bluuebunny/crossref_metadata_2025_split huggingface dataset:
TH‐CD‐207A‐10: Using the Gamma Index to Flag Changes in Anatomy During Radiation Therapy of Head and Neck Cancer
Authors: Schaly B , Kempe J , Mitchell S , Venkatesan V , Battista J 
Published: July 2016

Purpose:This article presents a fast algorithm for comparing 3‐D anatomy from Cone‐Beam CT (CBCT) imaging using the gamma comparison index and to demonstrate how this can be used to flag patients for possible re‐planning of treatment.Methods:CBCT scans acquired on a Varian linear accelerator during treatment were used as input to the gamma comparator using thresholds of 5 mm distance to agreement and 30 Hounsfield Unit CT number difference. The fraction 1 CBCT study was initially used as the reference. Should there be a re‐plan during treatment, the reference resets to the CBCT study acquired on the day 1 of the re‐plan. Histograms of failing pixels (γ > 1) were generated from each 3‐D gamma map. An indicator of anatomy congruence, the match quality parameter (MQP), was derived from failed pixel histograms using the 90th percentile gamma value. The MQP was plotted versus fraction number and related to actual repeat computed tomography (re‐CT) order dates as decided by a radiation oncologist. From this, decision criteria were derived for the algorithm to “trigger” re‐CT consideration and predictive power was scored using receiver‐operator characteristic (ROC) analysis.Results:The MQP plot generally showed that the on‐line match from CBCT image guidance deteriorated as the treatment progressed due to weight loss and tumor regression. The optimized MQP criteria for triggering re‐CT consideration demonstrated high sensitivity and specificity, consistent with actual re‐CT order dates within ± 3 fractions. Out of 20 patients that were actually re‐planned, the algorithm failed to trigger a re‐CT recommendation only twice and this was caused by CBCT ring artifacts.Conclusion:We have demonstrated that gamma comparisons can be used to evaluate CBCT‐acquired anatomy pairs and, from this, an algorithm can be “trained” to flag patients for possible re‐planning in a manner consistent with local radiation oncology practice.
TH‐CD‐206‐06: Regularized Composite Shape Prior Encoding Shape Relevance in Variational Image Segmentation
Authors: Zhao T , Ruan D 
Published: July 2016

Purpose:Variational image segmentation incorporating regularized composite shape prior (RCSP) based on a shape dictionary has demonstrated benefits in robustness and accuracy. However, it still has the risk of local minimum in a non‐convex optimization setting. This study aims to drive the variational segmentation towards global optimum by introducing hyper prior on the composite weights of RCSP, which encode the shape priors’ relevance to the specific segmentation task.Methods:In addition to using a RCSP to regularize the variational segmentation, where the RCSP is constructed by a linear combination of the shape dictionary, this study introduces a hyper prior on the linear weights of this shape composite. More specifically, geometric relevance of each shape in the dictionary to the unknown target segmentation is inferred from image based surrogate metrics. Such relevance value is used as hyper prior and imposed on the linear weights of the composite shapes. The resulted RCSP with hyper prior regularization is incorporated in a unified active contour optimization framework and a variational block‐descent algorithm is derived and implemented.Results:The performance was assessed on corpus callosum segmentation using a brain MR dataset, and compared with typical benchmark approaches. The resulted RCSP demonstrated proper composition of training data w.r.t. their individual geometric relevance. The accuracy of ultimate segmentation estimates yielded statistically significant improvement with mean and medium Dice similarity coefficient (DSC) of (.946, .963) compared to (.902, .919), (.932, .946), and (.944, .964) for ATLAS based scheme, Chan Vese active contour model and an existing RCSP model, respectively.Conclusion:This work has developed a hyper prior to encode shape priors’ geometric relevance for RCSP regularization in a variational segmentation framework, leading to superior segmentation over benchmark approaches.
TH‐CD‐209‐08: Quantification of the Interplay Effect in Proton Pencil Beam Scanning Treatment of Lung
Authors: Kang M , Huang S , Solberg T , Mayer R , Thomas A , Teo B , McDonough J , Simone C , Lin L 
Published: July 2016

