bluuebunny/crossref_metadata_2025 huggingface dataset:
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.
MO‐F‐CAMPUS‐J‐04: Tissue Segmentation‐Based MR Electron Density Mapping Method for MR‐Only Radiation Treatment Planning of Brain
Authors: Yu H , Lee Y , Ruschin M , Karam I , Sahgal A
Published: June 2015

Purpose:

Automatically derive electron density of tissues using MR images and generate a pseudo‐CT for MR‐only treatment planning of brain tumours.

Methods:

20 stereotactic radiosurgery (SRS) patients’ T1‐weighted MR images and CT images were retrospectively acquired. First, a semi‐automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution. Next, soft tissues ‐ muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo‐CT by linearly fitting their relative intensity histograms. Co‐registered CT was used as a ground truth. The bone segmentations of pseudo‐CT were compared with those of co‐registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo‐CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated.

Results:

The average of distances between voxels on external skull from pseudo‐CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively.

Conclusion:

The semi‐automated MR electron density mapping technique was developed using T1‐weighted MR images. The generated pseudo‐CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR‐only treatment planning.
MO‐F‐CAMPUS‐I‐01: Accuracy of Radiologists Interpretation of Mammographic Breast Density
Authors: Vedantham S , Shi L , Karellas A , O'Connell A
Published: June 2015

Purpose:

Several commercial and non‐commercial software and techniques are available for determining breast density from mammograms. However, where mandated by law the breast density information communicated to the subject/patient is based on radiologist's interpretation of breast density from mammograms. Several studies have reported on the concordance among radiologists in interpreting mammographic breast density. In this work, we investigated the accuracy of radiologist's interpretation of breast density.

Methods:

Volumetric breast density (VBD) determined from 134 unilateral dedicated breast CT scans from 134 subjects was considered the truth. An MQSA‐qualified study radiologist with more than 20 years of breast imaging experience reviewed the DICOM “for presentation” standard 2‐view mammograms of the corresponding breasts and assigned BIRADS breast density categories. For statistical analysis, the breast density categories were dichotomized in two ways; fatty vs. dense breasts where “fatty” corresponds to BIRADS breast density categories A/B, and “dense” corresponds to BIRADS breast density categories C/D, and extremely dense vs. fatty to heterogeneously dense breasts, where extremely dense corresponds to BIRADS breast density category D and BIRADS breast density categories A through C were grouped as fatty to heterogeneously dense breasts. Logistic regression models (SAS 9.3) were used to determine the association between radiologist's interpretation of breast density and VBD from breast CT, from which the area under the ROC (AUC) was determined.

Results:

Both logistic regression models were statistically significant (Likelihood Ratio test, p<0.0001). The accuracy (AUC) of the study radiologist for classification of fatty vs. dense breasts was 88.4% (95% CI: 83–94%) and for classification of extremely dense breast was 94.3% (95% CI: 90–98%).

Conclusion:

The accuracy of the radiologist in classifying dense and extremely dense breasts is high. Considering the variability in VBD estimates from commercial software, the breast density information communicated to the patient should be based on radiologist's interpretation.

This work was supported in part by NIH R21 CA176470 and R21 CA134128. The contents are solely the responsibility of the authors and do not reflect the official views of the NIH or NCI.
MO‐F‐CAMPUS‐I‐03: Tissue Equivalent Material Phantom to Test and Optimize Coherent Scatter Imaging for Tumor Classification
Authors: Albanese K , Morris R , Lakshmanan M , Greenberg J , Kapadia A
Published: June 2015

Purpose:

To accurately model different breast geometries using a tissue equivalent phantom, and to classify these tissues in a coherent x‐ray scatter imaging system.

Methods:

A breast phantom has been designed to assess the capability of coded aperture coherent x‐ray scatter imaging system to classify different types of breast tissue (adipose, fibroglandular, tumor). The tissue‐equivalent phantom was modeled as a hollow plastic cylinder containing multiple cylindrical and spherical inserts that can be positioned, rearranged, or removed to model different breast geometries. Each enclosure can be filled with a tissue‐equivalent material and excised human tumors. In this study, beef and lard, placed inside 2‐mm diameter plastic Nalgene containers, were used as surrogates for fibroglandular and adipose tissue, respectively. The phantom was imaged at 125 kVp, 40 mA for 10 seconds each with a 1‐mm pencil beam. The raw data were reconstructed using a model‐based reconstruction algorithm and yielded the location and form factor, or momentum transfer (q) spectrum of the materials that were imaged. The measured material form factors were then compared to the ground truth measurements acquired by x‐ray diffraction (XRD) imaging.

