bluuebunny/crossref_metadata_2025 huggingface dataset:
TU‐A‐BRF‐01: MR Guided Radiation Therapy
Authors: Stanescu T , Balter J , Nyholm T , Lagendijk J
Published: July 2014

In recent years, there has been an increasing interest in the development of new technologies focused on the deeper integration of MR in radiotherapy. The innovations span from image data acquisition and post‐processing to clinical implementation of MR‐guided RT systems and workflow development. The session is intended to provide a review of the key and most recent advancements. Targeted discussions will cover topics which currently define the concept of MR‐guided radiotherapy including a) system commissioning, quality control and safety, b) MR data manipulation for dose computations and treatment simulation, c) quantification/management of organ motion and treatment delivery guidance.

Learning Objectives:

Understand the concept and specifics of MR‐guided radiotherapy;

Understand the requirements for system integration in clinical workflow;

Become familiar with the proposed strategies for system commissioning, RT planning and delivery guidance.
TU‐A‐17A‐02: In Memoriam of Ben Galkin: Virtual Tools for Validation of X‐Ray Breast Imaging Systems
Authors: Myers K , Bakic P , Abbey C , Kupinski M , Mertelmeier T
Published: July 2014

This symposium will explore simulation methods for the preclinical evaluation of novel 3D and 4D x‐ray breast imaging systems – the subject of AAPM taskgroup TG234. Given the complex design of modern imaging systems, simulations offer significant advantages over long and costly clinical studies in terms of reproducibility, reduced radiation exposures, a known reference standard, and the capability for studying patient and disease subpopulations through appropriate choice of simulation parameters. Our focus will be on testing the realism of software anthropomorphic phantoms and virtual clinical trials tools developed for the optimization and validation of breast imaging systems. The symposium will review the stateof‐ the‐science, as well as the advantages and limitations of various approaches to testing realism of phantoms and simulated breast images. Approaches based upon the visual assessment of synthetic breast images by expert observers will be contrasted with approaches based upon comparing statistical properties between synthetic and clinical images. The role of observer models in the assessment of realism will be considered. Finally, an industry perspective will be presented, summarizing the role and importance of virtual tools and simulation methods in product development. The challenges and conditions that must be satisfied in order for computational modeling and simulation to play a significantly increased role in the design and evaluation of novel breast imaging systems will be addressed.

Learning Objectives:

Review the state‐of‐the science in testing realism of software anthropomorphic phantoms and virtual clinical trials tools;

Compare approaches based upon the visual assessment by expert observers vs. the analysis of statistical properties of synthetic images;

Discuss the role of observer models in the assessment of realism;

Summarize the industry perspective to virtual methods for breast imaging.
TU‐A‐9A‐02: Analysis of Variations in Clinical Doppler Ultrasound Peak Velocity Measurements
Authors: Zhang Y , Stekel S , Tradup D , Hangiandreou N
Published: July 2014

Purpose:

Doppler ultrasound (US) peak velocity (Vmax) measurements show considerable variations due to intrinsic spectral broadening with different scanning techniques, machines and manufacturers. We developed a semi‐automated Vmax estimation method and used this method to investigate the performance of a US system for clinical Doppler Vmax measurement.

Methods:

Semi‐automated Vmax is defined as the velocity at which the computed mean spectral profile falls to within 1 background standard deviation of the background mean. GE LOGIQ E9 system with 9L and ML6‐15 probes were studied with steady flow (5.3 – 12.5 ml/s) in a Gammex OPTIMIZER 1425A phantom. All Doppler spectra were acquired by 1 operator at the distal end of 5 mm angular tube using a modified clinical carotid artery protocol. Repeatability and variation of Vmax to scanning parameters and probes were analyzed and reported as percentage, i.e. (max‐min)/mean.

Results:

Vmax estimation had good repeatability (3.1% over 6 days for 9L, and 3.6% for ML6‐15). For 9L probe, varying gain, compression, scale, SV depth and length, and frequency had minimal impact on Vmax (all variations less than 4.0%). Beam steering had slightly higher influence (largest variations across flow rates were 4.9% for 9L and 6.9% for ML6‐15). For both probes, Doppler angle had the greatest effect on Vmax. Percentage increase of Vmax was largely independent of actual flow rates. For Doppler angle varied from 30 to 60°, Vmax increased 24% for 9L, and 20% for ML6‐15. Vmax measured by ML6‐15 were lower than that by 9L at each Doppler angle with differences less than 5%.

Conclusion:

The proposed Vmax estimation method is shown to be a useful tool to evaluate clinical Doppler US system performance. For the tested system and probes, Doppler angle had largest impact in measured Vmax.
TU‐A‐9A‐06: Semi‐Automatic Segmentation of Skin Cancer in High‐Frequency Ultrasound Images: Initial Comparison with Histology
Authors: Gao Y , Li X , Fishman K , Yang X , Liu T
Published: July 2014

Purpose:

In skin‐cancer radiotherapy, the assessment of skin lesion is challenging, particularly with important features such as the depth and width hard to determine. The aim of this study is to develop interative segmentation method to delineate tumor boundary using high‐frequency ultrasound images and to correlate the segmentation results with the histopathological tumor dimensions.

