bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐PIS‐Exhibit Hall‐01: Tools for TG‐142 Linac Imaging QA II
Authors: Childress N , Murray B 
Published: July 2014

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands‐on” information about the equipment and software systems that we use in our clinics. The therapy topic this year is solutions for TG‐142 recommendations for linear accelerator imaging QA. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors.Using DoseLab to Perform TG‐142 Imaging QAThe goals of this session will be to present a clinical overview of acquiring images for TG‐142 Imaging QA, as well as analyzing and evaluating results using DoseLab software. DoseLab supports planar imaging QA analysis using almost any QA phantom provided by numerous vendors. General advantages and disadvantages of selecting each of these phantoms will be briefly summarized. Best practices for selecting image acquisition parameters will be presented. A demonstration of using DoseLab software to perform a series of TG‐142 tests will be performed. We will disuss why DoseLab uses its own set of imaging QA formulas, and why imaging QA measurement values of the same nominal properties will vary between TG‐ 142 software packages. Because TG‐142 does not specify baseline and tolerance values for imaging QA, the presentation will recommend performing the manufacturer's acceptance test procedure to validate the equipment is functioning correctly. Afterwards, results can be obtained using the clinic's selected set of phantoms, image acquisition parameters, and TG‐142 software to set proper baseline values. This presentation will highlight the reasons why comparing imaging QA results can be trickier than comparing linear accelerator treatment results and what physicists should keep in mind when comparing imaging QA results for different machines. Physicists are often unsure of the next step when there is an issue discovered during Imaging QA. Therefore, a few common examples of imaging issues and recommended solutions will be discussed.TG‐142 Imaging QA Simplified: Lessons From Diagnostic Physics CollaborationThere are many commercial options for the performance of imaging quality assurance tests for the linear accelerator imaging systems as required by the AAPM TG‐142 report. Imaging quality assurance testing is largely performed by the diagnostic physicist so routine performance of these tests by the practicing oncology physicist can be complicated and confusing given all of the commercially available options. This presentation focuses on the performance of imaging quality assurance testing for linear accelerators using methods similar to that of diagnostic physicists. This presentation will address the ability to perform the required testing without complicated software and explores solutions for the performance of these tests in an efficient manner while still maintaining the ability to ascertain image quality changes that may ultimately affect clinical decisions.
WE‐E‐19A‐01: Globalization of Medical Physics
Authors: Rehani M , Meghzifene A , Tsapaki V , Padovani R , Pipman Y , Lief E 
Published: July 2014

Following successful 2012–2013 International Professional Symposiums as a part of Annual AAPM meetings, representatives of AAPM and International Organization of Medical Physics (IOMP) suggested to make this tradiational Symposium a permanent part of Annual AAPM meetings in future. Following the tradition, this session includes presentations of representatives of AAPM, IOMP, European Federation of Medical Physics (EFOMP), International Atomic Energy Agency (IAEA) and International Center for Theoretical Physics (ICTP). The speakers will cover various aspects of International collaboration such as educational, professional, and scientific issues, as well as help to developing countries.With further developments of medicine and technology and increased communication with our colleagues overseas, Medical Physics becomes more and more global profession. Use of the same technology, significant progress in medical physics research and developing practical regulations worldwide makes it increasingly useful to organize global collaboration of medical physicists. Several international organizations are tasked to promote such collaboration and provide help to developing countries. Not all AAPM members are fully aware of these international efforts. It is very useful for medical physicists to know about success of our profession in other countries. Different schools present different approaches to the same problem, which allows to find the best solution. By communicating with colleagues overseas, one can learn more than from just reading scientific publications. At this session the attendees will receive a glimpse of International Medical Physics activities.Learning Objectives:

Understand the globalization of Medical Physics profession and advantages of collaboration with foreign colleagues.
See what role AAPM is playing in establishing contacts with colleagues overseas.
Understand the role of IOMP and main directions of its activity.
Learn about IAEA and how it helps developing countries.
Learn about activity of EFOMP and how can help the global development of Medical Physics.
Find out about ICTP and its educational programs.
WE‐E‐BRE‐10: Level of Breast Cancer Stem Cell Correlated with Tumor Radioresistence: An Indication for Individualized Breast Cancer Therapy Adapted to Cancer Stem Cell Fractions
Authors: Qi S , Pajonk F , McCloskey S , Low D , Kupelian P , Steinberg M , Sheng K 
Published: July 2014

