated until the treatment is completed.

Results:

Since we only need to compute the dose for the difference in fluence for a few projections each time, dose with 2% statistical uncertainty can be computed in less than 1 second on a 4‐core cpu. However, the current bottleneck in this near real‐time system is the repeated fetching and processing the growing DAS data file throughout the delivery. The frame rate drops from 10Hz at the beginning of treatment to 5Hz after 3 minutes and to 2Hz after 10 minutes.

Conclusion:

A during‐treatment delivery monitor system has been built to monitor tomotherapy treatments. The system improves patient safety by allowing operators to assess the delivery variations and errors during treatment delivery and adopt appropriate actions.
SU‐D‐201‐05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework
Authors: Santhanam A , Min Y , Beron P , Agazaryan N , Kupelian P , Low D
Published: July 2016

Purpose:

Patient safety hazards such as a wrong patient/site getting treated can lead to catastrophic results. The purpose of this project is to automatically detect potential patient safety hazards during the radiotherapy setup and alert the therapist before the treatment is initiated.

Methods:

We employed a set of co‐located and co‐registered 3D cameras placed inside the treatment room. Each camera provided a point‐cloud of fraxels (fragment pixels with 3D depth information). Each of the cameras were calibrated using a custom‐built calibration target to provide 3D information with less than 2 mm error in the 500 mm neighborhood around the isocenter. To identify potential patient safety hazards, the treatment room components and the patient's body needed to be identified and tracked in real‐time. For feature recognition purposes, we used a graph‐cut based feature recognition with principal component analysis (PCA) based feature‐to‐object correlation to segment the objects in real‐time. Changes in the object's position were tracked using the CamShift algorithm. The 3D object information was then stored for each classified object (e.g. gantry, couch). A deep learning framework was then used to analyze all the classified objects in both 2D and 3D and was then used to fine‐tune a convolutional network for object recognition. The number of network layers were optimized to identify the tracked objects with >95% accuracy.

Results:

Our systematic analyses showed that, the system was effectively able to recognize wrong patient setups and wrong patient accessories. The combined usage of 2D camera information (color + depth) enabled a topology‐preserving approach to verify patient safety hazards in an automatic manner and even in scenarios where the depth information is partially available.

Conclusion:

By utilizing the 3D cameras inside the treatment room and a deep learning based image classification, potential patient safety hazards can be effectively avoided.
SU‐D‐201‐07: A Survey of Radiation Oncology Residents’ Training and Preparedness to Lead Patient Safety Programs in Clinics
Authors: Spraker M , Nyflot M , Ford E , Kane G , Zeng J , Hendrickson K
Published: July 2016

Purpose:

Safety and quality has garnered increased attention in radiation oncology, and physicians and physicists are ideal leaders of clinical patient safety programs. However, it is not clear whether residency programs incorporate formal patient safety training and adequately equip residents to assume this leadership role. A national survey was conducted to evaluate medical and physics residents’ exposure to safety topics and their confidence with the skills required to lead clinical safety programs.

Methods:

Radiation oncology residents were identified in collaboration with ARRO and AAPM. The survey was released in February 2016 via email using REDCap. This included questions about exposure to safety topics, confidence leading safety programs, and interest in training opportunities (i.e. workshops). Residents rated their exposure, skills, and confidence on 4 or 5‐point scales. Medical and physics residents responses were compared using chi‐square tests.

Results:

Responses were collected from 56 of 248 (22%) physics and 139 of 690 (20%) medical residents. More than two thirds of all residents had no or only informal exposure to incident learning systems (ILS), root cause analysis (RCA), failure mode and effects analysis (FMEA), and the concept of human factors engineering (HFE). Likewise, 63% of residents had not heard of RO‐ILS. Response distributions were similar, however more physics residents had formal exposure to FMEA (p<0.0001) and felt they were adequately trained to lead FMEAs in clinic (p<0.001) than medical residents. Only 36% of residents felt their patient safety training was adequate, and 58% felt more training would benefit their education.

Conclusion:

These results demonstrate that, despite increasing desire for patient safety training, medical and physics residents’ exposure to relevant concepts is low. Physics residents had more exposure to FMEA than medical residents, and were more confident in leading FMEA. This suggests that increasing resident exposure to specific topics may increase their confidence.
SU‐D‐BRC‐03: Development and Validation of an Online 2D Dose Verification System for Daily Patient Plan Delivery Accuracy Check
Authors: Zhao J , Hu W , Xing Y , Wu X , Li Y
Published: July 2016

Purpose:

All plan verification systems for particle therapy are designed to do plan verification before treatment. However, the actual dose distributions during patient treatment are not known. This study develops an online 2D dose verification tool to check the daily dose delivery accuracy.

