bluuebunny/crossref_metadata_2025_split huggingface dataset:
MO‐C‐BRD‐01: SBRT I: Overview of Simulation, Planning, and Delivery
Authors: Cai J , Wijesooriya K , Benedict S 
Published: July 2014

SBRT is having a dramatic impact on radiation therapy of early‐stage, locally advanced cancers. Treatment planning of SBRT is characterized by precise target volume delineation, highly conformal dose distribution, sharp dose gradient outside the target volume, and meticulous tumor motion management. Stringent dose volume constraints of SBRT require the treatment planning to be highly personalized. Not only the complete patient's medical information (anatomy, tumor size/shape/location/motion, breathing pattern, health status) should be taken into consideration during planning, but also a wide range of factors including imaging techniques (imaging modality, breathing control, immobilization, patient position, etc.), treatment planning techniques (3D‐conformal, dynamic conformal arcs, IMRT, VMAT, etc.), image guidance techniques (CBCT, orthogonal X‐Ray, fiducial marker tracking, fluoroscopy, etc.), and delivery techniques (FFF, gating, tracking, etc.). In addition, potential pitfalls may exist in SBRT techniques and processes, such as target volume error in 4DCT and image registration error in image guidance. How to manage these affecting factors to achieve optimal, personalized SBRT treatment plans remains a great challenge. In this proposal, we will review technologies, share experiences, and discuss strategies concerning SBRT simulation, planning, and delivery.Learning Objectives:

Understand requirements and challenges of SBRT treatment planning
Discuss information‐driven strategies of developing optimal SBRT plans
Discuss practical considerations and potential pitfalls of SBRT treatment planning
MO‐C‐17A‐02: A Novel Method for Evaluating Hepatic Stiffness Based On 4D‐MRI and Deformable Image Registration
Authors: Cui T , Liang X , Czito B , Palta M , Bashir M , Yin F , Cai J 
Published: July 2014

Purpose:Quantitative imaging of hepatic stiffness has significant potential in radiation therapy, ranging from treatment planning to response assessment. This study aims to develop a novel, noninvasive method to quantify liver stiffness with 3D strains liver maps using 4D‐MRI and deformable image registration (DIR).Methods:Five patients with liver cancer were imaged with an institutionally developed 4D‐MRI technique under an IRB‐approved protocol. Displacement vector fields (DVFs) across the liver were generated via DIR of different phases of 4D‐MRI. Strain tensor at each voxel of interest (VOI) was computed from the relative displacements between the VOI and each of the six adjacent voxels. Three principal strains (E1, E2 and E3) of the VOI were derived as the eigenvalue of the strain tensor, which represent the magnitudes of the maximum and minimum stretches. Strain tensors for two regions of interest (ROIs) were calculated and compared for each patient, one within the tumor (ROI1) and the other in normal liver distant from the heart (ROI2).Results:3D strain maps were successfully generated fort each respiratory phase of 4D‐MRI for all patients. Liver deformations induced by both respiration and cardiac motion were observed. Differences in strain values adjacent to the distant from the heart indicate significant deformation caused by cardiac expansion during diastole. The large E1/E2 (∼2) and E1/E2 (∼10) ratios reflect the predominance of liver deformation in the superior‐inferior direction. The mean E1 in ROI1 (0.12±0.10) was smaller than in ROI2 (0.15±0.12), reflecting a higher degree of stiffness of the cirrhotic tumor.Conclusion:We have successfully developed a novel method for quantitatively evaluating regional hepatic stiffness based on DIR of 4D‐MRI. Our initial findings indicate that liver strain is heterogeneous, and liver tumors may have lower principal strain values than normal liver. Thorough validation of our method is warranted in future studies.NIH (1R21CA165384‐01A1)
MO‐C‐17A‐08: Evaluation of Lung Deformation Using Three Dimensional Strain Maps
Authors: Cui T , Huang Q , Miller W , Zhong X , Yin F , Cai J 
Published: July 2014

