bluuebunny/crossref_metadata_2025 huggingface dataset:
MO‐C‐18A‐01: Advances in Model‐Based 3D Image Reconstruction
Authors: Chen G , Pan X , Stayman J , Samei E
Published: July 2014

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient‐specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task‐specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization‐based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task‐based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications.

Learning Objectives:

Learn the general methodologies associated with model‐based 3D image reconstruction.

Learn the potential advantages in image quality and dose associated with model‐based image reconstruction.

Learn the challenges associated with computational load and image quality assessment for such reconstruction methods.

Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques.

Learn how model‐based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.
MO‐C‐18C‐01: Radiation Risks at Level of Few CT Scans: How Real?‐ Science to Practice
Authors: Rehani M , Samei E , Morgan W , Shore R , Goske M
Published: July 2014

There are controversies surrounding radiation effects in human population in the range of radiation doses encountered by patients resulting from one to several CT scans. While it is understandable why the effects from low levels of diagnostic radiation are controversial, the situation is complicated by the media which may distort the known facts. There is need to understand the state of science regarding low‐level radiation effects and also to understand how to communicate the potential risk with patients, the public and media. This session will seek to come to a consensus in order to speak with one voice to the media and the public. This session will review radiation effects known so far from a variety of exposed groups since the nuclear holocaust, provide clarification where effects are certain and where they are not, at what level extrapolation is the only way and at what level there is weak but agreeable acceptance. We will depict where and why there is agreement among organizations responsible for studying radiation effects, and how to deal with situations where effects are uncertain. Specific focus on radiation effects in children will be provided.Finally, the session will attempt to bridge the communication gap from the science to how to be an effective communicator with patients, parents, and media about ionizing radiation.

Learning Objectives:

To have a clear understanding about certainties and uncertainties of radiation effects at the level of a few CT scans

To understand the results and limitations from 3 major pediatric CT scientific studies on childhood exposures published recently.

To understand successful strategies used in risk communication
MO‐D‐BRD‐02: In Memoriam of Bengt Bjarngard: SBRT II: Small Field Dosimetry – TG155
Authors: Das I , Reft C
Published: July 2014

Specialized radiation treatment such as SRS/SRT. SBRT, IMRT, VMAT, Tomotherapy, CyberKnife and Gamma Knife use small fields or combination of small fields where dosimetry is challenging and uncertain due to non‐equilibrium conditions such as longitudinal and lateral disequilibrium. Additionally the primary photon fluence is greatly affected by the obstruction of the source size by the jaws creating a large dose gradient across the field. Electronic equilibrium is a phenomenon associated with the range of secondary particles which depend on the beam energy, photon spectrum and the composition of the medium. Additionally, the finite size of detectors creates volume averaging and fluence perturbations especially in small fields. The IAEA/AAPM has provided a frame work for non‐compliant reference dosimetry in small fields1. The AAPM TG‐1552 has adopted this frame work to provide guidelines in relative dosimetry. This course provides the insight of TG‐155 that defines small field, provides recommendations for suitable detectors and associated correction factors to convert reading to dose. Recommendations of a good working practice for relative dosimetry measurements (PDD, TMR, output factor, etc.) and dose calculations based on the new formulation is are elaborated. It also discusses beam modeling and dose calculations as a critical step in clinical utilization of small field radiotherapy. Small errors in beam data, approximations in dose algorithms, or misaligned of detectors and field settings can propagate into large errors in planned and delivered dose. The modeling and treatment planning aspects of small field dosimetry are reviewed with emphasis on the most critical parts for ensuring accurate and safe radiation therapy. Discussion on k(fmsr, fclin) for commercially available detectors are also provided.1 P. Alfonso, P. Andreo, R. Capote, M. S. Huq, W. Kilby, P. Kjall, T. R. Mackie, H. Palmans, K. Rosser, J. Seuntjens, W. Ullrich and S. Vatnitsky, “A new formalism for reference dosimetry of small and nonstandard fields,” Med Phys 35, 5179–5186 (2008).2 I. J. Das, P. Francescon, A. Ahnesjo, M. M. Aspradakis, C. W. Cheng, G. X. Ding, G. S. Ibbott, M. Oldham, M. S. Huq, C. S. Reft and S. O.A., “Task Group 155 report: Small fields and non‐equilibrium condition photon beam dosimetry,” Med Phys (under review).