Methods:

We analyzed 6 patients who comprised a total of 10 skin lesions involving the face, scalp, and hand. The patient’s various skin lesions were scanned using a high‐frequency ultrasound system (Episcan, LONGPORT, INC., PA, U.S.A), with a 30‐MHz single‐element transducer. The lateral resolution was 14.6 micron and the axial resolution was 3.85 micron for the ultrasound image. Semiautomatic image segmentation was performed to extract the cancer region, using a robust statistics driven active contour algorithm. The corresponding histology images were also obtained after tumor resection and served as the reference standards in this study.

Results:

Eight out of the 10 lesions are successfully segmented. The ultrasound tumor delineation correlates well with the histology assessment, in all the measurements such as depth, size, and shape. The depths measured by the ultrasound have an average of 9.3% difference comparing with that in the histology images. The remaining 2 cases suffered from the situation of mismatching between pathology and ultrasound images.

Conclusion:

High‐frequency ultrasound is a noninvasive, accurate and easy‐accessible modality to image skin cancer. Our segmentation method, combined with high‐frequency ultrasound technology, provides a promising tool to estimate the extent of the tumor to guide the radiotherapy procedure and monitor treatment response.
TU‐A‐9A‐07: X‐Ray Acoustic Computed Tomography (XACT): 100% Sensitivity to X‐Ray Absorption
Authors: Xiang L , Ahmad M , Nikoozadeh A , Pratx G , Khuri‐Yakub B , Xing L
Published: July 2014

Purpose:

To assess whether X‐ray acoustic computed tomography (XACT) is more sensitive to X‐ray absorption than that of the conventional X‐ray imaging.

Methods:

First, a theoretical model was built to analyze the X‐ray absorption sensitivity of XACT imaging and conventional X‐ray imaging. Second, an XACT imaging system was developed to evaluate the X‐ray induced acoustic signal generation as well as the sensitivity improvement over transmission x‐ray imaging. Ultra‐short x‐ray pulses (60‐nanosecond) were generated from an X‐ray source operated at the energy of 150 kVp with a 10‐Hz repetition rate. The X‐ray pulse was synchronized with the acoustic detection via a x‐ray scintillation triggering to acquire the X‐ray induced acoustic signal.

Results:

Theoretical analysis shows that X‐ray induced acoustic signal is sensitive only to the X‐ray absorption, while completely insensitive to out the X‐ray scattering and fluorescence. XACT has reduced background and increased contrast‐to‐noise ratio, and therefore has increased sensitivity compared to transmission x‐ray imaging. For a 50‐μm size, gadolinium insertion in tissue exposed to 40 keV X‐rays; the sensitivity of XACT imaging is about 28.9 times higher than that of conventional X‐ray imaging.

Conclusion:

X‐ray acoustic computer tomography (XACT) as a new imaging modality combines X‐ray absorption contrast and high ultrasonic resolution in a single modality. It is feasible to improve the imaging sensitivity with XACT imaging compared with conventional X‐ray imaging. Taking advantage of the high ultrasonic resolution, it is possible to perform 3‐D imaging with a single x‐ray pulse with arrays of transducers without any mechanical motion of the imaging system. This single‐shot capability offers the potential of reducing radiation dose by a factor of 1000, and imaging 100 times faster when compared to the conventional X‐ray CT, and thus revolutionizing x‐ray imaging applications in medicine and biology.

The authors gratefully acknowledge the Department of Defense Prostate Cancer Research Programs W81XWH‐13‐1‐0481 (LX), the National Institutes of Health 1R01 CA133474 and 1R21 A153587, and SRFDP (20124407120012) for funding.
TU‐A‐9A‐08: Alternative Magnetic Particle Imaging Methods
Authors: Weaver J
Published: July 2014

Purpose:

Magnetic particle imaging (MPI) is being actively developed for applications where truly quantitative maps of the magnetic nanoparticle distribution are required. The applications include thermal therapies and cell tracking applications. Our Objectives: is to demonstrate the preliminary feasibility of alternative methods of imaging magnetic nanoparticles.

Methods:

Current MPI methods use an alternating magnetic field (AMF) to induce a magnetization and a static field gradient to localize their position. The magnetization is in the same direction as the AMF so only the harmonics of the magnetization can be isolated from the AMF by recording the signal at the harmonic frequencies. We recently introduced a method of using a small static field to induce a component of the magnetization in the direction perpendicular to the AMF for spectroscopic sensing applications. The perpendicular magnetization is geometrically as well as spectrally isolated from the AMF. Here we used equilibrium, Langevin function simulations to show that the perpendicular magnetization can be used to image nanoparticles using two gradient geometries. Simulations used 100nm iron oxide nanoparticles and a 75mT gradient over the field of view.