Purpose:To develop a systematic approach to generate three dimensional (3D) strain maps of lung using the displacement vector field (DVF) during the respiratory deformation, and to demonstrate its application in evaluating deformable image registration (DIR).Methods:A DVF based strain tensor at each voxel of interest (VOI) was calculated from the relative displacements between the VOI and each of the six nearest neighbors. The maximum and minimum stretches of a VOI can be determined by the principal strains (E1, E2 and E3), which are the eigenvalues and the corresponding strain tensors. Two healthy volunteers enrolled in this study under IRB‐approved protocol, each was scanned using 3D Hyperpolarized He‐3 tagging‐MRI and 3D proton‐MRI with TrueFISP sequence at the endof‐ inhalation (EOI) and the end‐of‐exhalation (EOE) phases. 3D DVFs of tagging‐ and proton‐MRI were obtained by the direct measurements of the tagging grid trajectory and by the DIR method implemented in commercial software.Results:3D strain maps were successfully generated for all DVFs. The principal strain E1s were calculated as 0.43±0.05 and 0.17±0.25 for tagging‐MRI and proton‐MRI, respectively. The large values of E1 indicate the predominant lung motion in the superior‐inferior (SI) direction. Given that the DVFs from the tagging images are considered as the ground truth, the discrepancies in the DIR‐based strain maps suggest the inaccuracy of the DIR algorithm. In the E1 maps of tagging‐MRI for subject 1, the fissures were distinguishable by the larger values (0.49±0.02) from the adjacent tissues (0.41±0.03) due to the larger relative displacement between the lung lobes.Conclusion:We have successfully developed a methodology to generate DVF‐based 3D strain maps of lung. It can potentially enable us to better understand the pulmonary biomechanics and to evaluate and improve the DIR algorithms for the lung deformation. We are currently studying more subjects to evaluate this tool.
MO‐C‐17A‐13: Uncertainty Evaluation of CT Image Deformable Registration for H&N Cancer Adaptive Radiotherapy
Authors: Qin A , Yan D 
Published: July 2014

Purpose:To evaluate uncertainties of organ specific Deformable Image Registration (DIR) for H&N cancer Adaptive Radiation Therapy (ART).Methods:A commercial DIR evaluation tool, which includes a digital phantom library of 8 patients, and the corresponding “Ground truth Deformable Vector Field” (GT‐DVF), was used in the study. Each patient in the phantom library includes the GT‐DVF created from a pair of CT images acquired prior to and at the end of the treatment course. Five DIR tools, including 2 commercial tools (CMT1, CMT2), 2 in‐house (IH_FFD1, IH_FFD2), and a classic DEMON algorithms, were applied on the patient images. The resulting DVF was compared to the GT‐DVF voxel by voxel. Organ specific DVF uncertainty was calculated for 10 ROIs: Whole Body, Brain, Brain Stem, Cord, Lips, Mandible, Parotid, Esophagus and Submandibular Gland. Registration error‐volume histogram was constructed for comparison.Results:The uncertainty is relatively small for brain stem, cord and lips, while large in parotid and submandibular gland. CMT1 achieved best overall accuracy (on whole body, mean vector error of 8 patients: 0.98±0.29 mm). For brain, mandible, parotid right, parotid left and submandibular glad, the classic Demon algorithm got the lowest uncertainty (0.49±0.09, 0.51±0.16, 0.46±0.11, 0.50±0.11 and 0.69±0.47 mm respectively). For brain stem, cord and lips, the DVF from CMT1 has the best accuracy (0.28±0.07, 0.22±0.08 and 0.27±0.12 mm respectively). All algorithms have largest right parotid uncertainty on patient#7, which has image artifact caused by tooth implantation.Conclusion:Uncertainty of deformable CT image registration highly depends on the registration algorithm, and organ specific. Large uncertainty most likely appears at the location of soft‐tissue organs far from the bony structures. Among all 5 DIR methods, the classic DEMON and CMT1 seem to be the best to limit the uncertainty within 2mm for all OARs.Partially supported by research grant from Elekta.
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