Results:

The tissue equivalent phantom was found to accurately model different types of breast tissue by qualitatively comparing our measured form factors to those of adipose and fibroglandular tissue from literature. Our imaging system has been able to define the location and composition of the various materials in the phantom.

Conclusion:

This work introduces a new tissue equivalent phantom for testing and optimization of our coherent scatter imaging system for material classification. In future studies, the phantom will enable the use of a variety of materials including excised human tissue specimens in evaluating and optimizing our imaging system using pencil‐ and fan‐beam geometries.

United States Department of Homeland Security Duke University Medical Center ‐ Department of Radiology Carl E Ravin Advanced Imaging Laboratories Duke University Medical Physics Graduate Program
MO‐F‐CAMPUS‐I‐05: The Total Internal Reflection Based Elastography Method for Tissue Elasticity Quantification and Characterization
Authors: Lee J
Published: June 2015

Purpose:

Diagnosing early formation of tumors or lumps, particularly those caused by cancer, has been a problem. Our goal is to develop a simple and easy to use system that can document the properties of palpable lumps in soft tissue using noninvasive technique.

Methods:

The tactile sensor we propose comprises of an elastic optical waveguide unit, a high resolution CCD camera unit, a LED light source unit, and a laptop computer. The sensing element is formed on poly dimethylsiloxane (PDMS) and is illuminated along its four edges by LED light sources. The tactile sensor operates on the principle of total internal reflection (TIR) within an optical waveguide. The light directed into the waveguide remains within it due to the TIR generated, since the waveguide is surrounded by air having a lower refractive index than the waveguide. When an object adheres to the waveguide and compresses, the contact area of the waveguide deforms and causes the light to scatter.

Results:

To validate the proposed method, three patients presented with a lesion that was initially detected by another modality (mammography, ultrasound, or manual palpation). When performing the sensor scans, the doctor already knew where the lesions were located. For each lesion, 10 tactile images were obtained and the estimated parameters were averaged. Regarding the hardness estimation of the lesions, malignant breast lesion of the patient 1 had increased Young's modulus (146 kPa), compared to benign lesions (97 kPa and 103 kPa, patient 2 and 3). The elasticity information was correlated with the malignancy data from the pathology reports. The minimum and maximum relative errors are 1.1% and 11.72%. The mean error is 5.07% with 3.39% standard deviation.

Conclusion:

In this paper, a tactile elasticity imaging sensor using the total internal reflection principle is designed and experimentally evaluated.
MO‐F‐CAMPUS‐T‐03: Continuous Dose Delivery with Gamma Knife Perfexion
Authors: Ghobadi  , Aleman D , Li W , Chung C , Jaffray D
Published: June 2015

Purpose:

We propose continuous dose delivery techniques for stereotactic treatments delivered by Gamma Knife Perfexion using inverse treatment planning system that can be applied to various tumour sites in the brain. We test the accuracy of the plans on Perfexion's planning system (GammaPlan) to ensure the obtained plans are viable. This approach introduces continuous dose delivery for Perefxion, as opposed to the currently employed step‐and‐shoot approaches, for different tumour sites. Additionally, this is the first realization of automated inverse planning on GammaPlan.

Methods:

The inverse planning approach is divided into two steps of identifying a quality path inside the target, and finding the best collimator composition for the path. To find a path, we select strategic regions inside the target volume and find a path that visits each region exactly once. This path is then passed to a mathematical model which finds the best combination of collimators and their durations. The mathematical model minimizes the dose spillage to the surrounding tissues while ensuring the prescribed dose is delivered to the target(s). Organs‐at‐risk and their corresponding allowable doses can also be added to the model to protect adjacent organs.

Results:

We test this approach on various tumour sizes and sites. The quality of the obtained treatment plans are comparable or better than forward plans and inverse plans that use step‐ and‐shoot technique. The conformity indices in the obtained continuous dose delivery plans are similar to those of forward plans while the beam‐on time is improved on average (see Table 1 in supporting document).

Conclusion:

We employ inverse planning for continuous dose delivery in Perfexion for brain tumours. The quality of the obtained plans is similar to forward and inverse plans that use conventional step‐and‐shoot technique. We tested the inverse plans on GammaPlan to verify clinical relevance.