Purpose:To quantify the dose degradation caused by the interplay effect based on a beam specific motion analysis in proton pencil beam scanning (PBS) treatment of lung tumorsMethods:PBS plans were optimized on average CT using a beam‐specific PTV method for 10 consecutive patients with locally advanced non‐small‐cell‐lung‐cancer (NSCLC) treated with proton therapy to 6660/180 cGy. End inhalation (CT0) and end exhalation (CT50) were selected as the two extreme scenarios to acquire the relative stopping power ratio difference (Δrsp) for a respiration cycle. The water equivalent difference (ΔWET) per radiological path was calculated from the surface of patient to the iCTV by integrating the Δrsp of each voxel. The motion magnitude of each voxel within the target follows a quasi‐Gaussian distribution. A motion index (MI (>5mm WET)), defined as the percentage of target voxels with an absolute integral ΔWET larger than 5 mm, was adopted as a metric to characterize interplay. To simulate the treatment process, 4D dose was calculated by accumulating the spot dose on the corresponding respiration phase to the reference phase CT50 by deformable image registration based on spot timing and patient breathing phase.Results:The study indicated that the magnitude of target underdose in a single fraction plan is proportional to the MI (p<0.001), with larger motion equating to greater dose degradation and standard deviations. The target homogeneity, minimum, maximum and mean dose in the 4D dose accumulations of 37 fractions varied as a function of MI.Conclusion:The MI quantification metric can predict the level of dose degradation in PBS lung cancer treatment, which potentially serves as a clinical decision tool to assess whether patients are suitable to receive PBS treatment.
TH‐CD‐209‐09: Quickly Identifying Good Candidates for Proton Therapy From Geometric Considerations
Authors: Hall D , Trofimov A , Winey B , Liebsch N , Paganetti H 
Published: July 2016

Purpose:We developed a knowledge‐based model that can predict the patient‐specific benefits of proton therapy based upon geometric considerations. The model could also aid patient selection in model‐based clinical trials or help justify clinical decisions to insurance companies.Methods:The knowledge‐based method trains a model upon existing proton treatment plans, exploiting correlations between dose and distance‐to‐target. Each OAR is split into concentric subvolumes surrounding the target volume, and a skew‐normal PDF is fit to the dose distribution found within each shell. The model learns from shared trends in how the best‐fit skew‐normal parameters depend upon distance‐to‐target. It can then predict feasible OAR DVHs for a new patient (without a proton plan) based upon their geometry. The expected benefits of proton therapy are assessed by comparing the predicted DVHs to those of an IMRT plan, using a metric such as the equivalent uniform dose (EUD).Results:A model was trained for clival chordoma, owing to its geometric complexity and the multitude of nearby OARs. The model was trained using 20 patients and validated with a further 20 patients, and considers several different OARs. The predicted EUD was in good agreement with that of the actual proton plan. The coefficient of determination (R‐squared) was 85% overall, 92% for cochleas, 80% for optic chiasm and 79% for spinal cord. The model exhibited no signs of bias or overfitting. When compared to an IMRT plan, the model could classify whether a patient will experience a gain or a loss with an accuracy between 75% and 95%, depending upon the OAR.Conclusion:We developed a model that can quickly and accurately predict the patient‐specific benefits of proton therapy in clival chordoma patients, though models could be trained for other tumor sites.This work is funded by National Cancer Institute grant U19 CA 021239.
TH‐CD‐207B‐02: An Automated Technique to Measure Spatial Resolution in Clinical CT Images: Application to Patient Data
Authors: Sanders J , Ding A , Samei E 
Published: July 2016

