bluuebunny/crossref_metadata_2025 huggingface dataset:
TU‐A‐9A‐09: Proton Beam X‐Ray Fluorescence CT
Authors: Bazalova M , Ahmad M , Fahrig R , Xing L
Published: July 2014

Purpose:

To evaluate x‐ray fluorescence computed tomography induced with proton beams (pXFCT) for imaging of gold contrast agent.

Methods:

Proton‐induced x‐ray fluorescence was studied by means of Monte Carlo (MC) simulations using TOPAS, a MC code based on GEANT4. First, proton‐induced K‐shell and L‐shell fluorescence was studied as a function of proton beam energy and 1) depth in water and 2) size of contrast object. Second, pXFCT images of a 2‐cm diameter cylindrical phantom with four 5‐ mm diameter contrast vials and of a 20‐cm diameter phantom with 1‐cm diameter vials were simulated. Contrast vials were filled with water and water solutions with 1‐5% gold per weight. Proton beam energies were varied from 70‐250MeV. pXFCT sinograms were generated based on the net number of gold K‐shell or L‐shell x‐rays determined by interpolations from the neighboring 0.5keV energy bins of spectra collected with an idealized 4π detector. pXFCT images were reconstructed with filtered‐back projection, and no attenuation correction was applied.

Results:

Proton induced x‐ray fluorescence spectra showed very low background compared to x‐ray induced fluorescence. Proton induced L‐shell fluorescence had a higher cross‐section compared to K‐shell fluorescence. Excitation of L‐shell fluorescence was most efficient for low‐energy protons, i.e. at the Bragg peak. K‐shell fluorescence increased with increasing proton beam energy and object size. The 2% and 5% gold contrast vials were accurately reconstructed in K‐shell pXFCT images of both the 2‐cm and 20‐cm diameter phantoms. Small phantom L‐shell pXFCT image required attenuation correction and had a higher sensitivity for 70MeV protons compared to 250MeV protons. With attenuation correction, L‐shell pXFCT might be a feasible option for imaging of small size (∼2cm) objects. Imaging doses for all simulations were 5‐30cGy.

Conclusion:

Proton induced x‐ray fluorescence CT promises to be an alternative quantitative imaging technique to the commonly considered XFCT imaging with x‐ray beams.
TU‐A‐12A‐02: Novel Lung Ventilation Imaging with Single Energy CT After Single Inhalation of Xenon: Comparison with SPECT Ventilation Images
Authors: Negahdar M , Yamamoto T , Shultz D , Gable L , Shan X , Mittra E , Diehn M , Loo B , Maxim P
Published: July 2014

Purpose:

We propose a novel lung functional imaging method to determine the spatial distribution of xenon (Xe) gas in a single inhalation as a measure of regional ventilation. We compare Xe‐CT ventilation to single‐photon emission CT (SPECT) ventilation, which is the current clinical reference. Regional lung ventilation information may be useful for the diagnosis and monitoring of pulmonary diseases such as COPD, radiotherapy planning, and assessing the progression of toxicity after radiation therapy.

Methods:

In an IRB‐approved clinical study, Xe‐CT and SPECT ventilation scans were acquired for three patients including one patient with severe emphysema and two lung cancer patients treated with radiotherapy. For Xe‐ CT, we acquired two breath‐hold single energy CT images of the entire lung with inspiration of 100% O2 and a mixture of 70% Xe and 30% O2, respectively. A video biofeedback system was used to achieve reproducible breath‐holds. We used deformable image registration to align the breathhold images with each other to accurately subtract them, producing a map of the distribution of Xe as a surrogate of lung ventilation. We divided each lung into twelve parts and correlated the Hounsfield unit (HU) enhancement at each part with the SPECT ventilation count of the corresponding part of the lung.

Results:

The mean of the Pearson linear correlation coefficient values between the Xe‐CT and ventilation SPECT count for all three patients were 0.62 (p<0.01). The Xe‐CT image had a higher resolution than SPECT, and did not show central airway deposition artifacts that were present in the SPECT image.