Purposes:The presence of cancer stem cells (CSCs) in a solid tumor could result in poor tumor control probability. The purposes are to study CSC radiosensitivity parameters α and β and their correlation to CSC levels to understand the underlying radioresistance mechanisms and enable individualized treatment design.Methods:Four established breast cancer cell lines (MCF‐7, T47D, MDA‐MB‐231, and SUM159PT) were irradiated in vitro using single radiation doses of 0, 2, 4, 6, 8 or 10 Gy. The fractions of CSCs in each cell lines were determined using cancer stem cell markers. Mammosphere assays were also performed to better estimate the number of CSCs and represent the CSC repopulation in a human solid tumor. The measured cell surviving fractions were fitted using the Linear‐quadratic (LQ) model with independent fitting parameters: α_TC, β_TC (TCs), α_CSC, β_CSC (CSCs), and fs (the percentage of CSCs in each sample).Results:The measured fs increased following the irradiation by MCF‐7 (0.1%), T47D (0.9%), MDA‐MB‐231 (1.18%) and SUM159T (2.46%), while decreasing surviving curve slopes were observed, indicating greater radioresistance, in the opposite order. The fitting yielded the radiosensitive parameters for the MCF‐7: α_TC=0.1±0.2Gy−1, β_TC= 0.08 ±0.14Gy−2, α_CSC=0.04±0.07Gy−1, β_CSC =0.02±0.3Gy−2; for the SUM159PT, α_TC=0.08±0.25 Gy−1, β_TC=0.02±0.02Gy−2, α_CSC=0.04±0.18Gy−1, β_CSC =0.004±0.24Gy−2. In the mammosphere assay, where fs were higher than the corresponding cell line assays, there was almost no shoulder found in the surviving curves (more radioresistant in mammosphere assays) yielding β_CSC of approximately 0.Conclusion:Breast cancer stem cells were more radioresistant characterized by smaller α and β values compared to differentiated breast cancer cells. Percentage of breast cancer stem cells strongly correlated to overall tumor radioresistance. This observation suggested the feasibility of individualized radiotherapy prescription based on the fractions of cancer stem cells found in biopsy.
WE‐E‐18A‐02: Enhancement of Lung Tumor Visibility by Dual‐Energy X‐Ray Imaging in An Anthropomorphic Chest Phantom Study
Authors: Menten MJ , Fast MF , Nill S , Oelfke U 
Published: July 2014

Purpose:Intrafractional lung tumor motion during radiotherapy can be compensated for by tracking the tumor position using x‐ray imaging and adapting the treatment in real‐time. However, locating the tumor with an automated template‐matching algorithm is often challenging if the tumor is obscured by ribs. This study investigates the feasibility of creating dual‐energy (DE) images of the chest with increased tumor visibility on an Elekta XVI system.Methods:An anthropomorphic chest phantom was imaged at two different energies. Low‐energy images were obtained at 80 kVp (0.8 mAs); high‐energy images at 129 kVp (0.6 mAs, additional 1.26 mm tin filter). A Geant4 Monte‐Carlo framework was developed allowing simulation of the x‐ray tube, flat‐panel detector and phantom in order to optimize the beam energies, filtration and the weighting factor used to subtract the individual images into a synthetic DE image. The weighting factor was selected to minimize the visibility of bones while maintaining a sufficient tumor visibility. We scored the bone visibility as the contrast of tumor (with bone) to tumor (without bone), and similarly of lung tissue (with bone) to lung tissue (without bone). Tumor visibility was quantified as the contrast between tumor and lung tissue (both without bone).Results:In the experimentally obtained DE image the bone visibility was reduced by 79.2% in tumor and by 96.8% in lung tissue while the overall tumor visibility only decreased by 69.5%. The Monte‐Carlo simulation yielded similar results reducing the scores by 90.0%, 85.3% and only 71.9%, respectively.Conclusion:This work demonstrates the feasibility of DE imaging to enhance lung tumor detectability. In the future, we hope to further refine the Monte‐Carlo simulation to more accurately predict the weighting factors which would aid real‐time implementation. Furthermore, we plan to use the Monte‐Carlo framework to simulate DE images of actual lung tumors.The authors would like to thank Paul‐Scherrer‐Institut and Centre Suisse d' Electronique et Microtechnique for lending us the anthropomorphic phantom. We acknowledge support from Elekta AB under a research agreement. Research at The Institute of Cancer Research is also supported by Cancer Research UK under Programme C46/A10588.
WE‐E‐18A‐04: Precision In‐Vivo Dosimetry Using Optically Stimulated Luminescence Dosimeters and a Pulsed‐Stimulating Dose Reader
Authors: Chen Q , Herrick A , Hoke S , Burns J 
Published: July 2014

