bluuebunny/crossref_metadata_2025_split huggingface dataset:
MO‐FG‐BRA‐07: Theranostic Gadolinium‐Based AGuIX Nanoparticles for MRI‐Guided Radiation Therapy
Authors: Detappe A , Rottmann J , Kunjachan S , Tillement O , Berbeco R 
Published: June 2015

Purpose:AGuIX are gadolinium‐based nanoparticles, initially developed for MRI, that have a potential role in radiation therapy as a radiosensitizer. Our goal is to demonstrate that these nanoparticles can both be used as an MRI contrast agent, as well as to obtain local dose enhancement in a pancreatic tumor when delivered in combination with an external beam irradiation.Methods:We performed in vitro cell uptake and radiosensitization studies of a pancreatic cancer cell line in a low energy (220kVp) beam, a standard clinical 6MV beam (STD) and a flattening filter free clinical 6MV beam (FFF). After injection of 40mM of nanoparticles, a biodistribution study was performed in vivo on mice with subcutaneous xenograft pancreatic tumors. In vivo radiation therapy studies were performed at the time point of maximum tumor uptake.Results:The concentration of AGuIX nanoparticles in Panc‐1 pancreatic cancer cells, determined in vitro by MRI and ICPMS, peaks after 30 minutes with 0.3% of the initial concentration (5mg/g). Clonogenic assays show a significant effect (p<0.05) when the AGuIX are coupled with MV photon irradiation (DEF20%=1.31). Similar AGuIX tumor uptake is found in vivo by both MRI and ICPMS 30 minutes after intravenous injection. For long term survival studies, the choice of the radiation dose is determined with 5 control groups (3mice/group) irradiated with 0, 5, 10, 15, and 20Gy. Afterwards, 4 groups (8mice/group) are used to evaluate the effect of the nanoparticles. A Logrank test is performed as a statistical test to evaluate the effect of the nanoparticles.Conclusion:The combination of the MRI contrast and radiosensitization properties of gadolinium nanoparticles reveals a strong potential for usage with MRI‐guided radiation therapy.
MO‐FG‐BRA‐09: Quantification of Nanoparticle Heating and Concentration for MR‐Guided Laser Interstitial Thermal Therapy
Authors: MacLellan CJ , Melancon M , Salatan F , Yang Q , Hwang KP , Fuentes D , Stafford RJ 
Published: June 2015

Purpose:Nanoparticle Mediated Laser Interstitial Thermal Therapy (npLITT) is a technique that utilizes tumor localized optically activated nanoparticles to increase the conformality of laser ablation procedures. Temperatures in these procedures are dependent on the particle concentration which generally cannot be measured noninvasively prior to therapy. In this work we attempt to quantify particle concentration in vivo by estimating the increase in R2* relaxation induced by bifunctional magnetic resonance (MR)‐visible gold‐based nanoparticles (SPIO@Au) and relate it to the temperature increase observed during real time MR temperature imaging (MRTI) of laser ablation.Methods:SPIO@Au nanoparticles (90nm) were synthesized containing a silica‐iron core (for MR visibility via R2*) and gold shell (for near‐infrared absorption). High resolution R2* maps were acquired before and after injecting four different particle concentrations (saline,1e10, 5e10, and 10e10 particles/mL) into HN5 flank xenografts. Tumors were monitored using MRTI during treatment with an interstitial fiber. (1 watt, 808 nm, 3 minutes)Results:The maximum temperature within the tumors increased linearly with concentration of injected particles, reaching 34.0, 37.6, 45.8, and 55.4 ⁰C for saline, 1e10, 5e10 and 10e10 particles/mL injections, respectively (R2=.994). The highest temperatures occur at the injection site rather than the fiber, confirming that SPIO@Au nanoparticles are the primary absorber. The differences between the median R2* measured at the injection site and the rest of the tumor were −6, 134, 111, 156 s‐1 for the saline,1e10,5e10 and 10e10 particles/mL injections, respectively. This R2* change is consistent with the measured relaxivity for the 1e10 particles/mL injection but does not maintain linearity at higher concentrations.Conclusion:Bifunctional SPIO@Au nanoparticles are a promising technology for providing noninvasive estimates of particle concentration via MRI and temperature increase in npLITT procedures. Future experiments will focus on lower, physiologically relevant particle concentrations and spin echo R2 mapping to better quantify the particle concentration.This research was supported by the National Institutes of Health and National Cancer Institute under Award Numbers TL1TR000369 and P30CA016672 and was conducted at the MD Anderson Center for Advanced Biomedical Imaging in‐part with equipment support from General Electric Healthcare.
MO‐FG‐303‐02: BEST IN PHYSICS (THERAPY): Cherenkov Emission Dosimetry: Feasibility for Electron Radiotherapy
Authors: Zlateva Y , El Naqa I 
Published: June 2015