Conclusion:

We developed a rapid, safe, clinically practical, and potentially widely accessible method for regional lung functional imaging. We demonstrated strong correlations between the Xe‐CT ventilation image and SPECT ventilation image as the clinical reference. This ongoing study will investigate more patients to confirm this finding.
TU‐A‐12A‐04: Quantitative Texture Features Calculated in Lung Tissue From CT Scans Demonstrate Consistency Between Two Databases From Different Institutions
Authors: Cunliffe A , Armato S , Castillo R , Pham N , Guerrero T , Al‐Hallaq H
Published: July 2014

Purpose:

To evaluate the consistency of computed tomography (CT) scan texture features, previously identified as stable in a healthy patient cohort, in esophageal cancer patient CT scans.

Methods:

116 patients receiving radiation therapy (median dose: 50.4Gy) for esophageal cancer were retrospectively identified. For each patient, diagnostic‐quality pre‐therapy (0‐183 days) and post‐therapy (5‐120 days) scans (mean voxel size: 0.8mm×0.8mm×2.5mm) and a treatment planning scan and associated dose map were collected. An average of 501 32×32‐pixel ROIs were placed randomly in the lungs of each pre‐therapy scan. ROI centers were mapped to corresponding locations in post‐therapy and planning scans using the displacement vector field output by demons deformable registration. Only ROIs with mean dose <5Gy were analyzed, as these were expected to contain minimal post‐treatment damage. 140 texture features were calculated in pre‐therapy and post‐therapy scan ROIs and compared using Bland‐Altman analysis. For each feature, the mean feature value change and the distance spanned by the 95% limits of agreement were normalized to the mean feature value, yielding normalized range of agreement (nRoA) and normalized bias (nBias). Using Wilcoxon signed rank tests, nRoA and nBias were compared with values computed previously in 27 healthy patient scans (mean voxel size: 0.67mm×0.67mm×1mm) acquired at a different institution.

Results:

nRoA was significantly (p<0.001) larger in cancer patients than healthy patients. Differences in nBias were not significant (p=0.23). The 20 features identified previously as having nRoA<20% for healthy patients had the lowest nRoA values in the current database, with an average increase of 5.6%.

Conclusion:

Despite differences in CT scanner type, scan resolution, and patient health status, the same 20 features remained stable (i.e., low variability and bias) in the absence of disease changes for databases from two institutions. Identification of these features is the first step towards quantifying radiation‐induced changes between preand post‐therapy scans.

Supported, in part, by NIH Grant Nos. S10 RR021039, and P30 CA14599, the Virginia and D. K. Ludwig Fund for Cancer Research, Imaging Research Institute, Biological Sciences Division, The University of Chicago, and The Institute for Translational Medicine Pilot Award, The University of Chicago.
TU‐A‐12A‐08: Computing Longitudinal Material Changes in Bone Metastases Using Dual Energy Computed Tomography
Authors: Schmidtlein CR , Hwang S , Veeraraghavan H , Fehr D , Humm J , Deasy J
Published: July 2014

Purpose:

This study demonstrates a methodology for tracking changes in metastatic bone disease using trajectories in material basis space in serial dual energy computed tomography (DECT) studies.

Methods:

This study includes patients with bone metastases from breast cancer that had clinical surveillance CT scans using a General Electric CT750HD in dual energy mode. A radiologist defined regions‐of‐interested (ROI) for bone metastasis, normal bone, and marrow across the serial DECT scans. Our approach employs a Radon transform to forward‐projection the basis images, namely, water and iodine, into sinogram space. This data is then repartitioned into fat/bone and effective density/Z image pairs using assumed energy spectrums for the x‐ray energies. This approach both helps remove negative material densities and avoids adding spectrum‐hardening artifacts. These new basis data sets were then reconstructed via filtered back‐projection to create new material basis pair images. The trajectories of these pairs were then plotted in the new basis space providing a means to both visualize and quantitatively measure changes in the material properties of the tumors.

Results:

ROI containing radiologist defined metastatic bone disease showed well‐defined trajectories in both fat/bone and effective density/Z space. ROI that contained radiologist defined normal bone and marrow did not exhibit any discernible trajectories and were stable from scan to scan.