Methods:

A Siemens particle treatment system with a modulated scanning spot beam is used in our center. In order to do online dose verification, we made a program to reconstruct the delivered 2D dose distributions based on the daily treatment log files and depth dose distributions. In the log files we can get the focus size, position and particle number for each spot. A gamma analysis is used to compare the reconstructed dose distributions with the dose distributions from the TPS to assess the daily dose delivery accuracy. To verify the dose reconstruction algorithm, we compared the reconstructed dose distributions to dose distributions measured using PTW 729XDR ion chamber matrix for 13 real patient plans. Then we analyzed 100 treatment beams (58 carbon and 42 proton) for prostate, lung, ACC, NPC and chordoma patients.

Results:

For algorithm verification, the gamma passing rate was 97.95% for the 3%/3mm and 92.36% for the 2%/2mm criteria. For patient treatment analysis,the results were 97.7%±1.1% and 91.7%±2.5% for carbon and 89.9%±4.8% and 79.7%±7.7% for proton using 3%/3mm and 2%/2mm criteria, respectively. The reason for the lower passing rate for the proton beam is that the focus size deviations were larger than for the carbon beam. The average focus size deviations were −14.27% and −6.73% for proton and −5.26% and −0.93% for carbon in the x and y direction respectively.

Conclusion:

The verification software meets our requirements to check for daily dose delivery discrepancies. Such tools can enhance the current treatment plan and delivery verification processes and improve safety of clinical treatments.
SU‐D‐BRB‐01: A Comparison of Learning Methods for Knowledge Based Dose Prediction for Coplanar and Non‐Coplanar Liver Radiotherapy
Authors: Tran A , Ruan D , Woods K , Yu V , Nguyen D , Sheng K
Published: July 2016

Purpose:

The predictive power of knowledge based planning (KBP) has considerable potential in the development of automated treatment planning. Here, we examine the predictive capabilities and accuracy of previously reported KBP methods, as well as an artificial neural networks (ANN) method. Furthermore, we compare the predictive accuracy of these methods on coplanar volumetric‐modulated arc therapy (VMAT) and non‐coplanar 4π radiotherapy.

Methods:

30 liver SBRT patients previously treated using coplanar VMAT were selected for this study. The patients were re‐planned using 4π radiotherapy, which involves 20 optimally selected non‐coplanar IMRT fields. ANNs were used to incorporate enhanced geometric information including liver and PTV size, prescription dose, patient girth, and proximity to beams. The performance of ANN was compared to three methods from statistical voxel dose learning (SVDL), wherein the doses of voxels sharing the same distance to the PTV are approximated by either taking the median of the distribution, non‐parametric fitting, or skew‐normal fitting. These three methods were shown to be capable of predicting DVH, but only median approximation can predict 3D dose. Prediction methods were tested using leave‐one‐out cross‐validation tests and evaluated using residual sum of squares (RSS) for DVH and 3D dose predictions.

Results:

DVH prediction using non‐parametric fitting had the lowest average RSS with 0.1176(4π) and 0.1633(VMAT), compared to 0.4879(4π) and 1.8744(VMAT) RSS for ANN. 3D dose prediction with median approximation had lower RSS with 12.02(4π) and 29.22(VMAT), compared to 27.95(4π) and 130.9(VMAT) for ANN.

Conclusion:

Paradoxically, although the ANNs included geometric features in addition to the distances to the PTV, it did not perform better in predicting DVH or 3D dose compared to simpler, faster methods based on the distances alone. The study further confirms that the prediction of 4π non‐coplanar plans were more accurate than VMAT.

NIH R43CA183390 and R01CA188300
SU‐D‐BRB‐02: Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality
Authors: Robertson SP , Moore JA , Hui X , DeWeese TL , Tran P , Quon H , Cheng Z , Bzdusek K , Kumar P , McNutt TR
Published: July 2016

Purpose:

Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality.

Methods:

Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan_A: traditional IMRT optimization using clinically relevant default objectives; Plan_B: traditional IMRT optimization using database dose predictions; Plan_C: autoplanning using default objectives; and Plan_D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volume (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%).