Purpose:To evaluate an automated technique for measuring spatial resolution across a database of clinical CT exams.Methods:A fully automated algorithm was developed to extract a CT resolution index (RI) analogous to the modulation transfer function from clinical CT images by measuring the edge‐spread function (ESF) across the patient's skin and converting the results into scalar values, frequency at 50% RI, f50. The program was previously validated against observer data. This algorithm was applied to a database of CT images from our hospital that were reconstructed with different reconstruction algorithms (filtered back‐projection and iterative) and reconstruction kernels (soft and hard). The results were analyzed in terms of mean and variability within reconstruction methods and kernel aspects of the protocols.Results:The automated algorithm successfully measured the RI index from all of the clinical datasets examined. The measured f50 values increased with harder kernels for both FBP and iterative reconstruction. The mean f50 was 0.30 ± 0.02 mm‐1 and 0.42 ± 0.03 mm‐1 for images reconstructed with soft and hard kernels, respectively, using filtered back‐projection. The corresponding values for iterative reconstructions were 0.34 ± 0.02 mm‐1 and 0.39 ± 0.03 mm‐1, respectively. Overall, there was more variability in the f50 measurements made on datasets reconstructed with a hard kernel. The differences were statistically significant (p<0.05).Conclusion:Clinically‐informed, patient‐specific spatial resolution can be measured from clinical datasets. The method is sufficiently sensitive to reflect changes in spatial resolution due to different reconstruction parameters. The method can be applied to automatically assess the spatial resolution of patient images and quantify dependencies that may not be captured in phantom data.
TH‐CD‐207B‐03: How to Quantify Temporal Resolution in X‐Ray MDCT Imaging?
Authors: Budde A , Li Y , Hsieh J , Chen G 
Published: July 2016

Purpose:In modern CT scanners, a quantitative metric to assess temporal response, namely, to quantify the temporal resolution (TR), remains elusive. Rough surrogate metrics, such as half of the gantry rotation time for single source CT, a quarter of the gantry rotation time for dual source CT, or measurements of motion artifact's size, shape, or intensity have previously been used. In this work, a rigorous framework which quantifies TR and a practical measurement method are developed.Methods:A motion phantom was simulated which consisted of a single rod that is in motion except during a static period at the temporal center of the scan, termed the TR window. If the image of the motion scan has negligible motion artifacts compared to an image from a totally static scan, then the system has a TR no worse than the TR window used. By repeating this comparison with varying TR windows, the TR of the system can be accurately determined. Motion artifacts were also visually assessed and the TR was measured across varying rod motion speeds, directions, and locations. Noiseless fan beam acquisitions were simulated and images were reconstructed with a short‐scan image reconstruction algorithm.Results:The size, shape, and intensity of motion artifacts varied when the rod speed, direction, or location changed. TR measured using the proposed method, however, was consistent across rod speeds, directions, and locations.Conclusion:Since motion artifacts vary depending upon the motion speed, direction, and location, they are not suitable for measuring TR. In this work, a CT system with a specified TR is defined as having the ability to produce a static image with negligible motion artifacts, no matter what motion occurs outside of a static window of width TR. This framework allows for practical measurement of temporal resolution in clinical CT imaging systems.Funding support: GE Healthcare; Conflict of Interest: Employee, GE Healthcare.
TH‐CD‐207B‐07: Noise Modeling of Single Photon Avalanche Diode (SPAD) for Photon Counting CT Applications
Authors: Cheng Z , Zheng X , Deen J , Peng H , Xing L 
Published: July 2016

Purpose:Silicon photomultiplier (SiPM) has recently emerged as a promising photodetector for biomedical imaging applications. Due to its high multiplication gain (comparable to PMT), fast timing, low cost and compactness, it is considered a good candidate for photon counting CT. Dark noise is a limiting factor which impacts both energy resolution and detection dynamic range. Our goal is to develop a comprehensive model for noise sources for SiPM sensors.Methods:The physical parameters used in this work were based upon a test SPAD fabricated in 130nm CMOS process. The SPAD uses an n+/p‐well junction, which is isolated from the p‐substrate by a deep n‐well junction. Inter‐avalanche time measurement was used to record the time interval between two adjacent avalanche pulses. After collecting 1×106 counts, the histogram was obtained and multiple exponential fitting process was used to extract the lifetime associated with the traps within the bandgap.Results:At room temperature, the breakdown voltage of the SPAD is ∼11.4V and shows a temperature coefficient of 7.7mV/°C. The dark noise of SPAD increases with both the excess biasing voltage and temperature. The primary dark counts from the model were validated against the measurement results. A maximum relative error of 8.7% is observed at 20 °C with an excess voltage of 0.5V. The probabilities of after‐pulsing are found to be dependent of both temperature and excess voltage. With 0.5V excess voltage, the after‐pulsing probability is 63.5% at ‐ 30 °C and drops to ∼6.6% at 40 °C.Conclusion:A comprehensive noise model for SPAD sensor was proposed. The model takes into account of static, dynamic and statistical behavior of SPADs. We believe that this is the first SPAD circuit simulation model that includes the band‐to‐band tunneling dark noise contribution and temporal dependence of the after‐pulsing probability.
TH‐CD‐207B‐08: Relationship Between Pixel Noise and Task‐Specific Low Contrast Detectability for Various Patient Sizes in Abdomen CT
Authors: Nute J , Zhou Y , Scott A , Lee C 
Published: July 2016