Conclusions:

The preliminary results show that changes in material composition and effective density/Z image pairs were seen primarily in metastasis and not in normal tissue. This study indicates that by using routine clinical DECT it may be possible to monitor therapy response of bone metastases because healing or worsening bone metastases change material composition of bone. Additional studies are needed to further validate these results and to test for their correlation with outcome.
TU‐A‐12A‐10: Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Authors: Rios E , Parmar C , Jermoumi M , Aerts H
Published: July 2014

Purpose:

Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non‐invasively. The emerging field of Radiomics addresses this by converting medical images into minable data by applying a large number of quantitative imaging algorithms. Accurate tumor segmentation is one of the main challenges of Radiomics. It has been shown that semiautomatic segmentation approaches efficiently reduce inter‐observer variability as compared to the time consuming manual delineations. In this study, a semiautomatic volumetric segmentation algorithm, implemented in the free and publicly available 3D‐Slicer platform, was investigated in terms of its robustness for Radiomics features quantification.

Methods:

Fifty‐six 3D‐Radiomics features, quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These Radiomics features were derived from the 3D‐tumor volumes defined by three independent observers twice using 3D‐Slicer, and compared to manual slice‐by‐slice delineations of five independent physicians in terms of intra‐class correlation coefficient (ICC) and feature range.

Results:

Radiomics features extracted from 3D‐Slicer segmentations had significantly higher reproducibility (ICC= 0.85 0.15, p=0.0009) compared to the features extracted from the manual segmentations (ICC= 0.77 0.17). Furthermore, the features extracted from 3D‐Slicer based segmentations, spread over significantly smaller range across observers as compared to those of the manual delineations (p= 3.819e‐07). Moreover, the features derived from 3D‐Slicer segmentations overlapped in range with those of the manual delineations, as the lower(higher) limit(s) being significantly higher(lower) for the 3D‐Slicer features (p= 0.007, p= 5.863e‐06).

Conclusion:

3D‐Slicer based semiautomatic segmentation significantly improves the robustness of radiomics features and thus could serve as a potential alternative to the time consuming manual segmentation process. So 3D‐Slicer can have a large application in Radiomics, and be employed for high‐throughput data mining research of medical imaging in clinical oncology.
TU‐A‐12A‐11: Improved Airway Measurement Accuracy for Low Dose Quantitative CT (qCT) Using Statistical (ASIR), at Reduced DFOV, and High Resolution Kernels in a Phantom and Swine Model
Authors: Rodriguez A , Ranallo F , Judy P , Gierada D , Fain S
Published: July 2014

Purpose:

To improve the accuracy of airway measures and reduce x‐ray dose in quantitative CT (qCT) of the lung.

Methods:

qCT was performed on an airway phantom (COPD gene) and a swine model. The airway phantom was used to identify expected improvements in spatial resolution and noise tradeoffs for choice of CT reconstruction kernel and display FOV (DFOV).

Results in the phantom were then applied to the swine model. Images were obtained via helical scans that were performed using a GE Discovery CT750 HD with the COPD Gene/SARP protocols except tube current was varied from 790 to 50 mA, and lung inflation was fixed via mechanical ventilator. CT data were reconstructed using STANDARD (Std) and Bone reconstruction kernels for FBP and 100% ASIR at half and full DFOV. Inner Diameter(ID) and wall area percent (WA%) of selected airway sizes were matched across the swine lung and phantom and performed using Airway Inspector (Surgical Planning Laboratory, Brigham and Women's Hospital, Boston, MA). For the swine lung the CT acquisition at 790 mA with ASIR and Bone kernel at half DFOV parameters was the reference standard for comparison to the lower dose scans. Results:

The accuracy of the ID and WA% improved by ∼24% and ∼23% for half DFOV with the Bone kernel and ASIR reconstruction for both phantom and swine models for all airways investigated. For low dose qCT, the difference between the reference standard measures and the measures obtained with half DFOV with Bone+ASIR remained much lower than those using current clinical parameters (FBP at full DFOV) even at one fourth the dose for all airways investigated as is shown in Table 1.

Conclusion:

The combination of ASIR with higher spatial resolution reconstruction shows promise to reduce x‐ray dose and improve qCT of lung airways.