Results:

The perpendicular magnetization is entirely in the even harmonics as opposed to the current MPI methods where the magnetization is entirely in the odd harmonics. Localization can be achieved with static gradient fields in either the direction perpendicular to the AMF or in the same direction as the AMF. Using a stepped field in combination with a 2mT sinusoidal field and perpendicular gradients, a condition number of 8 can be achieved. Using an 80mT sinusoidal field and in‐line gradients, a condition number of 5 can be achieved with faster imaging.

Conclusion:

The perpendicular magnetization at the even harmonics can be used to produce low noise images with reasonably conditioned reconstructions.
TU‐A‐12A‐05: Evaluating the Consistency of Lesion Volumetry in Chest CT After Substantial Dose Reduction
Authors: Young S , McNitt‐Gray M
Published: July 2014

Purpose:

To quantify how the choice of dose affects semi‐automated lung lesion volume measurements on chest CT.

Methods:

We created a framework consisting of three parts: (1) software to simulate reduced‐dose sinograms from real clinical CT scans, (2) semi‐automated contouring tools for measuring lesion volumes, and (3) figures of merit to quantify reader performance. Three readers measured 35 lung lesions on images acquired with our routine chest protocol (Siemens Definition Flash, 120 kV, 250 reference mAs, B45f kernel) and four simulated fractions of the clinical protocol (25%, 10%, 3%, and 1%). To quantify reader performance, we compared the percent differences between clinical and reduced‐dose volumes against 15% and 30% thresholds, inspired by a QIBA claim about the reliability of tumor volume change measurements. We analyzed the distribution of percent differences at each dose, as well as outliers in those distributions.

Results:

The 35 clinical lesions produced volumes ranging from 150 mm^3 to 22500 mm^3, corresponding to effective spherical diameters of 7 to 35 mm. The distributions of percent differences appeared consistent across the dose levels we considered, with the majority of absolute percent differences less than 15%. Some exceptions to this rule were smaller lesions that did not comply with QIBA’s definition of a clinically‐relevant lesion.

Conclusion:

Our framework shows promise for quantifying the effects of dose on various clinical tasks. Based on the relatively small patient cohort in this study, the task of measuring clinicallyrelevant lesion volumes seemed to be independent of dose down to the 1% level (corresponding to a phantom CTDIvol value of 0.21 mGy), with the exception of a few outliers that did not comply with the QIBA definition of measurable, clinically‐relevant lesions. For QIBA‐compliant lesions, the dose level does not appear to affect reader performance.
TU‐A‐12A‐06: Intra‐Observer Variability in Delineation of Target Volumes in Breast Radiotherapy and Its Effect On Accuracy of Deformation Measurements
Authors: Juneja P , Bonora M , Evans P , Harris E
Published: July 2014

Purpose:

In breast radiotherapy, the target volume may change during treatment and need adaptation of the treatment plan. This is possible for both tumour bed (TB) and whole breast (WB) target volumes. Delineation of the target (to detect changes) is also subject to uncertainty due to intra‐ and inter‐observer variability. This work measured the uncertainty, due to intraobserver variability, in the quantification of tissue deformation.

Methods:

Datasets consisting of paired prone and supine CT scans of three patients were used. Significant deformation in target volumes is expected between prone and supine patient positions. The selected cases had 1) no seroma, 2) some seroma, and 3) large seroma. The TB and WB were outlined on each dataset three times by one clinician. Delineation variability was defined as the standard deviations of the distances between observer outlines. For each target volume and each case, tissue deformation between prone and supine delineations was quantified using the Dice similarity coefficient (DSC) and the average surface distance (ASD). The uncertainty in the tissue deformation (due to delineation variability) was quantified by measuring the ranges of DSC and ASD using all combinations of pairs of outlines (9 pairs).

Results:

For the TB, the range of delineation variability was 0.44‐1.16 mm. The deformation, DSC and ASD, (and uncertainty in measurement) of the TB between prone and supine position of the cases were: 1) 0.21 (0.17‐0.28) and 12.4 mm (11.8‐13 mm); 2) 0.54 (0.51‐0.57) and 3.3 mm (3.1‐3.5 mm); 3) 0.62 (0.61‐0.64) and 4.9 mm (4.6‐5.2 mm). WB deformation measurements were subject to less uncertainty due to delineation variability than TB deformation measurements.

Conclusion:

For the first time, the uncertainty, due to observer variability, in the measurement of the deformation of breast target volumes was investigated. Deformations in these ranges would be difficult to detect.