Learning Objectives:

Physics of cavity theory for non‐equilibrium condition and definition of small fields

Description of absolute and relative dosimetry in the context of TG‐155

Choice of detector in small field dosimetry, perturbations and corrections (K(fmsr, fclin)) for accurate dosimetry

Understand the advantages and limitations of commercially available detectors for small field dosimetry
MO‐D‐16A‐01: International Day of Medical Physics
Authors: Cheung K , Damilakis J
Published: July 2014

International Organization for Medical Physics (IOMP) which represents medical physicists in more than 80 countries decided to celebrate 7th November, birth date of the Polish and naturalized‐French physicist Marie Sklodowska‐Curie, as International Day of Medical Physics (IDMP). The main purpose of the initiative is to raise the visibility and awareness of medical physicist in the global community, to introduce ourselves to the general public, and bring a message to the community that a group of health professionals, the medical physicists are there to help the patients and other health professionals. First celebration was done in 2013 and now IDMP will be celebrated every year. The theme of IDMP will be different each year. The theme for 2013 was ‘Radiation exposure from medical procedures, ask the Medical Physicist’. The inaugural event was celebrated in 23 countries and the amount of attention gained was remarkable. Main IDMP events were held in Poland, birthplace of Marie Curie, and France, workplace of Marie Curie. This year IOMP celebrates the 2nd IDMP and theme will be ‘Looking into the body‐Advancement in Imaging through Medical Physics’ to draw attention to the profound contributions Medical Physics has made to the use of ionizing and non‐ionizing radiation for the imaging of human body. A number of countries have informed about events that they are going to organize on IDMP. This gives wide attention to medical physics globally. AAPM is a major and important member of IOMP. It is hoped that AAPM will join in organizing activities.

Learning Objectives:

To learn about International Day of Medical Physics

To become familiar with how first IDMP was celebrated in 2013 and learning achieved

To understand on future plans for IDMPs
MO‐D‐9A‐01: “Stop Lecturing Me!” – New Methods for Teaching Medical Physics
Authors: Starkschall G , Kry S , Howell R
Published: July 2014

Medical physicists rarely have had exposure to teaching methodologies during their education. Our teaching methodologies are often based on copying the lecturing styles of effective teachers we have experienced. Consequently, it is rare that we make radical changes in our teaching methodologies. In recent years, new methodologies have been introduced that depart from the traditional lecture format. Some of these methodologies include Flipped Learning, Project‐Based Learning, and Peer Instruction. In all cases, students acquire information outside of class, either through readings or recorded lectures. In Flipped Learning, students use class time to solve problems and discuss information acquired prior to class. In Project‐Based Learning, students are assigned a project and acquire their understanding of the subject material by completing the project using a team approach. In Peer Instruction, students use class time to answer conceptual questions and assist each other in achieving conceptual understanding of the course material. In all cases, the course instructor serves as a guide to assist the student with their learning rather than as a presenter of information.In this course, several medical physicists will describe how these techniques are actually implemented in courses that they teach with the goal of providing attendees with models that attendees might use in their teaching.

Learning Objectives:

Identify novel teaching methodologies such as Flipped Learning, Project‐Based Learning, and Peer Instruction.

Be able to incorporate these teaching methodologies into their classes.

Identify various tools to aid in teaching such as Camtasia, Facebook, and SurveyMonkey
MO‐E‐19A‐01: Preparing for the ABR Therapy Medical Physics Exam
Authors: Grelewicz Z , Wang J , Vanderhoek M
Published: July 2014

Adequate and efficient preparation for the Diagnostic Medical Physics ABR exam is key to successfully obtaining this important professional certification.

Each part of the board exam presents its own difficulties:

Part I: Determining what material to study and how to efficiently review it, most of which might have been learned a long time ago.

Part II: Understanding how all types of imaging devices work, how their techniques affect images, and the doses delivered by them.

Part III: Gaining crucial clinical experiences and being able to explain orally how they are performed and what they mean.

All three require different skill sets and preparation, from memorizing vast amounts of material, to rapidly recognizing and solving calculations, to being able to easily and confidently respond to oral questions about all aspects of working in the clinic and why those clinical methods and procedures are performed that way. This symposium is not a comprehensive review of all required study material. Instead it focuses on the previously mentioned problems and required skill sets that are needed for each part of the board exam. Medical Physicists who have recently completed each of the different exam sections will share their experiences and preparation methods to help attendees best prepare for the challenges of each individual exam.

In accordance with ABR exam security policy, no recalls or exam questions will be discussed.

Learning Objectives:

How to Prepare for Part 1 by determining study material

How to Prepare for Part 2 by learning to solve clinical calculations rapidly

How to Prepare for Part 3 by determining critical clinical experiences and learning how to answer questions orally
MO‐E‐17A‐01: BEST IN PHYSICS (IMAGING) – Calculating SSDE From CT Exams Using Size Data Available in the DICOM Header of CT Localizer Radiographs
Authors: McMillan K , Bostani M , McCollough C , McNitt‐Gray M
Published: July 2014

Purpose:

To demonstrate the feasibility of using existing data stored within the DICOM header of certain CT localizer radiographs as a patient size metric for calculating CT size‐specific dose estimates (SSDE).