NIBIB PB EB 1010 159 JKS Recovery Quantitative Imaging Biomarker Alliance (QIBA) U10 HL109168 SARP
TU‐C‐BRE‐06: Effect of Implementing In‐House Treatment Couch Model On Patient Specific QA for Pinnacle SmartArc Treatment Plans
Authors: Ellis A , Jacqmin D , McDonald D , Peng J , Koch N , Ashenafi M , Vanek K
Published: July 2014

Purpose:

Failure to model the treatment couch during VMAT QA planar dose calculation may Result in discrepancies between measured and calculated dose. These discrepancies are due to beam attenuation by the treatment couch that is not included in dose calculation. This work evaluates effects of accounting for this attenuation on patient specific VMAT QA results using an in‐house created Varian Exact couch model in Pinnacl

Methods:

Patient specific VMAT QA results for 13 Pinnacle SmartArc plans generated for treatment on a Varian iX accelerator were studied. These plans included 3 treatment sites (7 H' N, 5 brain, 1 prostate). A Pinnacle model for Varian Exact couch was created in‐house to replace the CT simulator couch. Composite arc planar doses were calculated with no couch present (NC) and with the Exact couch model (CM) in place for each plan. QA measurements were taken using IBA Matrixx Evolution ion chamber array set up in IBA MultiCube and were compared to each planar dose. Gamma passing criteria of both 3%/3mm and 2%/2mm tolerances were used.

Results:

Over all treatment sites, increases in gamma passing rates from NC to CM ranged from ‐0.4% to +27.3% at 3%/3mm and +0.1% to +30.5% at 2%/2mm. Mean increases in passing rates were +3.7% and +5.3% for 3%/3mm and 2%/2mm tolerances, respectively. Site‐specific mean increases (NC to CM) in gamma passing rates were +4.4%, +3.4%, +0.4% (3%/3mm tolerance) and +6.9%, +3.7%, and +2.9% at (2%/2mm tolerance) for H' N, brain, and prostate, respectively.

Conclusion:

Results support use of a couch model when generating planar dose for patient specific VMAT QA analysis. The improvements were most noticeable at 2%/2mm tolerance and for the H' N and brain sites. Eliminating treatment couch beam attenuation as a source of discrepancy in QA measurements may improve the ability to recognize otherwise masked delivered dose errors.
TU‐C‐BRE‐08: IMRT QA: Selecting Meaningful Gamma Criteria Based On Error Detection Sensitivity
Authors: Steers J , Fraass B
Published: July 2014

Purpose:

To develop a strategy for defining meaningful tolerance limits and studying the sensitivity of IMRT QA gamma criteria by inducing known errors in QA plans.

Methods:

IMRT QA measurements (ArcCHECK, Sun Nuclear) were compared to QA plan calculations with induced errors. Many (>24) gamma comparisons between data and calculations were performed for each of several kinds of cases and classes of induced error types with varying magnitudes (e.g. MU errors ranging from ‐10% to +10%), resulting in over 3,000 comparisons. Gamma passing rates for each error class and case were graphed against error magnitude to create error curves in order to represent the range of missed errors in routine IMRT QA using various gamma criteria.

Results:

This study demonstrates that random, case‐specific, and systematic errors can be detected by the error curve analysis. Depending on location of the peak of the error curve (e.g., not centered about zero), 3%/3mm threshold=10% criteria may miss MU errors of up to 10% and random MLC errors of up to 5 mm. Additionally, using larger dose thresholds for specific devices may increase error sensitivity (for the same X%/Ymm criteria) by up to a factor of two. This analysis will allow clinics to select more meaningful gamma criteria based on QA device, treatment techniques, and acceptable error tolerances.