This research was partially supported by Elekta, Sweden (vendor of Gamma Knife Perfexion).
MO‐F‐CAMPUS‐T‐04: Development and Evaluation of a Knowledge‐Based Model for Treatment Planning of Lung Cancer Patients Using Stereotactic Body Radiotherapy (SBRT)
Authors: Snyder K , Kim J , Reding A , Fraser C , Lu S , Gordon J , Ajlouni M , Movsas B , Chetty I
Published: June 2015

Purpose:

To describe the development of a knowledge‐based treatment planning model for lung cancer patients treated with SBRT, and to evaluate the model performance and applicability to different planning techniques and tumor locations.

Methods:

105 lung SBRT plans previously treated at our institution were included in the development of the model using Varian's RapidPlan DVH estimation algorithm. The model was trained with a combination of IMRT, VMAT, and 3D–CRT techniques. Tumor locations encompassed lesions located centrally vs peripherally (43:62), upper vs lower (62:43), and anterior vs posterior lobes (60:45). The model performance was validated with 25 cases independent of the training set, for both IMRT and VMAT. Model generated plans were created with only one optimization and no planner intervention. The original, general model was also divided into four separate models according to tumor location. The model was also applied using different beam templates to further improve workflow. Dose differences to targets and organs‐at‐risk were evaluated.

Results:

IMRT and VMAT RapidPlan generated plans were comparable to clinical plans with respect to target coverage and several OARs. Spinal cord dose was lowered in the model‐based plans by 1Gy compared to the clinical plans, p=0.008. Splitting the model according to tumor location resulted in insignificant differences in DVH estimation. The peripheral model decreased esophagus dose to the central lesions by 0.5Gy compared to the original model, p=0.025, and the posterior model increased dose to the spinal cord by 1Gy compared to the anterior model, p=0.001. All template beam plans met OAR criteria, with 1Gy increases noted in maximum heart dose for the 9‐field plans, p=0.04.

Conclusion:

A RapidPlan knowledge‐based model for lung SBRT produces comparable results to clinical plans, with increased consistency and greater efficiency. The model encompasses both IMRT and VMAT techniques, differing tumor locations, and beam arrangements.

Research supported in part by a grant from Varian Medical Systems, Palo Alto CA.
MO‐F‐CAMPUS‐J‐01: A Novel and Efficient Daily QA Program for Both Modern Linear Accelerator and Optical Surface Motion Systems
Authors: Peng J , McDonald D , Ashenafi M , Koch N , Fugal M , Vanek K
Published: June 2015

Purpose:

The camera‐based optical surface monitoring system(OSMS) is a standard motion‐tracking system for TrueBeam™ machine. Daily quality assurance(DQA) procedures for the TrueBeam and OSMS systems currently require multiple phantoms and test sequences. Machine Performance Check(MPC) uses the IsoCal™ phantom to test geometric and dosimetric aspects, and has been validated for routine TrueBeam DQA. This work examines the novel use of MPC to simultaneously conduct DQA of OSMS,utilizing one phantom and test sequence for QA of both the TrueBeam and OSMS systems.

Methods:

TrueBeam with 6 degree‐of‐freedom(6DOF) couch, MPC and OSMS were installed. The therapists routinely use MPC with the IsoCal phantom to verify geometric and dosimetric parameters with an automated sequence of varying gantry and collimator angles, MLC positions, and couch translations and rotations as the DQA for TrueBeam. A surface outline of the IsoCal phantom, generated from a CT dataset, was imported to OSMS and used to monitor the position of the phantom at isocenter and to track its movement up to 5cm/10°(yaw) and rotation in pitch and roll up to 3° in real time during the automated MPC sequence. Motion of the phantom throughout the MPC routine was compared to the couch position shown by the digital readout.

Results:

The coincidence of isocenter, as determined by OSMS, and TrueBeam radiation isocenter was within ±0.8mm/±0.8° in three axes. Between OSMS and TrueBeam 6DOF couch, the mean couch relative shifts/angles were within ±0.7mm/±0.8° for each axis. Reported pitch and roll values were within ±0.4mm/±0.4°. The total measurement time for each DQA session took approximately 5 minutes.