This work was supported in part by Cancer Research‐UK under Programme Grant C46/A10588 and in part by the National Institute for Health Research (NIHR) through funding of the biomedical research imaging centre. P. Juneja is supported by the EPSRC Platform Grant EP/H046526/1.
TU‐A‐12A‐09: Absolute Blood Flow Measurement in a Cardiac Phantom Using Low Dose CT
Authors: Ziemer B , Hubbard L , Lipinski J , Molloi S
Published: July 2014

Purpose:

To investigate a first pass analysis technique to measure absolute flow from low dose CT images in a cardiac phantom. This technique can be combined with a myocardial mass assignment to yield absolute perfusion using only two volume scans and reduce the radiation dose to the patient.

Methods:

A four‐chamber cardiac phantom and perfusion chamber were constructed from poly‐acrylic and connected with tubing to approximate anatomical features. The system was connected to a pulsatile pump, input/output reservoirs and power contrast injector. Flow was varied in the range of 1‐2.67 mL/s with the pump operating at 60 beats/min. The system was imaged once a second for 14 seconds with a 320‐row scanner (Toshiba Medical Systems) using a contrast‐enhanced, prospective‐gated cardiac perfusion protocol. Flow was calculated by the following steps: subsequent images of the perfusion volume were subtracted to find the contrast entering the volume; this was normalized by an upstream, known volume region to convert Hounsfield (HU) values to concentration; this was divided by the subtracted images time difference. The technique requires a relatively stable input contrast concentration and no contrast can leave the perfusion volume before the flow measurement is completed.

Results:

The flow calculated from the images showed an excellent correlation with the known rates. The data was fit to a linear function with slope 1.03, intercept 0.02 and an R2 value of 0.99. The average root mean square (RMS) error was 0.15 mL/s and the average standard deviation was 0.14 mL/s. The flow rate was stable within 7.7% across the full scan and served to validate model assumptions.

Conclusion:

Accurate, absolute flow rates were measured from CT images using a conservation of mass model. Measurements can be made using two volume scans which can substantially reduce the radiation dose compared with current dynamic perfusion techniques.
TU‐B‐19A‐01: Image Registration II: TG132‐Quality Assurance for Image Registration
Authors: Brock K , Mutic S
Published: July 2014

AAPM Task Group 132 was charged with a review of the current approaches and solutions for image registration in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. As the results of image registration are always used as the input of another process for planning or delivery, it is important for the user to understand and document the uncertainty associate with the algorithm in general and the Result of a specific registration. The recommendations of this task group, which at the time of abstract submission are currently being reviewed by the AAPM, include the following components. The user should understand the basic image registration techniques and methods of visualizing image fusion. The disclosure of basic components of the image registration by commercial vendors is critical in this respect. The physicists should perform end‐to‐end tests of imaging, registration, and planning/treatment systems if image registration is performed on a stand‐alone system. A comprehensive commissioning process should be performed and documented by the physicist prior to clinical use of the system. As documentation is important to the safe implementation of this process, a request and report system should be integrated into the clinical workflow. Finally, a patient specific QA practice should be established for efficient evaluation of image registration results. The implementation of these recommendations will be described and illustrated during this educational session.

Learning Objectives:

Highlight the importance of understanding the image registration techniques used in their clinic.

Describe the end‐to‐end tests needed for stand‐alone registration systems.

Illustrate a comprehensive commissioning program using both phantom data and clinical images.

Describe a request and report system to ensure communication and documentation.

Demonstrate an clinically‐efficient patient QA practice for efficient evaluation of image registration.
TU‐B‐16A‐01: To Which Journal Should I Submit My Paper
Authors: Williamson J , Mills M , Klein E , Pawlicki T
Published: July 2014

Research papers authored by Medical Physicists address a large spectrum of oncologic, imaging, or basic research problems; exploit a wide range of physical and engineering methodologies; and often describe the efforts of a multidisciplinary research team. Given the large number (about 100) competing journals accepting medical physics articles, it may not be clear to an individual author which journal is the best venue for disseminating their work to the scientific community. Relevant factors usually include the Journal’s audience and scientific impact, but also such factors as perceived acceptance rate, interest in their topic, and quality of service. The purpose of this symposium is to provide the medical physics community with an overview of scope, review processes, and article guidelines for the following journals: Medical Physics, International Journal of Radiation Biology and Physics, Journal of Applied Clinical Medical Physics, and Practical Radiation Oncology. The senior editors for each journal will provide details as to the journals review process, for example: single blind versus double blind reviews; the hierarchy of the review process in terms of editorial board structure; the reality of acceptance, in terms of acceptance rate; and the types of research the journal prefers to publish. The goal is to provide for authors guidance before they begin to write their papers, not only for proper formatting, but also that the readership is appropriate for the particular paper, hopefully increasing the likelihood of publication.