Purpose:To establish a relationship between pixel noise and the statistically defined minimum detectable contrast for various phantom sizes in order to provide a means for dose modulation driven by the task‐specific low contrast detectability.Methods:Seven anthropomorphically‐shaped abdomen phantoms (TE01‐TE08, CIRS) were employed to mimic a wide range of patient sizes. Each phantom was scanned on a Siemens 64‐slice mCT using a range of CTDIvol values (1–38mGy) with an average of 19 dose levels per phantom. Pixel noise was determined from the pixel standard deviations in the uniform phantom background of the images. The low contrast detectability was characterized in the following manner: adjacent slices were subtracted and regions representing uniform phantom background were partitioned to matrices with elements matching the size of the lesion (1.8–10 mm). For each dose level and lesion size, the mean pixel value from each matrix element was computed and the distribution was tested against Gaussian. The minimum detectable contrast (MDC) was defined by 3.29 multiplied by the standard deviation of the aforementioned distribution. We proposed that MDC is proportional to pixel noise and inversely proportional to the lesion size and that the proportionality factor is a function of the phantom size. This model was used to fit the MDC data and the dependency of the proportionality factor on the phantom size was examined.Results:Pixel noise distributions were found to be Gaussian (p<0.05) in over 99% of cases. The proportionality factor of our model was found to be stable as the phantom size changes (mean = 0.301, coefficient of variation ranges from −2.42% to 5.22%).Conclusion:The relationship between pixel noise and task‐specific MDC has been characterized for a range of patient sizes. This relationship is the first step in establishing dose modulation driven by patient‐ and task‐specific low contrast detectability.
TH‐CD‐207B‐12: Quantification of Clinical Feedback On Image Quality Differences Between Two CT Scanner Models
Authors: Bache S , Liu X , Loyer E , Rong J 
Published: July 2016

Purpose:This work sought to quantify a radiology team's assessment of image quality differences between two CT scanner models currently in clinical use, with emphasis on noise and low‐contrast detectability (LCD).Methods:A water phantom and a Kagaku anthropomorphic body phantom were scanned on GE Discovery CT750 HD and LightSpeed VCT scanners (4 each) with identical scan parameters and reconstructed to 2.5mm/5.0mm thicknesses. Images of water phantom were analyzed at the scanner console with a built‐in LCD tool that uses statistical methods to compute requisite CT‐number contrast for 95% confidence in detection of a user‐defined object size. LCD value was computed for 5mm, 3mm, and 1mm objects. Analysis of standard deviation and LCD values were performed on Kagaku phantom images within liver, stomach, and spleen. LCD value was computed for 4mm, 3mm, and 1mm objects using a benchmarked MATLAB implementation of the GE scanner‐console tool.Results:Water LCD values were larger (poorer performance) for all HD scanners compared to VCT scanners. Mean scanner model difference in requisite CT‐number contrast for 5mm, 3mm, and 1mm objects for 5.0mm/2.5mm images was 3.0%/3.4% (p=0.02/p=0.10), 5.3%/5.7% (0.00002/0.02), and 8.5%/8.2% (0.0004/0.002), respectively. Mean standard deviations within Kagaku phantom ROIs were greater in HD compared to VCT images, with mean differences for the liver, stomach, and spleen for 5.0mm/2.5mm of 16%/12% (p=0.04/0.10), 8%/12% (0.15/0.11), and 16%/15% (0.05/0.11), respectively. Mean LCD value difference between HD and VCT scanners over all ROIs for 4mm, 3m, and 1mm objects and 5.0mm/2.5mm was 34%/9%, 16%/8%, and 18%/10%, respectively. HD scanners outperformed VCT scanners only for the 4mm stomach object.Conclusion:Using both water and anthropomorphic phantoms, it was shown that HD scanners are outperformed by VCT scanners with respect to noise and LCD in a consistent and in most cases statistically significant manner. The relationship between statistical and clinical significance demands further work.
TH‐EF‐BRC‐00: TG‐100 Workshop
Authors: Dunscombe Peter 
Published: July 2016