Purpose:To investigate from first principles, corroborated by Monte Carlo simulations and experimental measurements, the feasibility of developing a relative Cherenkov emission (CE) dosimetry protocol for electron beam radiotherapy.Methods:Monte Carlo (MC) simulations of mono‐energetic electrons incident on water were carried out in Geant4. Percent depth Cherenkov emission (PDCE) and dose (PDD) distributions were scored for incidence energies of 4, 6, 9, 12, 15, and 18 MeV. PDCE‐to‐PDD analytical conversion models were developed from least‐squares data fits generated for PDD as a function of PDCE at the same depth and at different depths. Experimental techniques for validation of these models are examined.Results:Same‐depth PDD versus PDCE data fits indicate that although the relationship is linear to first order (correlation r > 0.9 for all energies), it is much more accurately approximated by separate linear and quadratic models for the build‐up and drop‐off regions, respectively (r > 0.999), which is theoretically underpinned. To understand the source of this relationship and its basis for developing robust conversion models, an approximate quadratic first‐principles model was derived and found in agreement with MC/measured data (20% deviation at worst). Conversely, data fits of PDD versus different‐depth PDCE unveiled a depth‐invariant effective point of measurement of 1.5–2.1 mm downstream with 4–18 MeV incidence, respectively (r > 0.999 in the drop‐off region). We present an analytical first‐principles justification for this shift. This method led to errors of <1% in drop‐off region PDD (<2% for PDD<20% with 4 MeV incidence) and <0.2 mm in practical range prediction.Conclusion:We present robust quantitative prediction models, derived from first‐principles and supported by simulation and measurement, for relative dose from Cherenkov emission by high‐energy electrons. This constitutes a major step towards development of protocols for routine clinical quality assurance as well as real‐time in vivo Cherenkov dosimetry in radiotherapy.The authors acknowledge partial support by Fonds de recherche du Quebec ‐ Nature et technologies (FRQNT), CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (NSERC), CREATE Integrated Sensor Systems grant of NSERC, the Canadian Institutes of Health Research (CIHR), and NSERC.
MO‐FG‐303‐04: A Smartphone Application for Automated Mechanical Quality Assurance of Medical Accelerators
Authors: Kim H , Lee H , Choi K , Ye S 
Published: June 2015

Purpose:The mechanical quality assurance (QA) of medical accelerators consists of a time consuming series of procedures. Since most of the procedures are done manually – e.g., checking gantry rotation angle with the naked eye using a level attached to the gantry –, it is considered to be a process with high potential for human errors. To remove the possibilities of human errors and reduce the procedure duration, we developed a smartphone application for automated mechanical QA.Methods:The preparation for the automated process was done by attaching a smartphone to the gantry facing upward. For the assessments of gantry and collimator angle indications, motion sensors (gyroscope, accelerator, and magnetic field sensor) embedded in the smartphone were used. For the assessments of jaw position indicator, cross‐hair centering, and optical distance indicator (ODI), an optical‐image processing module using a picture taken by the high‐resolution camera embedded in the smartphone was implemented. The application was developed with the Android software development kit (SDK) and OpenCV library.Results:The system accuracies in terms of angle detection error and length detection error were < 0.1° and < 1 mm, respectively. The mean absolute error for gantry and collimator rotation angles were 0.03° and 0.041°, respectively. The mean absolute error for the measured light field size was 0.067 cm.Conclusion:The automated system we developed can be used for the mechanical QA of medical accelerators with proven accuracy. For more convenient use of this application, the wireless communication module is under development. This system has a strong potential for the automation of the other QA procedures such as light/radiation field coincidence and couch translation/rotations.
MO‐FG‐204‐01: Improved Noise Suppression for Dual‐Energy CT Through Entropy Minimization
Authors: Petrongolo M , Zhu L 
Published: June 2015