Learning Objectives:

To review each Journal’s submission and review process

Guidance as to how to increase chances of acceptance

To help decipher which journal is appropriate for a given work
TU‐C‐BRD‐01: Image Guided SBRT I: Multi‐Modality 4D Imaging
Authors: Cai J , Mageras G , Pan T
Published: July 2014

Motion management is one of the critical technical challenges for radiation therapy. 4D imaging has been rapidly adopted as essential tool to assess organ motion associated with respiratory breathing. A variety of 4D imaging techniques have been developed and are currently under development based on different imaging modalities such as CT, MRI, PET, and CBCT. Each modality provides specific and complementary information about organ and tumor respiratory motion. Effective use of each different technique or combined use of different techniques can introduce a comprehensive management of tumor motion. Specifically, these techniques have afforded tremendous opportunities to better define and delineate tumor volumes, more accurately perform patient positioning, and effectively apply highly conformal therapy techniques such as IMRT and SBRT. Successful implementation requires good understanding of not only each technique, including unique features, limitations, artifacts, imaging acquisition and process, but also how to systematically apply the information obtained from different imaging modalities using proper tools such as deformable image registration. Furthermore, it is important to understand the differences in the effects of breathing variation between different imaging modalities. A comprehensive motion management strategy using multi‐modality 4D imaging has shown promise in improving patient care, but at the same time faces significant challenges. This session will focuses on the current status and advances in imaging respiration‐induced organ motion with different imaging modalities: 4D‐CT, 4D‐MRI, 4D‐PET, and 4D‐CBCT/DTS.

Learning Objectives:

Understand the need and role of multimodality 4D imaging in radiation therapy.

Understand the underlying physics behind each 4D imaging technique.

Recognize the advantages and limitations of each 4D imaging technique.
TU‐C‐BRE‐01: KEYNOTE PRESENTATION – Emerging Frontiers in IMRT QA
Authors: Siebers J
Published: July 2014

As IMRT treatment processes advance and mature, so must the quality assurance processes being used to validate their delivery. In some respects, treatment delivery advancements (e.g. VMAT) have out‐paced QA advancements. The purpose of this session is to describe new processes that are being implemented to bring IMRT QA up‐to‐date with the treatment delivery advances. It would explore emerging IMRT QA paradigms, including requirements‐based IMRT QA which necessitates definition of delivery errors (e.g. patient dose error, leaf positioning error) and development of processes to ensure reliable error detection. Engineeringbased QA approaches, including use of IMRT treatment delivery process trees, fault tree analysis and failure modes effects analysis would be described. Approaches to detect errors such as (1) during treatment delivery validation using exit fluence detectors (e.g. EPIDs); (2) analysis of treatment delivery via use of machine parameter log files; (3) dose recalculation using (3a) treatment planning system; (3b) record‐and‐verify; or (3c) entrance and exit fluence measurement parameters would be explained. The relative advantages and disadvantages of each method would be discussed. Schemes for error classification and root cause analysis would be described – steps which are essential for future error prevention. For each QA method, testing procedures and results would be presented indicating the types of errors that can be detected, those that cannot be detected, and the reliability of the error detection method (for example determined via ROC analysis). For speakers, we are seeking to engage non‐commercially biased experts. Those listed below are a sub‐sample of possible qualified individuals.
TU‐C‐17A‐03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotheray
Authors: Chen H , Tan J , Kavanaugh J , Dolly S , Gay H , Thorstad W , Anastasio M , Altman M , Mutic S , Li H
Published: July 2014

Purpose:

Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process.

Methods:

The contouring tool was developed using an object‐oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3‐D mesh models from critical RT structures in real‐time and 360° visualization. Principal component analysis (PCA) was used for system self‐updating geometry variations of normal structures based on physician‐approved RT contours as a training dataset. The inhouse design of supervised PCA‐based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer.

Results:

The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3‐D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi‐slice view, which allows physicians to visually check and edit abnormally contoured structures.

Conclusion:

This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand‐alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.
TU‐C‐17A‐04: BEST IN PHYSICS (THERAPY) – A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head‐ Neck Cancer
Authors: Chen H , Kavanaugh J , Tan J , Dolly S , Gay H , Thorstad W , Anastasio M , Altman M , Mutic S , Li H
Published: July 2014

Purpose:

Precise contour delineation of tumor targets and critical structures from CT simulations is essential for accurate radiotherapy (RT) treatment planning. However, manual and automatic delineation processes can be error prone due to limitations in imaging techniques and individual anatomic variability. Tedious and laborious manual verification is hence needed. This study develops a general framework for automatically assessing RT contours for head‐neck cancer patients using geometric attribute distribution models (GADMs).

Methods:

Geometric attributes (centroid and volume) were computed from physician‐approved RT contours of 29 head‐neck patients. Considering anatomical correlation between neighboring structures, the GADM for each attribute was trained to characterize intra‐ and interpatient structure variations using principal component analysis. Each trained GADM was scalable and deformable, but constrained by the principal attribute variations of the training contours. A new hierarchical model adaptation algorithm was utilized to assess the RT contour correctness for a given patient. Receiver operating characteristic (ROC) curves were employed to evaluate and tune system parameters for the training models.