Conclusion:

Use of the IsoCal phantom and MPC for simultaneous DQA of TrueBeam and OSMS increases QA efficiency and reduces complexity. OSMS isocenter position and motion‐tracking capability in 6DOF were verified in real‐time and were found to be within 1mm of the expected value.
MO‐F‐CAMPUS‐J‐03: Evaluation and Use of Cubic Phantoms for Daily IGRT QA Process with TrueBeam 6DoF Couch
Authors: Ito S , de los Santos L Fong , Cutsinger S McCauley
Published: June 2015

Purpose:

To improve efficiency and efficacy of Daily IGRT QA process and to prepare for Varian TrueBeam 6DoF couch implementation

Methods:

MIMI phantom from Standard Imaging was compared to PentaGuide phantom from Modus Medical in order to replace and update our daily IGRT QA process. The following metrics were used as our evaluation criteria: contrast structures, predefined shift mark, TG‐142 compliance, support for 6DoF and ease of use. To improve process compliance, daily IGRT QA set up and process, an acrylic in‐house baseplates with known offsets were designed. New Daily IGRT QA protocol for 3DoF and 6DoF systems was developed and implemented into our practice. The selected phantom was used for the commissioning of Varian TrueBeam 6DoF couches and test the reproducibility and accuracy of 2D/2D and 3D/3D matching and localization processes. Baseline and tolerances for Daily IGRT QA for 6DoF couch were determined.

Results:

Based on our evaluation criteria, the MIMI phantom fit best our QA processes and matching goals. Comprehensive daily IGRT QA which includes CBCT 3D automatic registration, couch positioning, coincidence of kV/MV isocenters, image quality, and laser alignment were accurately and efficiently validated using the MIMI phantom and the setup baseplate with Varian TrueBeam and EX systems with 3DoF and 6DoF couches. Also, the use of MIMI phantom and setup baseplate was found to be a useful and effective tool for the commissioning of the Varian TrueBeam 6DoF couches.

Conclusion:

Implementation of MIMI phantom improved our Daily IGRT QA process by eliminating the use multiple phantoms which resulted in 40% Daily IGRT QA time reduction. Feedback from therapists has been positive and 100% compliance to new protocol has been achieved with minimal training. MIMI phantom was used to test the stability of the couch and to set baseline for Daily QA target and tolerance values.
MO‐F‐CAMPUS‐I‐01: EIT Imaging to Monitor Human Salivary Gland Functionality: A Feasibility Study
Authors: Kohli K , Karvat A , Liu J , Krishnan K
Published: June 2015

Purpose:

Clinically, there exists a need to develop a non‐invasive technique for monitoring salivary activity. In this study, we investigate the feasibility of a using the electrical conductivity information from Electrical Impedance Tomography (EIT) to monitor salivary flow activity.

Methods:

To acquire EIT data, eight Ag/AgCl ECG electrodes were placed around the mandible of the subject. An EIT scan was obtained by injecting current at 50 KHz, 0.4 mA through each pair of electrodes and recording voltage across other electrode pairs. The functional conductivity image was obtained through reconstruction of the voltage data, using Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) in Matlab. In using EIDORS, forward solution was obtained using a user‐defined finite element model shape and inverse solution was obtained using one‐step Gaussian solver. EIT scans of volunteer research team members were acquired for three different physiological states: pre‐stimulation, stimulation and post‐stimulation. For pre‐stimulation phase, data were collected in intervals of 5 minutes for 15 minutes. The salivary glands were then stimulated in the subject using lemon and the data were collected immediately. Post‐stimulation data were collected at 4 different timings after stimulation.

Results:

Variations were observed in the electrical conductivity patterns near parotid regions between the pre‐ and post‐stimulation stages. The three images acquired during the 15 minute pre‐stimulation phase showed no major changes in the conductivity. Immediately after stimulation, electrical conductivity increased near parotid regions and 15 minutes later slowly returned to pre‐stimulation level.

Conclusion:

In the present study involving human subjects, the change in electrical conductivity pattern shown in the EIT images, acquired at different times with and without stimulation of salivary glands, appeared to be consistent with the change in salivary gland activity. The conductivity changes imaged through EIT are potentially useful for the purpose of salivary monitoring.
MO‐F‐CAMPUS‐I‐02: Stability Investigation of a Gamma Fitting Algorithm for Angiographic Parametric Imaging at Low X‐Ray Exposures Using a Patient Specific Neurovascular Phantom
Authors: Balasubramoniam A , Bednarek D , Rudin S , Ionita C
Published: June 2015

Purpose:

To analyze the stability of a Gamma fitting algorithm based on digital subtraction angiography sequences, at different exposure, hence noise levels.