Methods:

For most Siemens CT scanners, the CT localizer radiograph (topogram) contains a private DICOM field that stores an array of numbers describing AP and LAT attenuation‐based measures of patient dimension. The square root of the product of the AP and LAT size data, which provides an estimate of water‐equivalent‐diameter (WED), was calculated retrospectively from topogram data of 20 patients who received clinically‐indicated abdomen/pelvis (n=10) and chest (n=10) scans (WED‐topo). In addition, slice‐by‐slice water‐equivalent‐diameter (WED‐image) and effective diameter (ED‐image) values were calculated from the respective image data. Using TG‐204 lookup tables, size‐dependent conversion factors were determined based upon WED‐topo, WED‐image and ED‐image values. These conversion factors were used with the reported CTDIvol to calculate slice‐by‐slice SSDE for each method. Averaging over all slices, a single SSDE value was determined for each patient and size metric. Patientspecific SSDE and CTDIvol values were then compared with patientspecific organ doses derived from detailed Monte Carlo simulations of fixed tube current scans.

Results:

For abdomen/pelvis scans, the average difference between liver dose and CTDIvol, SSDE(WED‐topo), SSDE(WED‐image), and SSDE(ED‐image) was 18.70%, 8.17%, 6.84%, and 7.58%, respectively. For chest scans, the average difference between lung dose and CTDIvol, SSDE(WED‐topo), SSDE(WED‐image), and SSDE(ED‐image) was 25.80%, 3.33%, 4.11%, and 7.66%, respectively.

Conclusion:

SSDE calculated using WED derived from data in the DICOM header of the topogram was comparable to SSDE calculated using WED and ED derived from axial images; each of these estimated organ dose to within 10% for both abdomen/pelvis and chest CT examinations. The topogrambased method has the advantage that WED data are already provided and therefore available without additional post‐processing of the image data.

Funding Support: NIH Grant R01‐EB017095; Disclosures – Michael McNitt‐Gray: Institutional Research Agreement, Siemens AG; Research Support, Siemens AG; Consultant, Flaherty Sensabaugh Bonasso PLLC; Consultant, Fulbright and Jaworski; Disclosures – Cynthia McCollough: Research Grant, Siemens Healthcare
MO‐E‐17A‐05: Individualized Patient Dosimetry in CT Using the Patient Dose (PATDOSE) Algorithm
Authors: Hernandez A , Boone J
Published: July 2014

Purpose:

Radiation dose to the patient undergoing a CT examination has been the focus of many recent studies. While CTDIvol and SSDE‐based methods are important tools for patient dose management, the CT image data provides important information with respect to CT dose and its distribution. Coupled with the known geometry and output factors (kV, mAs, pitch, etc.) of the CT scanner, the CT dataset can be used directly for computing absorbed dose.

Methods:

The HU numbers in a patient's CT data set can be converted to linear attenuation coefficients (LACs) with some assumptions. With this (PAT‐DOSE) method, which is not Monte Carlo‐based, the primary and scatter dose are computed separately. The primary dose is computed directly from the geometry of the scanner, x‐ray spectrum, and the known patient LACs. Once the primary dose has been computed to all voxels in the patient, the scatter dose algorithm redistributes a fraction of the absorbed primary dose (based on the HU number of each source voxel), and the methods here invoke both tissue attenuation and absorption and solid angle geometry. The scatter dose algorithm can be run N times to include Nth‐scatter redistribution. PAT‐DOSE was deployed using simple PMMA phantoms, to validate its performance against Monte Carlo‐derived dose distributions.

Results:

Comparison between PAT‐DOSE and MCNPX primary dose distributions showed excellent agreement for several scan lengths. The 1st‐scatter dose distributions showed relatively higher‐amplitude, long‐range scatter tails for the PAT‐DOSE algorithm then for MCNPX simulations.

Conclusion:

The PAT‐DOSE algorithm provides a fast, deterministic assessment of the 3‐D dose distribution in CT, making use of scanner geometry and the patient image data set. The preliminary implementation of the algorithm produces accurate primary dose distributions however achieving scatter distribution agreement is more challenging. Addressing the polyenergetic x‐ray spectrum and spatially dependent scatter fractions will improve algorithm accuracy.

Funding Support: UC Davis Clinical and Translational Science Center (CTSC) Award; NIH IMSD Student Fellowship; Saxon Student Fellowship

Disclosure: PATENT Royalties from Samsung for dosimetry algorithm
MO‐E‐17A‐12: Direct Realization of the CT Dose to Phantom: Energy to Heat Conversion in Polyethylene Using Calorimetry
Authors: Chen‐Mayer H , Tosh R
Published: July 2014

Purpose:

To develop a primary reference standard for absorbed dose to phantom for medical CT dosimetry. CT dosimetry relies on the implementation of the CTDI standard based on air kerma. We are taking a step toward an absorbed dose to water standard by first investigating the dose in a solid phantom. By directly measuring the heat converted from the incident radiation, the absorbed dose in the phantom at a point can be assessed with primary methods without relying on indirect conversions.