Results:

Experiments utilizing training and non‐training data sets with simulated contouring errors were conducted to validate the framework performance. Promising assessment results of contour normality/abnormality for the training contour‐based data were achieved with excellent accuracy (0.99), precision (0.99), recall (0.83), and F‐score (0.97), while corresponding values of 0.84, 0.96, 0.83, and 0.9 were achieved for the non‐training data. Furthermore, the areas under the ROC curves were above 0.9, validating the accuracy of this test.

Conclusion:

The proposed framework can reliably identify contour normality/abnormality based upon intra‐ and inter‐structure constraints derived from clinically‐approved contours. It also allows physicians to analytically determine the system parameters to fit various clinic requirements (e.g. as‐low‐as‐possible false positives). It has great potential for improving RT work flow. More geometric attributes and training sets will be investigated to improve framework performance in the future.
TU‐C‐17A‐06: Evaluating IMRT Plan Deliverability Via PTV Shape and MLC Motion
Authors: McGurk R , Smith VA , Price M
Published: July 2014

Purpose:

For step‐and‐shoot intensity‐modulated radiation therapy (IMRT) plans, the dosimetry and deliverability can be affected by the number and shape of the segments used. Thus, plan deliverability is likely related to target volume and shape. We investigated whether the sphericity of target volumes and the previously proposed Modulation Complexity Score (MCS) could be used together to improve the detection of IMRT fields that failed quality assurance (QA).

Methods:

526 and 353 IMRT fields from 32 prostate and 28 head‐and‐neck (H' N) patients, respectively, were analyzed. MCS was used to quantify the complexity of multi‐leaf collimator shapes and motion patterns for each field. Sphericity was calculated using the surface area and volume of each patient’s planning target volume (PTV). Logistic regression models with MCS‐alone or MCS and sphericity terms were fit to PlanUNC IMRT pass/fail results (5% dose difference, 4mm distance‐to‐agreement criteria) using SAS 9.3 (Cary, NC). Model concordance, discordance and area under the curve (AUC) were used to quantify model accuracy.

Results:

Mean (±1 standard deviation) MCS for prostate and H' N were 0.58(±0.15) and 0.40 (±0.14), respectively. Mean sphericity scores were 0.75(±0.05) for prostate and 0.63 (±0.12) for H' N. Both metrics were significantly different between treatment locations (p<0.01, Wilcoxon Rank Sum Test) indicating greater complexity in shape and variations for H' N PTVs. For prostate, concordance, discordance and AUC using MCS alone were 80.8%, 18.7% and 0.811. Including sphericity in the model improved these to 81.7%, 17.7% and 0.820. For H' N, the original concordance, discordance and AUC were of 72.9%, 26.9% and 0.729. Including sphericity into the model improved these metrics to 76.5%, 23.2% and 0.729.

Conclusion:

Sphericity provides a quantitative measure of PTV shape. While improvement in IMRT QA failure detection was modest for both prostate and H' N plans, including sphericity in the model does improve IMRT field failure prediction.
TU‐C‐17A‐11: Progressive Knowledge Modeling for Pelvic IMRT/VMAT Treatment Planning
Authors: Lu S , Yuan L , Craciunescu O , Ge Y , Yin F , Wu Q
Published: July 2014

Purpose:

To investigate the feasibility of progressive knowledge modeling for IMRT/VMAT treatment planning for multiple cancer types in the pelvic region.

Methods:

The treatment planning knowledge model quantifies correlations between patient anatomical features and the OAR dose sparing features. The model is trained with prior plans using a stepwise regression machine learning technique. The progressive modeling process starts with 20 low risk prostate plans (type 1) which offer simplest PTV‐OAR geometry. Cases with more complex PTV‐OAR anatomies (prostate with lymph node, type 2 and anal rectal, type 3) are added to the training dataset one by one until the model prediction accuracies reach a plateau. The starting point of the plateau also defines the minimum numbers of type 2 and 3 training cases required for modelling. The DVHs predicted by the knowledge model for bladder, femoral heads and rectum were validated by 20, 9 and 18 cases with type 1, 2, and 3 geometries, respectively (rectum DVHs are omitted for type 3). Mean and standard deviation of differences between the dosimetric parameters sampled from the DVHs and the corresponding actual plan values measure the prediction accuracy of the model. Further, the accuracy was also compared with the single‐type models which were trained by single type cases.

Results:

Prediction accuracy reaches plateau when 6 type 2 and 8 type 3 cases were added to the training dataset. The determination coefficients R2 (should be square, font) for the OARs are: Bladder 0.90, rectum 0.64, and femoral heads 0.82. The prediction accuracies by the multiple‐type model and single‐type model have no significant differences by F‐test (p‐value: bladder: 0.58, femoral head: 0.44, rectum: 0.97).