Purpose:In dual energy CT (DECT), noise amplification during signal decomposition significantly limits the utility of basis material images. Since clinically relevant objects contain a limited number of materials, we propose to suppress noise for DECT based on image entropy minimization. An adaptive weighting scheme is employed during noise suppression to improve decomposition accuracy with limited effect on spatial resolution and image texture preservation.Methods:From decomposed images, we first generate a 2D plot of scattered data points, using basis material densities as coordinates. Data points representing the same material generate a highly asymmetric cluster. We orient an axis by minimizing the entropy in a 1D histogram of these points projected onto the axis. To suppress noise, we replace pixel values of decomposed images with center‐of‐mass values in the direction perpendicular to the optimal axis. To limit errors due to cluster overlap, we weight each data point's contribution based on its high and low energy CT values and location within the image. The proposed method's performance is assessed on physical phantom studies. Electron density is used as the quality metric for decomposition accuracy. Our results are compared to those without noise suppression and with a recently developed iterative method.Results:The proposed method reduces noise standard deviations of the decomposed images by at least one order of magnitude. On the Catphan phantom, this method greatly preserves the spatial resolution and texture of the CT images and limits induced error in measured electron density to below 1.2%. In the head phantom study, the proposed method performs the best in retaining fine, intricate structures.Conclusion:The entropy minimization based algorithm with adaptive weighting substantially reduces DECT noise while preserving image spatial resolution and texture. Future investigations will include extensive investigations on material decomposition accuracy that go beyond the current electron density calculations.This work was supported in part by the National Institutes of Health (NIH) under Grant Number R21 EB012700.
MO‐FG‐204‐07: Nonlocal Total Variation Based Spectral CT Image Reconstruction
Authors: Liu J , Ding H , Molloi S , Zhang X , Gao H 
Published: June 2015

Purpose:When divided into narrow energy bins, the projection data for spectral CT contains spectral information with reduced signal‐to‐noise ratio (SNR). When used together, the total projection data has improved SNR without spectral information. The purpose of this work is to develop an image reconstruction method that maximizes the utility of spectral data in terms of synergized spectral information and SNR.Methods:The proposed method is based on nonlocal total variation (TV) (NLTV). That is, we first reconstruct a spectrally averaged image using total projection data without energy binning, and build the NLTV weights from this image that characterize the nonlocal image feature with good SNR, such as texture and fine structures. Then these NLTV weights are incorporated into a NLTV‐based iterative spectral image reconstruction scheme using the binned projection data with reduced SNR, so that the weights serve as the reference to regularize the image quality during spectral image reconstruction. In addition, the spectral reconstruction incorporates the material decomposition by directly reconstructing material compositions instead of the conventional approach, that separates image reconstruction and material decomposition. The spectral image reconstruction is formulated as an iterative reconstruction method with the NLTV regularization. The split Bergman method is developed for solving the above sparse optimization problem.Results:The proposed NLTV‐based method was validated using both simulated and experimental data. In comparison with FBP and TV‐ based method, NLTV had improved image quality, e.g., contrast‐to‐noise ratio (CNR) and modulation transfer function (MTF).Conclusion:NLTV‐based spectral CT image reconstruction is developed that maximizes the utility of spectral data in terms of synergized spectral information, and it offers significant improvement in CNR without scarifying spatial resolution of the image.Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500).
MO‐FG‐204‐09: High Spatial Resolution and Artifact‐Free CT Bone Imaging at Off‐Centered Positions: An Application of Model‐Based Iterative Reconstruction
Authors: Chen G , Li K , Budde A , Gomez‐Cardona D , Hsieh J 
Published: June 2015