Methods:

Starting with a patient CT volume, we built a 3D printed neurovascular phantom which contained a complete Circle of Willis with major arteries and five aneurysms. We placed the phantom in a 15 cm water bath and connected it to a flow loop containing a peristaltic pump which simulated physiological relevant flow conditions. We injected 10 ml contrast boluses using an automatic contrast injector at a rate of 10 ml/sec. Digital Subtraction Angiography images were acquired at 30 frames/sec and processed with a gamma fitting based algorithm, to yield parametric maps of: Mean transit time (MTT), Time‐to‐Peak (TTP), Bolus Arrival Time (BAT). Starting with the optimal exposure parameters selected by the x‐ray system automatic exposure control, while keeping the same kV, we lowered the mA and exposure per frame in four steps until we reached the minimum value allowed by the system. We analyzed the variation of the MTT, TTP and BAT for various artery signal to noise ratios using four ROIs.

Results:

The peak (maximum opacification) SNRs for full dose and minimum dose were: 35 and 13 for 4 mm arteries and 18 and 7 for 2 mm arteries. The parameters standard deviation expressed as a percent fraction of the value measured at the full dose value, were: MTT=2.4%, BAT=0.5% and TTP=1.9% for 4 mm vessels and MTT=3.7%, BAT=5.4% and TTP=6.3% for 2 mm vessels.

Conclusion:

Despite a significant decrease of the peak SNR the algorithm performed very well displaying variations less than 6.3% of the ideal conditions. Partial Support: NIH grant R01EB002873 and Toshiba Medical Systems Corp.

Partial Support: NIH grant R01EB002873 and Toshiba Medical Systems Corp.
MO‐G‐304‐01: FEATURED PRESENTATION: Expanding the Knowledge Base for Data‐Driven Treatment Planning: Incorporating Patient Outcome Models
Authors: Robertson SP , Quon H , Cheng Z , Moore JA , Bowers M , McNutt TR
Published: June 2015

Purpose:

To extend the capabilities of knowledge‐based treatment planning beyond simple dose queries by incorporating validated patient outcome models.

Methods:

From an analytic, relational database of 684 head and neck cancer patients, 372 patients were identified having dose data for both left and right parotid glands as well as baseline and follow‐up xerostomia assessments. For each existing patient, knowledge‐based treatment planning was simulated for by querying the dose‐volume histograms and geometric shape relationships (overlap volume histograms) for all other patients. Dose predictions were captured at normalized volume thresholds (NVT) of 0%, 10%, 20, 30%, 40%, 50%, and 85% and were compared with the actual achieved doses using the Wilcoxon signed‐rank test. Next, a logistic regression model was used to predict the maximum severity of xerostomia up to three months following radiotherapy. Baseline xerostomia scores were subtracted from follow‐up assessments and were also included in the model. The relative risks from predicted doses and actual doses were computed and compared.

Results:

The predicted doses for both parotid glands were significantly less than the achieved doses (p < 0.0001), with differences ranging from 830 cGy ± 1270 cGy (0% NVT) to 1673 cGy ± 1197 cGy (30% NVT). The modelled risk of xerostomia ranged from 54% to 64% for achieved doses and from 33% to 51% for the dose predictions. Relative risks varied from 1.24 to 1.87, with maximum relative risk occurring at 85% NVT.

Conclusions:

Data‐driven generation of treatment planning objectives without consideration of the underlying normal tissue complication probability may Result in inferior plans, even if quality metrics indicate otherwise. Inclusion of complication models in knowledge‐based treatment planning is necessary in order to close the feedback loop between radiotherapy treatments and patient outcomes. Future work includes advancing and validating complication models in the context of knowledge‐based treatment planning.

This work is supported by Philips Radiation Oncology Systems.
MO‐G‐304‐02: Knowledge Based DVH Prediction Using a Geometric Dose Transform
Authors: Staub D , Wang J , Jiang S
Published: June 2015

Purpose:

To demonstrate a novel method for predicting patient dose‐volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning.

Methods:

Our initial demonstration utilized a database of 100 prostate intensity‐modulated radiotherapy (IMRT) data‐sets. Each data‐set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data‐sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD‐DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison.

Results:

DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method.

Conclusion:

We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.