Conclusion:

Progressive knowledge modeling of OAR sparing for multiple cancer types in pelvic region is feasible and has comparable accuracy to single cancer type modeling.

Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.
TU‐C‐12A‐02: Development of a Multiparametric Statistical Response Map for Quantitative Imaging
Authors: Bosca R , Mahajan A , Johnson VE , Brown PD , Dong L , Stafford RJ , Jackson EF
Published: July 2014

Purpose:

Quantitative imaging biomarkers (QIB) are becoming increasingly utilized in early phase clinical trials as a means of non‐invasively assessing treatment response and associated response heterogeneity. The aim of this study was to develop a flexible multiparametric statistical framework to predict voxel‐by‐voxel response of several potential MRI QIBs.

Methods:

Patients with histologically proven glioblastomas (n=11) were treated with chemoradiation (with/without bevacizumab) and underwent one baseline and two mid‐treatment (3–4wks) MRIs. Dynamic contrast‐enhanced (3D FSPGR, 6.3sec/phase, 0.1 mmol/kg Gd‐DTPA), dynamic susceptibility contrast (2D GRE‐EPI, 1.5sec/phase, 0.2mmol/kg Gd‐DTPA), and diffusion tensor (2D DW‐EPI, b=0, 1200 s/mm2, 27 directions) imaging acquisitions were obtained during each study. Mid‐treatment and pre‐treatment images were rigidly aligned, and regions of partial response (PR), stable disease (SD), and progressive disease (PD) were contoured in consensus by two experienced radiation oncologists. Voxels in these categories were used to train ordinal (PR<SD<PD) and logistic (PR/SD<PD) models incorporating the apparent diffusion coefficient, fractional anisotropy, Ktrans, ve, vp, and relative cerebral blood volume imaging biomarkers, as well as treatment type. Leave‐one‐out cross‐validation was performed at the patient level to assess model prediction accuracy.

Results:

Ordinal regression resulted in model prediction accuracies of 60% (PR), 0% (SD), 81% (PD), and 69% (overall), with coefficients of variation (COV) of 9.4%, 9.6%, and 23.6%, respectively. Logistic regression resulted in accuracies of 82.0% (PR/SD), 46.2% (PD), and 76.2% (overall) with COVs of 22.4%, 45.7%, and 23.8%, respectively.

Conclusion:

Despite limited patient numbers, this feasibility pilot study demonstrates that ordinal and logistic regression models potentially provide a flexible statistical framework for incorporating longitudinal multiparametric quantitative imaging data and, for these particular data, can potentially distinguish PR from PD. Additional training data exhibiting a full range of treatment responses is expected to allow prediction of all three treatment outcomes. Moreover, these models are extensible to multi‐modality, multiparametric analyses.
TU‐C‐12A‐04: Quantification of Hepatic Perfusion Maps From DCE MRI Using Spatial Regularization
Authors: Wang H , Cao Y
Published: July 2014

Purpose:

To reduce uncertainty in hepatic perfusion quantification from DCE‐MRI for assessing tumor and normal tissue response to RT in intrahepatic cancer.

Methods:

Pharmacokinetic modeling of liver DCE‐MRI estimates arterial inflow rate (ka), portal venous inflow rate (kp), central venous outflow rate (kl) and bolus arrival time delays (ta, tp) from respective arterial and portal vein input functions to tissue. The parametric images estimated by the conventional approach could exhibit large variance and severe bias. To reduce the uncertainty in the quantitative images, we incorporated the prior knowledge, the slowly spatial variation of the time delays in the liver, into the model by adding spatial regulations on the time delays. The weighting factors of regularizations were determined by Stein’s unbiased risk estimate (SURE) of the mean squared prediction error (MSPE). The regularized model was optimized by the conjugate gradient minimization and implemented on the GPU to achieve fast computation. The method was evaluated by simulated and patient DCE‐MRI data. The simulated perfusion image was generated with (k2, ka, kp) of (400, 20,100 ml/(100g min)), smooth (ta, tp) maps in a 64×64 matrix, and Gaussian random noise to have contrast‐to‐noise ratios (CNR) of 10 to 100.

Results:

Compared to the conventional estimation, the proposed method reduced estimate variances from 14% to 8% for k2, 38% to 9% for ka, and 14% to 10% for kp in the simulated data with 20 CNR. The SURE‐based method consistently led to the optimal weight of the regularization determined by the true MSPE on the simulation data. Regularizing time delays improved the hepatic perfusion images of the patient data.

Conclusion:

The improved repeatability and possible accuracy in hepatic perfusion images quantified from DCE‐MRI have the potential to increase statistical power in clinical application of the physiological images for predicting outcome of liver cancer treatment.