Results:

All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman's χ2 ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A‐D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A‐D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B‐D were significantly different from Plan_A for 3, 17, and 19 objectives, respectively. Plans C‐D were also significantly different from Plan_B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan_D provided significantly lower dose than plan C (p = 0.003).

Conclusion:

Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted in a larger cohort across HN, prostate, and other treatment sites.

This work is supported by Philips Radiation Oncology Systems.
SU‐D‐BRB‐07: Use of Knowledge‐Based Planning to Evaluate the Need for Motion Management in Lung SBRT
Authors: Lalonde R , Huq M , Heron D
Published: July 2016

Purpose:

The planning margins necessary to safely treat targets in the lung can be reduced depending upon the use of gating or abdominal compression to manage respiratory motion, but these may be uncomfortable for the patient, involve expensive equipment, or increase the length of each treatment fraction. The goal of this project is to use knowledge‐based‐planning (KBP) to evaluate which patients may benefit most from motion management.

Methods:

The Varian RapidPlan” KBP module was used to estimate doses for VMAT SBRT lung treatment plans with different planning margins corresponding to 1) standard lung setup (1.0 cm radial margin, 2.0 cm S‐I margin), 2) daily IGRT (0.7 cm radial 1.5 cm S‐I), 3) daily IGRT with motion management (0.5 cm radial, 1.0 cm S‐I). The estimated doses were then compared with treatment plan doses (AAA dose calculation) generated using the RapidPlan™ optimization constraints. Treatment plan doses were then compared against RTOG protocol dose limits for organs at risk.

Results:

The OAR doses were within or below the estimated range generated by the KBP system in 100% of plans. With standard margins, only 10% of the plans were able to meet all RTOG dose goals, compared with 75% of plans with IGRT margins. All plans were able to meet RTOG dose guidelines with motion management. The DVH estimation was able to correctly identify 100% of the plans in which one or more OAR doses exceeded RTOG guidelines. Also, the dose estimates correctly identified 88% of individual OAR doses exceeding RTOG guidelines.

Conclusion:

The KBP system was able to accurately predict the achievable doses in treatment plans, and predict patients in which RTOG dose guidelines would be exceeded. The system may be a useful tool in identifying patients where motion management may be necessary for SBRT treatment.

This work was supported by Varian Medical Systems.
SU‐D‐BRA‐02: Motion Assessment During Open Face Mask SRS Using CBCT and Surface Monitoring
Authors: Williams BB , Fox CJ , Hartford AC , Gladstone DJ
Published: July 2016

Purpose:

To assess the robustness of immobilization using open‐face mask technology for linac‐based stereotactic radiosurgery (SRS) with multiple non‐coplanar arcs via repeated CBCT acquisition, with comparison to contemporaneous optical surface tracking data.

Methods:

25 patients were treated in open faced masks with cranial SRS using 3–4 non‐coplanar arcs. Repeated CBCT imaging was performed to verify the maintenance of proper patient positioning during treatment. Initial patient positioning was performed based on prescribed shifts and optical surface tracking. Positioning refinements employed rigid 3D‐matching of the planning CT and CBCT images and were implemented via automated 6DOF couch control. CBCT imaging was repeated following the treatment of all non‐transverse beams with associated couch kicks. Detected patient translations and rotations were recorded and automatically corrected. Optical surface tracking was applied throughout the treatments to monitor motion, and this contemporaneous patient positioning data was recorded to compare against CBCT data and 6DOF couch adjustments.

Results:

Initial patient positions were refined on average by translations of 3±1mm and rotations of ±0.9‐degrees. Optical surface tracking corroborated couch corrections to within 1±1mm and ±0.4‐degrees. Following treatment of the transverse and subsequent superior‐oblique beam, average translations of 0.6±0.4mm and rotations of ±0.4‐degrees were reported via CBCT, with optical surface tracking in agreement to within 1.1±0.6mm and ±0.6‐degrees. Following treatment of the third beam, CBCT indicated additional translations of 0.4±0.2mm and rotations of ±0.3‐degrees. Cumulative couch corrections resulted in 0.7 ± 0.4mm average magnitude translations and rotations of ±0.4‐degrees.