Methods:

The calorimeter contains two small thermistors embedded in a removable PE “core” inserted into the cylindrical HDPE phantom. A core made with polystyrene (PS) was also tested because of its purportedly negligible heat defect. Measurements were made using the two cores and with a calibrated ionization chamber in a CT beam. The air chamber values were converted to the dose to medium using appropriate stopping‐power ratios from the literature, and then compared to the thermal data.

Results:

The PS core data yielded a dose of 1.3 times (4‐run average, 3% std. dev.) higher than the converted chamber value, whereas the PE core data were inexplicably higher. The possible systematic errors include 1) excess heat from the thermistors, 2) in PE the exothermic chemical reactions, 3) uncertainties of the specific heat capacities of the materials, 4) thermal drift, and 5) theoretical conversion of chamber values. Monte Carlo simulations and finite element heat transfer calculations were performed to address some of these issues. The general validity was assessed in a 6 MV photon beam with an entirely different calibration scheme.

Conclusion:

This study demonstrates the feasibility but also revealed the difficulty in developing a new primary reference standard for absorbed dose to material for CT. Additional experimental and theoretical work is planned to achieve our goal.
MO‐F‐16A‐05: Spot‐Scanning Proton Therapy Patient‐Specific Quality Assurance: Our Methodology and Results From 295 Treatment Plans
Authors: Mackin D , Zhang X , Li Y , Li H , Wu R , Poenisch F , Suzuki K , Kerr M , Holmes C , Zhu X , Sahoo N , Gillin M
Published: July 2014

Purpose:

To report on the patient‐specific quality assurance (PSQA) results for 295 spot‐scanning proton therapy treatment plans from the MD Anderson PTC‐Houston. We show how the results differed by treatment site and how they were affected by the treatment plan optimization method and by a range shifter in the treatment field. We also discuss some causes of PSQA problems.

Methods:

The PSQA procedure, which is designed to verify both the accuracy of the treatment planning system's (Eclipse™ v8.9) dose calculations and the dose delivery of the Hitachi PROBEAT synchrotron, consists of (1) an end‐to‐end test in which the beam is delivered and measured at the prescribed gantry angle, and (2) additional dose plane measurements made from gantry angle 270°. HPlusQA™ software automatically performs the gamma analysis with criteria 3% (dose tolerance), 3 mm (distance‐to‐agreement, DTA) and 2%, 2 mm. Passing is defined as at least 90% of the pixels having a gamma score less than 1.

Results:

The PSQA gamma passing rate was 96.2% for 3%, 3 mm, and 85.3% for 2%, 2 mm. The rate depended on the treatment site. For example, the 3%, 3 mm passing rate was 95% for head and neck plans, vs 100% for prostate plans. The passing rates of multi‐ vs. single‐field optimization plans did not significantly differ. However, the rate for fields with range shifters was 94.8±0.6%, vs 99.0±0.6% for those without (p = 0.002). Longitudinal dose gradients caused most of the low scores. Overestimation of the calculated dose proximal to the spread‐out Bragg peak (SOBP) caused many of the others.

Conclusion:

The planned and delivered doses consistently agreed within tolerance levels. Minor dose modeling deficiencies remain proximal to the SOBP. The 3% dose tolerance, 3 mm DTA, with 90% pixel passing rate is a reasonable action level for 2D gamma comparisons.
MO‐G‐16A‐01: Student and Trainee Mock Interviews
Authors: Robertson S
Published: July 2014

Need to practice your interviewing skills? Want feedback on your performance? Then this session is for you! The purpose of this session is to allow students and trainees to practice their interviewing skills with interviewers from various backgrounds (academic, clinical, industry) leading the interview and providing each interviewee with an evaluation and critique.
MO‐G‐19A‐01: ABR Exams; A Glimpse Behind the Curtains
Authors: Ibbott G , Dieterich S , Yester M , Allison J , Seibert J
Published: July 2014

While the vast majority of clinical medical physicists either have taken, or will take the ABR certification exams, there appears to be limited understanding about the process of creating, administering, scoring and evaluating the exams. In this course, ABR trustees and volunteers will educate current and future ABR diplomates about the process of question writing, written exam assembly, evaluation of the performance of the exams, and preparation and delivery of the oral board exam. By understanding the process behind creating the written and oral exams, ABR candidates and diplomates will gain a better understanding of the framework of the exams, including the financial resources needed. At the same time, understanding the many hours of volunteer work needed to create a fair and meaningful exam may create a positive incentive to respect the exam confidentiality.

Learning Objectives:

Understand the characteristics of a well‐written exam question.

Learn about the process of developing and administering an exam.

Review the application of psychometric analysis to the ABR exams.

Appreciate the effort and expense involved in preparing the certification exams.
MO‐G‐BRE‐01: A Real‐Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring
Authors: Shi F , Gu X , Graves Y , Jiang S , Jia X
Published: July 2014

Purpose:

Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real‐time using GPU‐based Monte Carlo (MC) method.