The work was supported by NIH/NCI grant RO1CA132834.
TU‐C‐12A‐09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo‐Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics
Authors: Zhang H , Tan S , Chen W , Kligerman S , Kim G , D’Souza W , Suntharalingam M , Lu W
Published: July 2014

Purpose:

To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo‐radiotherapy (CRT) in patients with locally advanced esophageal cancer.

Methods:

This study included 20 patients who underwent tri‐modality therapy (CRT + surgery) and had 18F‐FDG PET/CT scans before initiation of CRT and 4‐6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial‐temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross‐validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross‐validations to avoid model over‐fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC.

Results:

When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial‐temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model.

Conclusion:

The SVM model using all features including quantitative PET/CT features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.

This work was supported in part by National Cancer Institute Grant R21 CA131979 and R01 CA172638. Shan Tan was supported in part by the National Natural Science Foundation of China 60971112 and 61375018, and by Fundamental Research Funds for the Central Universities 2012QN086.
TU‐E‐BRD‐01: President’s Symposium: The Necessity of Innovation in Medical Physics
Authors: Bayouth J , Siewerdsen J , Wahl E
Published: July 2014

This abstract will not blow you away, but speed‐painting presenter Erik Wahl will certainly make a truly unique AAPM symposium that you will not want to miss. Along with clinical director John Bayouth and scientific leader Jeff Siewerdsen, this session will highlight innovation. To avoid being button pushers and irrelevant investigators of yesterday’s science, we must innovate. This is particularly challenging in the changing landscape of declining research funding and healthcare reimbursement.

But all hope is not lost, Medical Physics is a field born out of innovation. As scientists we quickly translated the man‐made and natural phenomena of radiation into a tool that could diagnose broken bones, locate foreign objects imbedded within the body, and treat a spectrum of diseases. As hyperbolae surrounding the curative powers of radiation overcame society, physicists continued their systematic pursuit of a fundamental understanding of radiation and applied their knowledge to enable the diagnostic and therapeutic power of this new tool.

Health economics and the decline in research funding have put the Medical Physicist in a precarious position: how do we optimally participate in medical research and advanced patient care in the face of many competing needs? Today's diagnostic imaging and therapeutic approaches are tremendously sophisticated. Researchers and commercial vendors are producing technologies at a remarkable rate; to enable their safe and effective implementation Medical Physicists must work from a fundamental understanding of these technologies. This requires all of us, clinically practicing Medical Physicists, Researchers and Educators alike, to combine our training in scientific methods with innovation.

Innovation is the key to our past, a necessity for our contemporary challenges, and critical for the future of Medical Physics. The keynote speakers for the 2014 AAPM Presidential Symposium session will address the way we approach these vitally important technologies for diagnosis and therapy into opportunities to innovate. The speed‐painting artist and lecturer Erik Wahl will finish the symposium with a fast‐paced and entertaining presentation on embracing the future by creating disruptive innovation strategies.

Learning Objectives:

Identify connection between Medical Physics and Innovation.

Understand how Innovation enables Clinical Medical Physicists to implement novel technologies.

Learn how innovative Medical Physics solutions can address significant and relevant challenges in science.

Become inspired to pursue a new scientific understanding, positive change in clinical practice, and benefit to patients.
TU‐F‐BRE‐03: Application of a Novel Mass‐Density Compensation Optimization Method to Improve the Response of a Liquid‐Filled Ionization Chamber in Nonstandard Fields
Authors: Kamio Y , Bouchard H
Published: July 2014

Purpose:

To improve the response of the microLion detector (PTW 31018) in small field conditions by optimizing the density of the detector's non‐sensitive components using a novel method based on the detector's dose response function.

Methods:

The central values h(0,0) of the perturbation functions for the microLion detector and a volume of water equivalent to its sensitive volume were calculated using the Monte Carlo user code egs_chamber by scoring the dose absorbed by a 6 MV photon pencil beam incident on their centroids. Values of h(0,0) were plotted as a function of the density of the microLion's graphite electrode with the detector placed in the axial orientation. The optimized density was found by finding the minimal value of h(0,0).

Results:

A density of 1.37 g/cm3 was found to minimize the perturbation function of the microLion detector. The modified microLion's response was then evaluated in small square fields with sides in the range of 5 – 40 mm and found to be consistent with highly watere‐quivalent detectors such as a scintillating detector (Exradin W1) and a generic alanine detector in both axial and radial orientations.

Conclusion:

This work illustrates a novel method which can used to optimize the design of radiation detectors in small fields. This method should also work with other optimization parameters (e.g. thickness of electrode). Density‐compensated detectors have the potential to eliminate the need to evaluate nonstandard field correction factors as described by the IAEA‐AAPM formalism (Alfonso et al.) and simplify future dosimetry protocols for SRS/SBRT modalities. Finally, we also expect an improvement in the response of density‐compensated detectors for composite IMRT fields.