Purpose:A new readout technology based on pulsed optically stimulating luminescence is introduced (microSTARii, Landauer, Inc, Glenwood, IL60425). This investigation searches for approaches that maximizes the dosimetry accuracy in clinical applications.Methods:The sensitivity of each optically stimulated luminescence dosimeter (OSLD) was initially characterized by exposing it to a given radiation beam. After readout, the luminescence signal stored in the OSLD was erased by exposing its sensing area to a 21W white LED light for 24 hours. A set of OSLDs with consistent sensitivities was selected to calibrate the dose reader. Higher order nonlinear curves were also derived from the calibration readings. OSLDs with cumulative doses below 15 Gy were reused. Before an in‐vivo dosimetry, the OSLD luminescence signal was erased with the white LED light.Results:For a set of 68 manufacturer‐screened OSLDs, the measured sensitivities vary in a range of 17.3%. A sub‐set of the OSLDs with sensitivities within ±1% was selected for the reader calibration. Three OSLDs in a group were exposed to a given radiation. Nine groups were exposed to radiation doses ranging from 0 to 13 Gy. Additional verifications demonstrated that the reader uncertainty is about 3%. With an external calibration function derived by fitting the OSLD readings to a 3rd‐order polynomial, the dosimetry uncertainty dropped to 0.5%. The dose‐luminescence response curves of individual OSLDs were characterized. All curves converge within 1% after the sensitivity correction. With all uncertainties considered, the systematic uncertainty is about 2%. Additional tests emulating in‐vivo dosimetry by exposing the OSLDs under different radiation sources confirmed the claim.Conclusion:The sensitivity of individual OSLD should be characterized initially. A 3rd‐order polynomial function is a more accurate representation of the dose‐luminescence response curve. The dosimetry uncertainty specified by the manufacturer is 4%. Following the proposed approach, it can be controlled to 2%.
WE‐G‐18A‐05: Cone‐Beam CT Reconstruction with Deformed Prior Image
Authors: Zhang H , Ouyang L , Huang J , Ma J , Chen W , Wang J 
Published: July 2014

Purpose:Prior image can be incorporated into image reconstruction process to improve the quality of on‐treatment cone‐beam CT (CBCT) from sparseview or low‐dose projections. However, the deformation between the prior image and on‐treatment CBCT are not considered in current prior image based reconstructions (e.g., prior image constrained compressed sensing (PICCS)). The purpose of this work is to develop a deformed‐prior‐imagebased‐ reconstruction strategy (DPIR) to address the mismatch problem between the prior image and target image.Methods:The deformed prior image is obtained by a projection based registration approach. Specifically, the deformation vector fields (DVF) used to deform the prior image is estimated through matching the forward projection of the prior image and the measured on‐treatment projection. The deformed prior image is then used as the prior image in the standard PICCS algorithm. Simulation studies on the XCAT phantom was conducted to evaluate the performance of the projection based registration procedure and the proposed DPIR strategy.Results:The deformed prior image matches the geometry of on‐treatment CBCT closer as compared to the original prior image. Using the deformed prior image, the quality of the image reconstructed by DPIR from few‐view projection data is greatly improved as compared to the standard PICCS algorithm. The relative image reconstruction error is reduced to 11.13% in the proposed DPIR from 17.57% in the original PICCS.Conclusion:The proposed DPIR approach can solve the mismatch problem between the prior image and target image, which overcomes the limitation of the original PICCS algorithm for CBCT reconstruction from sparse‐view or low‐dose projections.
WE‐G‐18C‐02: Estimation of Optimal B‐Value Set for Obtaining Apparent Diffusion Coefficient Free From Perfusion in Non‐Small Cell Lung Cancer
Authors: Karki K , Hugo G , Ford J , Olsen K , Saraiya S , Groves R , Weiss E 
Published: July 2014