Methods:

The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a ready signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real‐time.

Results:

An IMRT and a VMAT cases used in our patient‐specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time.

Conclusion:

By embedding a GPU‐based MC code in a novel data/work flow, it is possible to achieve real‐time MC dose calculations to monitor delivery process.
MO‐G‐BRE‐02: A Survey of IMRT QA Practices for More Than 800 Institutions
Authors: Pulliam K , Kerns J , Howell R , Followill D , O' Daniel J , Kry S
Published: July 2014

Purpose:

A wide range of techniques and measurement devices are employed for IMRT QA, causing a large variation of accepted action limits and potential follow up for failing plans. Such procedures are not well established or accepted in the medical physics community. To achieve the goal of proving insight into current IMRT QA practices, we created an electronic IMRT QA survey. The survey was open to a variety of the most common QA devices and assessed the type of comparison to measurement, action limits, delivery methods, and clinical action for failing QA plans.

Methods:

We conducted an online survey through the Radiological Physics Center's (RPC) annual survey with the goal of ascertaining elements of routine patient‐specific IMRT QA. A total of 874 institutions responded to the survey. The questions ranged from asking for action limits, dosimeter type(s) used, delivery techniques, and actions taken when a plan fails IMRT QA.

Results:

The most common (52%) planar gamma criteria was 3%/3 mm with a 95% of pixels passing criteria. The most common QA device were diode arrays (48%). The most common first response to a plan failing QA was to re‐measure at the same point the point dose (89%), second was to re‐measure at a new point (13%), and third was to analyze the plan in relative instead of absolute mode (10%) (Does not add to 100% as not all institutions placed a response for each QA follow‐up option). Some institutions, however, claimed that they had never observed a plan failure.

Conclusion:

The survey provided insights into the way the community currently performs IMRT QA. This information will help in the push to standardize action limits among dosimeters.
MO‐G‐BRE‐04: Automatic Verification of Daily Treatment Deliveries and Generation of Daily Treatment Reports for a MR Image‐Guided Treatment Machine
Authors: Yang D , li x , Li H , Wooten H , Green O , Rodriguez V , Mutic S
Published: July 2014

Purpose:

Two aims of this work were to develop a method to automatically verify treatment delivery accuracy immediately after patient treatment and to develop a comprehensive daily treatment report to provide all required information for daily MR‐IGRT review.

Methods:

After systematically analyzing the requirements for treatment delivery verification and understanding the available information from a novel MR‐IGRT treatment machine, we designed a method to use 1) treatment plan files, 2) delivery log files, and 3) dosimetric calibration information to verify the accuracy and completeness of daily treatment deliveries. The method verifies the correctness of delivered treatment plans and beams, beam segments, and for each segment, the beam‐on time and MLC leaf positions. Composite primary fluence maps are calculated from the MLC leaf positions and the beam‐on time. Error statistics are calculated on the fluence difference maps between the plan and the delivery. We also designed the daily treatment delivery report by including all required information for MR‐IGRT and physics weekly review – the plan and treatment fraction information, dose verification information, daily patient setup screen captures, and the treatment delivery verification results.

Results:

The parameters in the log files (e.g. MLC positions) were independently verified and deemed accurate and trustable. A computer program was developed to implement the automatic delivery verification and daily report generation. The program was tested and clinically commissioned with sufficient IMRT and 3D treatment delivery data. The final version has been integrated into a commercial MR‐IGRT treatment delivery system.

Conclusion:

A method was developed to automatically verify MR‐IGRT treatment deliveries and generate daily treatment reports. Already in clinical use since December 2013, the system is able to facilitate delivery error detection, and expedite physician daily IGRT review and physicist weekly chart review.
MO‐G‐BRE‐06: Metrics of Success: Measuring Participation and Attitudes Related to Near‐Miss Incident Learning Systems
Authors: Nyflot MJ , Kusano AS , Zeng J , Carlson JC , Novak A , Sponseller P , Jordan L , Kane G , Ford EC
Published: July 2014

Purpose:

Interest in incident learning systems (ILS) for improving safety and quality in radiation oncology is growing, as evidenced by the upcoming release of the national ILS. However, an institution implementing such a system would benefit from quantitative metrics to evaluate performance and impact. We developed metrics to measure volume of reporting, severity of reported incidents, and changes in staff attitudes over time from implementation of our institutional ILS.

Methods:

We analyzed 2023 incidents from our departmental ILS from 2/2012–2/2014. Incidents were prospectively assigned a near‐miss severity index (NMSI) at multidisciplinary review to evaluate the potential for error ranging from 0 to 4 (no harm to critical). Total incidents reported, unique users reporting, and average NMSI were evaluated over time. Additionally, departmental safety attitudes were assessed through a 26 point survey adapted from the AHRQ Hospital Survey on Patient Safety Culture before, 12 months, and 24 months after implementation of the incident learning system.