Purpose:Although the anatomy of interest should be positioned as close as possible to the isocenter of CT scanners, off‐centering may be inevitable during certain exams in clinical practice such as lumbar spine and elbow imaging. Off‐centering degrades image sharpness, generates streak artifacts, and sometimes creates blooming artifacts due to truncation. The purpose of this work was to investigate whether the use of model‐based image reconstruction (MBIR) can alleviate the negative impacts of off‐centering to achieve high quality CT bone imaging.Methods:Both an anthropomorphic phantom and an ex vivo swine elbow sample were scanned at centered and off‐centered positions using clinical CT bone scan protocols. The magnitude of off‐centering was determined from localizer radiographs. Both FBP and MBIR reconstructions were performed. For FBP, both standard and Bone Plus kernels commonly used in bone imaging were used. Objective assessment of image sharpness, noise standard deviation, and noise nonuniformity were performed. Additionally, we retrospectively analyzed human subject data acquired under off‐centered conditions as a validation study.Results:In FBP images of the phantom, off‐centering by 10 cm led to a 14% increase in noise (p<1e‐3) and a 68% increase in noise nonuniformity (p<0.02). A visible drop in bone sharpness was observed. In contrast, no significant difference in the noise magnitude or the noise nonuniformity between the centered and off‐centered MBIR images was found. The image sharpness of off‐centered MBIR images outperformed that of FBP images reconstructed with the Bone Plus kernel. In images of the swine elbow off‐centered by 20 cm, not only was the noise and spatial resolution performance improved by MBIR, truncation artifacts were also elliminated. The human subject study generated similar results, in which the visibility of the off‐centered lumbar spine was significantly improved.Conclusion:High quality CT bone imaging at off‐centered positions can be achieved using MBIR.This work was partially supported by an NIH grant R01CA169331 and GE Healthcare. K. Li, D. Gomez‐Cardona: Nothing to disclose. G.‐H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX. A. Budde, J. Hsieh: Employee, GE Healthcare.
MO‐FG‐210‐00: US Guided Systems for Brachytherapy
Authors: Benedict Stanley 
Published: June 2015

Ultrasound (US) is one of the most widely used imaging modalities in medical practice. Since US imaging offers real‐time imaging capability, it has becomes an excellent option to provide image guidance for brachytherapy (IGBT). (1) The physics and the fundamental principles of US imaging are presented, and the typical steps required to commission an US system for IGBT is provided for illustration. (2) Application of US for prostate HDR brachytherapy, including partial prostate treatments using MR‐ultrasound co‐registration to enable a focused treatment on the disease within the prostate is also presented. Prostate HDR with US image guidance planning can benefit from real time visualization of the needles, and fusion of the ultrasound images with T2 weighted MR allows the focusing of the treatment to the specific areas of disease within the prostate, so that the entire gland need not be treated. Finally, (3) ultrasound guidance for an eye plaque program is presented. US can be a key component of placement and QA for episcleral plaque brachytherapy for ocular cancer, and the UCLA eye plaque program with US for image guidance is presented to demonstrate the utility of US verification of plaque placement in improving the methods and QA in episcleral plaque brachytherapy.Learning Objectives:

To understand the physics of an US system and the necessary aspects of commissioning US for image guided brachytherapy (IGBT).
To understand real time planning of prostate HDR using ultrasound, and its application in partial prostate treatments using MR‐ultrasound fusion to focus treatment on disease within the prostate.
To understand the methods and QA in applying US for localizing the target and the implant during a episcleral plaque brachytherapy procedures.
MO‐F‐CAMPUS‐T‐01: A Comparison of Two Risk Analysis Techniques for Surface Image Guided Radiosurgery
Authors: Kim G , Manger R , Pawlicki T 
Published: June 2015

Purpose:Failure Modes and Effects Analysis (FMEA) techniques have been used to analyze surface image guided radiosurgery (SIG‐RS). Hazard model, a modified FMEA approach developed by the Dutch, is applied to SIG‐RS risk assessment and evaluated against the AAPM's FMEA approach.Methods:The SAFER approach uses a risk inventory matrix to categorize hazards (rather than probabilities). A multidisciplinary team was assembled to create the process map of SIG‐RS and 91 steps and 167 failure modes were determined. Each failure mode was categorized for frequency (weekly, monthly, quarterly, yearly and less than once a year) and severity (negligible, minor, moderate, major and catastrophic) according to the SAFER procedures. All failure modes are placed in the matrix of arbitrary risk score matrix: very high, high, low, and very low. The top 14 high risk failure modes from the Result of FMEA and SAFER analysis were compared.Results:167 failure modes categorized in the risk inventory matrix with 1 very high, 13 high, 66 low and 87 very low. Comparison of top 14 high risk failure modes between two techniques shows 9 common failure modes and 5 isolated failure modes. Two failure modes (FM: 58, 145) with the highest risk priority number (both RPN=288) in FMEA are also ranked as high risk in SAFER analysis. However one failure mode (FM: 154) with very high risk score in SAFER is not recognized by FMEA analysis due to its low “lack of detectability” score.Conclusion:FMEA is a well‐established technique for prospective risk analysis. SAFER is a practical alternative that is easy to implement with a reliable category structure. Also the risk inventory matrix is conceptually straightforward to obtain agreement among multidisciplinary team members but still demonstrates a full scale of criticality.
MO‐F‐CAMPUS‐T‐04: Implementation of a Standardized Monthly Quality Check for Linac Output Management in a Large Multi‐Site Clinic
Authors: Xu H , Yi B , Prado K 
Published: June 2015