This Hands‐on Workshop will be focused on providing participants with experience with the principal tools of TG 100 and hence start to build both competence and confidence in the use of risk‐based quality management techniques. The three principal tools forming the basis of TG 100's risk analysis: Process mapping, Failure‐Modes and Effects Analysis and fault‐tree analysis will be introduced with a 5 minute refresher presentation and each presentation will be followed by a 30 minute small group exercise. An exercise on developing QM from the risk analysis follows. During the exercise periods, participants will apply the principles in 2 different clinical scenarios. At the conclusion of each exercise there will be ample time for participants to discuss with each other and the faculty their experience and any challenges encountered.

Learning Objectives:
To review the principles of Process Mapping, Failure Modes and Effects Analysis and Fault Tree Analysis.
To gain familiarity with these three techniques in a small group setting.
To share and discuss experiences with the three techniques with faculty and participants.Director, TreatSafely, LLC. Director, Center for the Assessment of Radiological Sciences. Occasional Consultant to the IAEA and Varian.
TH‐EF‐BRC‐02: FMEA
Authors: Huq M. 
Published: July 2016

This Hands‐on Workshop will be focused on providing participants with experience with the principal tools of TG 100 and hence start to build both competence and confidence in the use of risk‐based quality management techniques. The three principal tools forming the basis of TG 100's risk analysis: Process mapping, Failure‐Modes and Effects Analysis and fault‐tree analysis will be introduced with a 5 minute refresher presentation and each presentation will be followed by a 30 minute small group exercise. An exercise on developing QM from the risk analysis follows. During the exercise periods, participants will apply the principles in 2 different clinical scenarios. At the conclusion of each exercise there will be ample time for participants to discuss with each other and the faculty their experience and any challenges encountered.

Learning Objectives:
To review the principles of Process Mapping, Failure Modes and Effects Analysis and Fault Tree Analysis.
To gain familiarity with these three techniques in a small group setting.
To share and discuss experiences with the three techniques with faculty and participants.Director, TreatSafely, LLC. Director, Center for the Assessment of Radiological Sciences. Occasional Consultant to the IAEA and Varian.
TH‐D‐BRC‐04: Panel Member
Authors: Yu C. 
Published: July 2016

Interest in proton therapy has increased dramatically in the past couple of years, especially in the United States. There certainly is an important place for proton therapy in the arsenal of cancer treatments. Its dosimetric advantage and potential for low toxicity makes it the perfect partner for photons and other cancer treatment modalities.Often there is a belief that the new technology ought to be better but many believe that they should be widely adopted in the clinic only after evidence has shown that they are at least as safe and efficacious as existing technologies, which are often less expensive. This is the case for proton radiotherapy.This session being both educational and debate will provide a good review of basics and technological advancement as well as a comprehensive clinical update for both proton and photon therapy.Future technology developments and how they will impact the potential dosimetric advantage of proton therapy will be discussed. Particularly the debate will focus on whether these developments will close or widen the gap between photon and proton therapy.

Learning Objectives:
To review challenges, limitations, and recent and future developments in proton therapy.
To review challenges, limitations, recent and future developments in photon radiotherapy.
To review current clinical trials and challenging clinical cases.C. Yu, No company or organization that my presentation mentions provided me with financial support. I am the Founder and CEO of Xcision Medical Systems, LLC. However, my presentation will not mention any product or technology developed by Xcision.