Results:

Participation in the ILS increased as demonstrated by total reports (approximately 2.12 additional reports/month) and unique users reporting (0.51 additional users reporting/month). Also, the average NMSI of reports trended lower over time, significantly decreasing after 12 months of reporting (p<0.001) but with no significant change at months 18 or 24. In survey data significant improvements were noted in many dimensions, including perceived barriers to reporting incidents such as concern of embarrassment (37% to 18%; p=0.02) as well as knowledge of what incidents to report, how to report them, and confidence that these reports were used to improve safety processes.

Conclusion:

Over a two‐year period, our departmental ILS was used more frequently, incidents became less severe, and staff confidence in the system improved. The metrics used here may be useful for other institutions seeking to create or evaluate their own incident learning systems.
MO‐G‐BRF‐03: BEST IN PHYSICS (JOINT IMAGING‐THERAPY) – Using Quantitative MRI to Test the Validity of the Reaction‐Diffusion Equation in Describing in Vivo Glioma Growth
Authors: Hormuth D , Weis J , Rericha E , Yankeelov T
Published: July 2014

Purpose:

The reaction‐diffusion equation has been used extensively to model brain tumor growth. However, no one has used quantitative, in vivo imaging data to invert the reaction‐diffusion equation to estimate model parameters and then use those values to predict future growth. In this contribution first we determine, in silico, the measurement strategies required to accurately estimate model parameters and test how accurately these parameters can be used to predict future growth. We then perform the analogous studies in vivo where quantitative diffusion weighted magnetic resonance imaging (DW‐MRI) data is used to estimate cell number to constrain and initialize the reaction‐diffusion equation.

Methods:

Measurement strategies were tested using an in silico tumor, seeded within a rat brain domain and “grown” for 9 days as dictated by the reactiondiffusion equation. Parameters were estimated from data subsets (using days 1–2,1–3,…1‐8) and used to predict subsequent growth. Prediction accuracy was assessed at the region of interest (total volume and cell number, and Dice value) and voxel (concordance correlation coefficient, CCC) levels. Guided by the simulation results, DW‐MRI data was acquired in rats (n=12) with C6 gliomas and used to evaluate the model's accuracy for predicting in vivo tumor growth.

Results:

Both the in silico and in vivo experiments returned error below 15% when predicting total cell number and volume 1–4 days into the future. The in silico study had CCC and Dice values greater than 0.93 for these predictions. However, the in vivo study had lower Dice (0.64–0.81) and quite poor CCC (0.11–0.25) values.

Conclusion:

The simulations show that the reaction‐diffusion equation can be used to make accurate bulk scale (i.e., total tumor volume and tumor cell number) predictions of in vivo tumor growth. However, the poor voxel level agreement, suggests the reaction‐diffusion equation is an incomplete description of in vivo C6 glioma growth.

NCI‐1U01CA174706, NCI‐ P30CA068485, NCI‐ R01CA138599, NCIR25CA136440
MO‐G‐BRF‐07: Optical Characterization of Novel Terbium‐Doped Nanophosphors Excited by Clinical Electron and Photon Beams for Potential Use in Molecular Imaging Or Photodynamic Therapy
Authors: Darafsheh A , Paik T , Tenuto M , Najmr S , Friedberg J , Murray C , Finlay J
Published: July 2014

Purpose:

Optical properties of terbium (Tb3+)‐doped gadolinium trifluoride (GdF3) nanoplates irradiated by electron and photon beams were investigated for their potential as optical probes. The contribution of induced Cerenkov radiation in exciting the nanophosphors was investigated as well.

Methods:

The emission spectra of Terbium‐doped GdF3 dispersed in hexane, embedded in tissue mimicking phantoms were collected by an optical fiber connected to a CCD‐coupled spectrograph, while the samples were irradiated by a medical linear accelerator with electron beams of energies 6, 9, 12, 16, and 20 MeV or X‐ray beams of energies of 6, and 15 MV. The contribution of induced Cerenkov radiation in exciting the nanophosphores was investigated in a dedicated experimental apparatus through optical isolation of the samples and also by using 125 kVp X‐ray beams whose energy is below the threshold for generating Cerenkov radiation in that medium.

Results:

Terbium‐doped GdF3 nanoplates show characteristic cathodoluminescence emission peaks at 488, 543, 586, and 619 nm, which are responsible for the characteristic f‐f transition of terbium ion. In a series of experiments, the contribution of Cerenkov radiation in the luminescence of such nanophosphors was ruled out.

Conclusion:

We have characterized the optical properties of Terbium‐doped GdF3 nanoplates. Such nanocrystals with emission tunability and high surface area that facilitates attachment with targeting reagents are promising in situ light source candidates for molecular imaging or exciting a photosensitizer for ultralow fluence photodynamic therapy.