Purpose:Diffusion‐weighted MRI (DW‐MRI) is increasingly being investigated for radiotherapy planning and response assessment. Selection of a limited number of b‐values in DW‐MRI is important to keep geometrical variations low and imaging time short. We investigated various b‐value sets to determine an optimal set for obtaining monoexponential apparent diffusion coefficient (ADC) close to perfusion‐insensitive intravoxel incoherent motion (IVIM) model ADC (ADC<sub>IVIM</sub>) in nonsmall cell lung cancer.Methods:Seven patients had 27 DW‐MRI scans before and during radiotherapy in a 1.5T scanner. Respiratory triggering was applied to the echo‐planar DW‐MRI with TR=4500ms approximately, TE=74ms, pixel size=1.98×1.98mm2, slice thickness=4–6mm and 7 axial slices. Diffusion gradients were applied to all three axes producing traceweighted images with eight b‐values of 0–1000μs/μm2. Monoexponential model ADC values using various b‐value sets were compared to ADC<sub>IVIM</sub> using all b‐values. To compare the relative noise in ADC maps, intra‐scan coefficient of variation (CV) of active tumor volumes was computed.Results:ADC<sub>IVIM</sub>, perfusion coefficient and perfusion fraction for tumor volumes were in the range of 880‐1622 μm2/s, 8119‐33834 μm2/s and 0.104–0.349, respectively. ADC values using sets of 250, 800 and 1000; 250, 650 and 1000; and 250–1000μs/μm2 only were not significantly different from ADC<sub>IVIM</sub>(p>0.05, paired t‐test). Error in ADC values for 0–1000, 50–1000, 100–1000, 250–1000, 500–1000, and three b‐value sets‐ 250, 500 and 1000; 250, 650 and 1000; and 250, 800 and 1000μs/μm2 were 15.0, 9.4, 5.6, 1.4, 11.7, 3.7, 2.0 and 0.2% relative to the reference‐standard ADC<sub>IVIM</sub>, respectively. Mean intrascan CV was 20.2, 20.9, 21.9, 24.9, 32.6, 25.8, 25.4 and 24.8%, respectively, whereas that for ADC<sub>IVIM</sub> was 23.3%.Conclusion:ADC values of two 3 b‐value sets (250, 650 and 1000; and 250, 800 and 1000μs/μm2) approached ADC<sub>IVIM</sub>, with relative noise comparable to that of ADC<sub>IVIM</sub>. These sets may be used to obtain perfusion‐insensitive ADC values in lung tumors.E. Weiss: Funding through Varian Medical Systems and Philips Oncology Systems, UpToDate royalties.G. Hugo: NIH R01CA166119, P01 CA116602, NHMRC Project Grant.
WE‐G‐18C‐03: Effects of Breathing Irregularity On Tumor Motion Measurement of 4D‐MRI: A Comparison Between Cine and Sequential Image Acquisition Modes
Authors: Liu Y , Yin F , Rhee D , Cai J 
Published: July 2014

Purpose:We have recently developed a cine‐mode T2*/T1‐weighted 4DMRI technique, and a sequential‐mode T2‐weighted 4D‐MRI for imaging respiratory motion . The latter has better tumor‐contrast due to its T2‐ weighting. This study aims at investigating which 4D‐MRI image acquisition mode, cine or sequential, provides more accurate measurement of respiratory motion.Methods:A 4D Digital Extended Cardiac‐Torso (XCAT) human phantom with a hypothesized tumor was used to simulate image acquisition and 4D‐MRI reconstruction. Tumor was set to move continuously with a given breathing signal. Its trajectories were measured from both sequential‐ and cine‐mode 4D‐MRI. Comparisons of measurements with the average tumor trajectories calculated from the input profile as references were conducted. Absolute tumor motion amplitude differences (D) were determined. A total of 500 simulated respiratory profiles with a wide range of irregularity were used to investigate the relationship between D and Ir. Statistical analysis of D for breathing profiles of 20 real cancer patients were conducted with a sign rank test regarding two modes. Lastly, we investigated the possibility of further improving motion measurement accuracy in sequential‐mode 4D‐MRI by removing data points of high irregularity. An irregularity filter was applied to the same 20 patients. Sign rank tests for D of sequential‐mode with filter and cine‐mode were performed.Results:D increased faster for cine‐mode (D=0.37*Ir) than sequential‐mode (D=0.16*Ir) as irregularity increased. For 20 cancer patients, D was 0.12cm and 0.10cm for cine‐ and sequentialmodes on average; p‐value was 0.0228. Removing highly irregular data points increased accuracy of tumor trajectory for sequential‐mode 4D‐MRI, D was decreased by 10%. P‐value was 0.0065.Conclusion:Tumor motion measurement is more accurate and less susceptible to breathing irregularity in sequential‐mode 4D‐MRI than that in cine‐mode 4D‐MRI; its accuracy could be further improved in sequential‐mode 4D‐MRI by selectively removing data points of high irregularity.NIH (1R21CA165384‐01A1)
WE‐G‐18C‐04: Comparison of Image Registration Strategies to Improve DCE‐MRI Uptake Curves in Esophageal Cancer
Authors: Heethuis S , van Rossum P , Lips I , van Vulpen M , Meijer G , van Lier A 
Published: July 2014