Purpose:This work is to investigate the feasibility of a standardized monthly quality check (QC) of LINAC output determination in a multi‐site, multi‐LINAC institution. The QC was developed to determine individual LINAC output using the same optimized measurement setup and a constant calibration factor for all machines across the institution.Methods:The QA data over 4 years of 7 Varian machines over four sites, were analyzed. The monthly output constancy checks were performed using a fixed source‐to‐chamber‐distance (SCD), with no couch position adjustment throughout the measurement cycle for all the photon energies: 6 and 18MV, and electron energies: 6, 9, 12, 16 and 20 MeV. The constant monthly output calibration factor (Nconst) was determined by averaging the machines’ output data, acquired with the same monthly ion chamber. If a different monthly ion chamber was used, Nconst was then re‐normalized to consider its different NDW,Co‐60. Here, the possible changes of Nconst over 4 years have been tracked, and the precision of output results based on this standardized monthly QA program relative to the TG‐51 calibration for each machine was calculated. Any outlier of the group was investigated.Results:The possible changes of Nconst varied between 0–0.9% over 4 years. The normalization of absorbed‐dose‐to‐water calibration factors corrects for up to 3.3% variations of different monthly QA chambers. The LINAC output precision based on this standardized monthly QC relative to the TG‐51 output calibration is within 1% for 6MV photon energy and 2% for 18MV and all the electron energies. A human error in one TG‐51 report was found through a close scrutiny of outlier data.Conclusion:This standardized QC allows for a reasonably simplified, precise and robust monthly LINAC output constancy check, with the increased sensitivity needed to detect possible human errors and machine problems.
MO‐F‐CAMPUS‐J‐03: Sorting 2D Dynamic MR Images Using Internal Respiratory Signal for 4D MRI
Authors: Wen Z , Hui C , Stemkens B , Tijssen R , van den Berg C , Beddar S 
Published: June 2015

Purpose:To develop a novel algorithm to extract internal respiratory signal (IRS) for sorting dynamic magnetic resonance (MR) images in order to achieve four‐dimensional (4D) MR imaging.Methods:Dynamic MR images were obtained with the balanced steady state free precession by acquiring each two‐dimensional sagittal slice repeatedly for more than one breathing cycle. To generate a robust IRS, we used 5 different representative internal respiratory surrogates in both the image space (body area) and the Fourier space (the first two low‐frequency phase components in the anterior‐posterior direction, and the first two low‐frequency phase components in the superior‐inferior direction). A clustering algorithm was then used to search for a group of similar individual internal signals, which was then used to formulate the final IRS. A phantom study and a volunteer study were performed to demonstrate the effectiveness of this algorithm. The IRS was compared to the signal from the respiratory bellows.Results:The IRS computed by our algorithm matched well with the bellows signal in both the phantom and the volunteer studies. On average, the normalized cross correlation between the IRS and the bellows signal was 0.97 in the phantom study and 0.87 in the volunteer study, respectively. The average difference between the end inspiration times in the IRS and bellows signal was 0.18 s in the phantom study and 0.14 s in the volunteer study, respectively. 4D images sorted based on the IRS showed minimal mismatched artifacts, and the motion of the anatomy was coherent with the respiratory phases.Conclusion:A novel algorithm was developed to generate IRS from dynamic MR images to achieve 4D MR imaging. The performance of the IRS was comparable to that of the bellows signal. It can be easily implemented into the clinic and potentially could replace the use of external respiratory surrogates.This research was partially funded by the the Center for Radiation Oncology Research from UT MD Anderson Cancer Center.