This work is supported by the Department of Radiation Oncology at the University of Pennsylvania, the American Cancer Society through IRG‐78‐002‐28, and the University of Pennsylvania's Nano/Bio Interface Center through NSEC DMR08‐32802.
MO‐G‐BRF‐09: Local and Global Function Probability of the Liver
Authors: Wang H , Feng M , Jackson A , R Ten Haken , Lawrence T , Cao Y
Published: July 2014

Purpose:

To develop a local‐and‐global liver function probability model to aid in physiologically adaptive RT in patients with intrahepatic cancer.

Methods:

Twenty‐two patients enrolled in an IRB‐approved study had liver DCE‐MRI and Indocyanine green (ICG) clearance tests prior to, during and 1 month post RT. The percentage of ICG retention in the blood plasma 15 minutes after administration (ICG‐R15) was used as an overall liver function measure. Portal venous perfusion images were derived from DCE‐MRI. Assuming parallel architecture in the liver and considering both volume and function effects of tissue subunits, a global liver function probability Pl was assumed to be a volume‐weighted summation of local function probabilities p of subunits, excluding the subunits with p<γ, a threshold indicting poor function. The local function probability p was mapped from portal venous perfusion F by a logistic function p(F)=1/(1+(F50/F)^n), where we considered function probabilities saturated at high perfusion and suppressed at low perfusion. Finally, Pl was linearly fitted to a logarithm of ICG‐R15 to determine parameters (F50, n, γ). The leave‐m‐out technique was used to cross‐validate the model. This model was applied to evaluate the doseresponse of the local function probability one month after RT.

Results:

The best fitted values of (F50, n, γ) were 65.3ml/(100g.min), 2.86 and 0.14, respectively. The leave‐one‐out and 10‐fold tests resulted in 66.34±3.21 and 69.26±}10.35ml/(100g.min) of F50, 2.90±0.76 and 3.25±1.59 of n, and 0.14±0.03 and 0.13±}0.05 of γ, respectively. The derived Pl significantly correlated with ICG‐R15 logarithm (r=0.66, P<;1e‐6). Average percentage reduction in the local function probability post‐RT was related to the local dose by a logistic function (P=1e−6).

Conclusion:

The local‐and‐global liver function probability model has the potential to guide dose redistribution during a course of treatment to spare liver functional subunits, thereby allowing target d5 ose intensification without increasing the risk for complication.

The work was supported by NIH/NCI grant RO1CA132834.
MO‐G‐17A‐03: MR‐Based Cortical Bone Segmentation for PET Attenuation Correction with a Non‐UTE 3D Fast GRE Sequence
Authors: Ai H , Hwang K , Pan T
Published: July 2014

Purpose:

To determine the feasibility of identifying cortical bone on MR images with a short‐TE 3D fast‐GRE sequence for attenuation correction of PET data in PET/MR.

Methods:

A water‐fat‐bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE1/TE2/TE3=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast‐GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm3) was implemented along with a 3D Dixon sequence (TE1/TE2/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm3) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm3) and reconstructed voxel size (0.94×0.94×1.5mm3) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast‐GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separated using a threshold determined from the histogram.

Results:

There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short‐TE MR image. The acquisition time for the 3D fast‐GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water‐fat‐bone segmentation is visually similar compared to reference CT.

Conclusion:

Cortical bone and air can be separated based on intensity in MR image with a short‐TE 3D fast‐GRE sequence. Further research is required to optimize the strategy to reduce Gibbs artifacts.
MO‐G‐18C‐03: Evaluation of Deformable Image Registration for Lung Motion Estimation Using Hyperpolarized Gas Tagging MRI
Authors: Huang Q , Zhang Y , Liu Y , Hu L , Miller W , Yin F , Cai J
Published: July 2014

Purpose:

Hyperpolarized gas (HP) tagging MRI is a novel imaging technique for direct measurement of lung motion during breathing. This study aims to quantitatively evaluate the accuracy of deformable image registration (DIR) in lung motion estimation using HP tagging MRI as references.

Methods:

Three healthy subjects were imaged using the HP MR tagging, as well as a high‐resolution 3D proton MR sequence (TrueFISP) at the end‐of‐inhalation (EOI) and the end‐of‐exhalation (EOE). Ground truth of lung motion and corresponding displacement vector field (tDVF) was derived from HP tagging MRI by manually tracking the displacement of tagging grids between EOI and EOE. Seven different DIR methods were applied to the high‐resolution TrueFISP MR images (EOI and EOE) to generate the DIR‐based DVFs (dDVF). The DIR methods include Velocity (VEL), MIM, Mirada, multi‐grid B‐spline from Elastix (MGB) and 3 other algorithms from DIRART toolbox (Double Force Demons (DFD), Improved Lucas‐Kanade (ILK), and Iterative Optical Flow (IOF)). All registrations were performed by independent experts. Target registration error (TRE) was calculated as tDVF – dDVF. Analysis was performed for the entire lungs, and separately for the upper and lower lungs.