Conclusion:

We propose a strategy for selecting gamma parameters based on the sensitivity of gamma criteria and individual QA devices to induced calculation errors in QA plans. Our data suggest large errors may be missed using conventional gamma criteria and that using stricter criteria with an increased dose threshold may reduce the range of missed errors. This approach allows quantification of gamma criteria sensitivity and is straightforward to apply to other combinations of devices and treatment techniques.
TU‐C‐BRE‐11: 3D EPID‐Based in Vivo Dosimetry: A Major Step Forward Towards Optimal Quality and Safety in Radiation Oncology Practice
Authors: Mijnheer B , Mans A , Olaciregui‐Ruiz I , Rozendaal R , Spreeuw H , van Herk M
Published: July 2014

Purpose:

To develop a 3D in vivo dosimetry method that is able to substitute pre‐treatment verification in an efficient way, and to terminate treatment delivery if the online measured 3D dose distribution deviates too much from the predicted dose distribution.

Methods:

A back‐projection algorithm has been further developed and implemented to enable automatic 3D in vivo dose verification of IMRT/VMAT treatments using a‐Si EPIDs. New software tools were clinically introduced to allow automated image acquisition, to periodically inspect the record‐and‐verify database, and to automatically run the EPID dosimetry software. The comparison of the EPID‐reconstructed and planned dose distribution is done offline to raise automatically alerts and to schedule actions when deviations are detected. Furthermore, a software package for online dose reconstruction was also developed. The RMS of the difference between the cumulative planned and reconstructed 3D dose distributions was used for triggering a halt of a linac.

Results:

The implementation of fully automated 3D EPID‐based in vivo dosimetry was able to replace pre‐treatment verification for more than 90% of the patient treatments. The process has been fully automated and integrated in our clinical workflow where over 3,500 IMRT/VMAT treatments are verified each year. By optimizing the dose reconstruction algorithm and the I/O performance, the delivered 3D dose distribution is verified in less than 200 ms per portal image, which includes the comparison between the reconstructed and planned dose distribution. In this way it was possible to generate a trigger that can stop the irradiation at less than 20 cGy after introducing large delivery errors.

Conclusion:

The automatic offline solution facilitated the large scale clinical implementation of 3D EPID‐based in vivo dose verification of IMRT/VMAT treatments; the online approach has been successfully tested for various severe delivery errors.
TU‐C‐BRF‐01: Innovation in Medical Physics and Engineering
Authors: Mohan R , Pelc N , Jaffray D , Mackie T
Published: July 2014

We seek to heighten the awareness of the role of research and innovation that leads to clinical advances in the field of medical physics and engineering. Marie Curie (discovery and use of radium) and Harold Johns (Co‐60 tele‐therapy) in radiotherapy, and pioneers in imaging (Allan Cormack and Godfrey Hounsfield for the CT and Paul Lauterbur, Peter Mansfield for MRI, etc.) were scientists often struggling against great odds. Examples of more recent innovations that are clearly benefitting our patients include IMRT, Image Guided Radiation Therapy and Surgery, Particle Therapy, Quantitative imaging, amongst others.We would also like to highlight the fact that not all of the discovery and engineering that we benefit from in today’s world, was performed at research institutions alone. Rather, companies often tread new ground at financial and reputational risk. Indeed the strength of the private sector is needed in order to bring about new advances to our practice. The keys to long term success in research and development may very well include more public and private research spending. But, when more investigators are funded, we also need to recognize that there needs to be a willingness on the part of the funding institutions, academic centers and investigators to risk failure for the greater potential achievements in innovation and research. The speakers will provide examples and insight into the fields of innovation and research in medical physics from their own first hand experiences.

Learning Objectives:

To obtain an understanding of the importance of research and development towards advances in physics in medicine.

To raise awareness of the role of interdisciplinary collaborations in translational research and innovation.

To highlight the importance of entrepreneurships and industrial‐institutional research partnerships in fostering new ideas and their commercial success.

To recognize and account for the risk of failure for the greater potential achievements in innovation and research.
TU‐C‐17A‐01: A Data‐Based Development for Pratical Pareto Optimality Assessment and Identification
Authors: Ruan D , Qi S , DeMarco J , Kupelian P , Low D
Published: July 2014

Purpose:

To develop an efficient Pareto optimality assessment scheme to support plan comparison and practical determination of best‐achievable practical treatment plan goals.