Purpose:Neoadjuvant chemoradiotherapy (nCRT) is often used to treat resectable esophageal cancer. In 29% of the patients, nCRT results in a pathologic complete reponse (pCR). pCR is an important prognostic factor and surgery can potentially be omitted in this patient group. We will study the potential of DCE‐MRI for accurate response assessment in those patients. In preparation to this study, motion compensation by retrospective image registration strategies of DCE‐MRI time series was investigated to enable correct pharmacokinetic analysis required for response assessment.Methods:Five patients with biopsy‐proven esophageal cancer received a DCE‐MRI scan (62 time‐frames, 3 s/frame) prior to neoadjuvant therapy; contrast was injected after the 10th time‐frame. Three different registration strategies were reviewed; registration to: (1) 1st time‐frame, (2) 41th timeframe, and (3) previous time‐frame; rigid or affine transformations were allowed (using Elastix toolbox). We chose these time‐points as they occur well before (1) or after (2) contrast uptake. In strategy (3), the registration process is thought to be less susceptible to image intensity variations caused by contrast uptake. The effectiveness of the registration strategies was measured with SSIM (structure component of Structural SIMilarity, 1 = perfect match) metric within the volume encompassing the tumor. The 41th frame was chosen as reference image.Results:The average SSIM over all time‐frames and patients was: pre‐registration: 0.67, post‐registration rigid to 1st/41th/previous frame: 0.68/0.70/0.66, affine: 0.68/0.69/0.66. For rigid registration to frame 41, the minimum and maximum SSIM change per time‐frame ranged from −0.09 to 0.14.Conclusion:In this analysis, rigid registration to a time‐frame after contrast‐enhancement led to the best retrospective motion compensation of DCE‐MRI in esophageal cancer measured by SSIM. After verification in a larger patient group, this correction will be used prior to semi‐quantitative analysis and pharmacokinetic modeling to assess response to neoadjuvant therapy.
WE‐G‐18C‐05: Characterization of Cross‐Vendor, Cross‐Field Strength MR Image Intensity Variations
Authors: Paulson E , Prah D 
Published: July 2014

Purpose:Variations in MR image intensity and image intensity nonuniformity (IINU) can challenge the accuracy of intensity‐based image segmentation and registration algorithms commonly applied in radiotherapy. The goal of this work was to characterize MR image intensity variations across scanner vendors and field strengths commonly used in radiotherapy.Methods:ACR‐MRI phantom images were acquired at 1.5T and 3.0T on GE (450w and 750, 23.1), Siemens (Espree and Verio, VB17B), and Philips (Ingenia, 4.1.3) scanners using commercial spin‐echo sequences with matched parameters (TE/TR: 20/500 ms, rBW: 62.5 kHz, TH/skip: 5/5mm). Two radiofrequency (RF) coil combinations were used for each scanner: body coil alone, and combined body and phased‐array head coils. Vendorspecific B1‐ corrections (PURE/Pre‐Scan Normalize/CLEAR) were applied in all head coil cases. Images were transferred offline, corrected for IINU using the MNI N3 algorithm, and normalized. Coefficients of variation (CV=σ/μ) and peak image uniformity (PIU = 1−(Smax−Smin)/(Smax+Smin)) estimates were calculated for one homogeneous phantom slice. Kruskal‐Wallis and Wilcoxon matched‐pairs tests compared mean MR signal intensities and differences between original and N3 image CV and PIU.Results:Wide variations in both MR image intensity and IINU were observed across scanner vendors, field strengths, and RF coil configurations. Applying the MNI N3 correction for IINU resulted in significant improvements in both CV and PIU (p=0.0115, p=0.0235). However, wide variations in overall image intensity persisted, requiring image normalization to improve consistency across vendors, field strengths, and RF coils. These results indicate that B1‐ correction routines alone may be insufficient in compensating for IINU and image scaling, warranting additional corrections prior to use of MR images in radiotherapy.Conclusions:MR image intensities and IINU vary as a function of scanner vendor, field strength, and RF coil configuration. A two‐step strategy consisting of MNI N3 correction followed by normalization was required to improve MR image consistency.Funding provided by Advancing a Healthier Wisconsin.