Results:

Significant differences between tDVF and dDVF were observed. Besides the DFD and IOF algorithms, all other dDVFs showed similarity in deformation magnitude distribution but away from the ground truth. The average TRE for entire lung ranged 2.5−23.7mm (mean=8.8mm), depending on the DIR method and subject's breathing amplitude. Larger TRE (13.3–23.7mm) was found in subject with larger breathing amplitude of 45.6mm. TRE was greater in lower lung (2.5−33.9 mm, mean=12.4mm) than that in upper lung (2.5−11.9 mm, mean=5.8mm).

Conclusion:

Significant differences were observed in lung motion estimation between the HP gas tagging MRI method and the DIR methods, especially when lung motion is large. Large variation among different DIR methods was also observed.
MO‐G‐18C‐07: Improving T2 Determination and Quantification of Lipid Methylene Protons in Proton Magnetic Resonance Spectroscopy at 3 T
Authors: Breitkreutz D. , B. G. Fallone , Yahya A.
Published: July 2014

Purpose:

To improve proton magnetic resonance spectroscopy (MRS) transverse relaxation (T2) determination and quantification of lipid methylene chain (1.3 ppm) protons by rewinding their J‐coupling evolution.

Methods:

MRS experiments were performed on four lipid phantoms, namely, almond, corn, sunflower and oleic acid, using a 3 T Philips MRI scanner with a transmit/receive birdcage head coil. Two PRESS (Point RESolved Spectroscopy) pulse sequences were used. The first PRESS sequence employed standard bandwidth (BW) (∼550 Hz) RF (radiofrequency) refocussing pulses, while the second used refocussing pulses of narrow BW (∼50 Hz) designed to rewind J‐coupling evolution of the methylene protons in the voxel of interest. Signal was acquired with each sequence from a 5×5×5 mm3 voxel, with a repetition time (TR) of 3000 ms, and with echo times (TE) of 100 to 200 ms in steps of 20 ms. 2048 sample points were measured with a 2000 Hz sampling bandwidth. Additionally, 30 mm outer volume suppression slabs were used to suppress signal outside the voxel of interest. The frequency of the RF pulses was set to that of the methylene resonance. Methylene peak areas were calculated and fitted in MATLAB to a monexponentially decaying function of the form M0exp(‐TE/T2), where M0 is the extrapolated area when TE = 0 ms and yields a measure of concentration.

Results:

The determined values of M0 and T2 increased for all fatty acids when using the PRESS sequence with narrow BW refocussing pulses. M0 and T2 values increased by an average amount (over all the phantoms) of 31% and 14%, respectively.

Conclusion:

This investigation has demonstrated that J‐coupling interactions of lipid methylene protons causes non‐negligible signal losses which, if not accounted for, Result in underestimations of their levels and T2 values when performing MRS measurements.

Funded by the Natural Sciences and Engineering Research Council of Canada and the Canadian Breast Cancer Foundation – Prairies.NWT
MO‐G‐18C‐09: Thermal Characterization and Initial Imaging Results From the Diffusive Quantitative Imaging Phantom (DQIP)
Authors: Yanasak N , Li X , Buzzelli M , Randazzo A
Published: July 2014

Purpose:

The goals of this project were a) to characterize thermal relaxation of the phantom and b) to demonstrate correlations between temperature, signal‐to‐noise ratio (SNR) and diffusion tensor imaging (DTI) metrics. A site‐based clinical DTI protocol was used in this study. The hypothesis is that thermal stability of the phantom will be adequate such that temperature monitoring during a scan may be neglected.

Methods:

The Diffusive Quantitative Imaging Phantom (DQIP) is a prototype phantom consisting of fifteen cylindrical compartments containing capillary arrays, encased in a larger compartment. Thermal stability of the phantom was established using a cork container and a Peltier incubator. After the phantom was cooled or heated, thermal relaxation was measured as the phantom was allowed to return to room temperature, with and without cork. A clinical‐grade DTI protocol was used to collect scan and temperaturedependent data from the phantom over ten days. SNR and thermal dependences of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were characterized by scanning a heated or cooled phantom over several hours.

Results:

The phantom temperature was stable within ±0.08°C/hour per degree difference from the scan room using the cork enclosure, compared to ±0.3°C/hour per degree difference without the cork enclosure. During scanning, temperature variation was ∼ 0.1°C/hour. The dependences of (FA, ADC) on (SNR, temperature) were characterized for the heating and cooling experiments. However, for the range of temperature (1.2°C) and SNR (max: ±77%) variations in a particular compartment over all daily measurements, no significant improvement in total variation was achieved after regressing out temperature and SNR, although one compartment showed significant decline after regression.

Conclusion:

Thermal stability of the DQIP phantom and incubator system is sufficient for neglecting temperature variations during scanning. For a standard clinical protocol, SNR dependence of FA and ADC are not sufficient to warrant correction.

Schott Glass North America and The Phantom Laboratory have donated materials and personnel time to this project.