Conclusion:

Based on CBCT measurements of patients during SRS, the open face mask maintained patient positioning to within 1.5mm and 1‐degree with >95% confidence. Patient positioning determined by optical surface tracking agreed with CBCT assessment to within 1±1mm and ±0.6‐degree rotations. These data support the use of 1–2mm PTV margins and repeated CBCT to maintain stereotactic positioning tolerances.
SU‐D‐BRA‐05: Time Series Analysis of EPID Images to Identify Patients in Need of Treatment Adaptation
Authors: Archambault L , Piron O , Varfalvy N
Published: July 2016

Purpose:

to evaluate if time series analysis of portal dose images can be used to identify patients undergoing important anatomical changes.

Methods:

daily EPID images of every treatment fields were acquired for 48 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis (weekly or biweekly). Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g. average gamma value, standard deviation, percent above 1). The gamma parameters formed a patient‐specific time series that was analyzed. The first 24 patients were retrospectively evaluated to establish an action threshold that was then applied on the remaining 24 patients. Dosimetric evaluation of patients above that threshold was performed to as assess the level of degradation compared to the initial treatment plan.

Results:

after performing a clinical retrospective analysis of the first 24 cases, an action threshold on the average gamma value was established at 0.6 and a warning level was set at 0.4. These thresholds were then applied to the remaining 24 cases. Of these, 6 patients (25%) were above the warning level and 4 (17%) were above the action threshold. Dosimetric evaluation was performed on the CBCT for all these 6 patients. Three of these patients had changes above 3% in their PTV coverage, one had changes of about 2% and the remaining two had negligible changes. Patients with the strongest changes all had clear trending in their gamma parameters that could be classified with techniques such as hidden Markov models.

Conclusion:

by using time series analysis of relative EPID image it was possible to identify a subset of patient most likely to benefit from treatment adaptation.

This work was funded in part by Varian Medical Systems
SU‐D‐BRA‐06: Duodenal Interfraction Motion with Abdominal Compression
Authors: Witztum A , Holyoake D , Warren S , Partridge M , Hawkins M
Published: July 2016

Purpose:

To quantify the effect of abdominal compression on duodenal motion during pancreatic radiotherapy.

Methods:

Seven patients treated for pancreatic cancer were selected for analysis. Four patients were treated with abdominal compression and three without. The duodenum was contoured by the same physician on each CBCT (five CBCTs for patients with compression, four for non‐compression patients). CBCTs were rigidly registered using a soft tissue match and contours were copied to the delivered plans which were all radical (BED > 50 Gy). The distance between the duodenum on the planning CT and each CBCT was quantified by calculating the root mean square (RMS) distance. The DVHs of each abdominal compression patient was converted to an EQD2 DVH (alpha/beta = 10) using an in‐house tool and volumes receiving at least 25, 35, 45, and 50 Gy were recorded.

Results:

The maximum variation in duodenal volumes on the CBCTs for the four abdominal compression patients were 19.1 cm3 (32.8%), 19.1 cm3 (20.6%), 19.9 cm3 (14.3%), and 12.9 cm3 (27.3%) compared to 15.2 cm3 (17.6%), 34.7 cm3 (83.4%), and 56 cm3 (60.2%) for non‐compression patients. The average RMS distance between the duodenum on the planning CT and each CBCT for all abdominal compression patients was 0.3 cm compared to 0.7 cm for non‐compressed patients. The largest (and average) difference between the planning CT and CBCTs in volume of duodenum receiving more than 25, 35, 45 and 50 Gy for abdominal compression patients was 11% (5%), 9% (3%), 9% (2%), and 6% (1%).

Conclusion:

Abdominal compression reduces variation in volume and absolute position of the duodenum throughout treatment. This is seen as an improvement but does not eliminate the need to consider dosimetric effects of motion. Abdominal compression is particularly useful in SBRT when only a few fractions are delivered.

Alon Witztum is supported by an MRC/Gray Institute DPhil Studentship. Daniel Holyoake is supported by a CRUK/Nuffield Clinical Research Fellowship. Sam Warren and Mike Partridge are supported by CRUK grant C5255/A15935. Maria Hawkins received an MRC Fellowship MC_PC_12001/2.
SU‐D‐202‐02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images
Authors: Lamichhane N , Johnson P , Chinea F , Patel V , Yang F
Published: July 2016

Purpose:

To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images

Methods:

A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray‐level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined.

Results:

Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001).

Conclusion:

The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.