Methods:

Pareto efficiency reflects the tradeoffs among competing target coverage and normal tissue sparing in multi‐criterion optimization (MCO) based treatment planning. Assessing and understanding Pareto optimality provides insightful guidance for future planning. However, current MCO‐driven Pareto estimation makes relaxed assumptions about the Pareto structure and insufficiently account for practical limitations in beam complexity, leading to performance upper bounds that may be unachievable. This work proposed an alternative data‐driven approach that implicitly incorporates the practical limitations, and identifies the Pareto frontier subset by eliminating dominated plans incrementally using the Edgeworth Pareto hull (EPH). The exactness of this elimination process also permits the development of a hierarchical procedure for speedup when the plan cohort size is large, by partitioning the cohort and performing elimination in each subset before a final aggregated elimination. The developed algorithm was first tested on 2D and 3D where accuracy can be reliably assessed. As a specific application, the algorithm was applied to compare systematic plan quality for lower head‐and‐neck, amongst 4 competing treatment modalities.

Results:

The algorithm agrees exactly with brute‐force pairwise comparison and visual inspection in low dimensions. The hierarchical algorithm shows sqrt(k) folds speedup with k being the number of data points in the plan cohort, demonstrating good efficiency enhancement for heavy testing tasks. Application to plan performance comparison showed superiority of tomotherapy plans for the lower head‐and‐neck, and revealed a potential nonconvex Pareto frontier structure.

Conclusion:

An accurate and efficient scheme to identify Pareto frontier from a plan cohort has been developed. This implementation would guide generating good yet practically achievable plan quality goals for further planning. The observation of a systematic performance bias and a nonconvex Pareto frontier warrants further investigation.
TU‐C‐17A‐02: An Efficient Method for Making IMRT Treatment Planning Optimizations Results Robust Against Geometrical Uncertainties
Authors: Xie Y , Chen Y , Wickerhauser M , Deasy J
Published: July 2014

Purpose:

Systematic and random geometrical shifts of target and normal tissues can lead to delivered doses being significantly worse than planned dose distributions. We developed new methods to make treatment plans robust against geometrical uncertainties.

Methods:

We used prioritized prescription algorithms as a basis for development. Under the assumption that each structure moves rigidly, a 3‐dimentional vector with a certain probability distribution was used to characterize the position of each structure. One important metric (e.g. mean dose for bladder) was selected to characterize the treatment quality for each structure. An additional optimization step was added to optimize both the mean of each priority Objectives: function as well as the variance values, with respect to the uncertainty probability distributions, while retaining the good quality of the initial optimization results. This method was tested on 10 de‐identified prostate treatment cases.

Results:

Our optimization algorithm gave very good robust treatment plans consistently for the tested 10 prostate treatment cases, with almost the same robust quality as the traditional CTV‐PTV method on targets but with much lower and more robust doses to sensitive structures, especially on structures very close or overlapping with PTV (the rectum and bladder). The method is computationally efficient: in average the method adds only 3 minutes to the overall optimization time (less than 5 minutes total time on a quad‐core processor.)

Conclusion:

An optimization algorithm, compatible with prioritized (or lexicographic) optimization, was designed to generate robust treatment plans against motion uncertainties. The method optimizes both the mean and variance values of relevant dose metrics (e.g. bladder mean dose) for all interesting structures. This new algorithm theoretically works for any existing IMRT optimization algorithm and thus has great potential to improve treatment plan robustness.
TU‐C‐17A‐05: Dose Domain Optimization of MLC Leaf Patterns for Highly Complicated 4Ï€ IMRT Plans
Authors: Nguyen D , Yu V , Ruan D , Semwal H , O’Connor D , Cao M , Low D , Sheng K
Published: July 2014

Purpose:

Highly conformal non‐coplanar 4π radiotherapy plans typically require more than 20 intensity‐modulated fields to deliver. A novel method to calculate multileaf collimator (MLC) leaf patterns is introduced to maximize delivery efficiency, accuracy and plan quality.

Methods:

4 GBM patients, with a prescription dose of 59.4 Gy or 60 Gy, were evaluated using the 4π algorithm using 20 beams. The MLC calculation utilized a least square minimization of the dose distribution, with an anisotropic total variation regularization term to encourage piecewise continuity in the fluence maps. Transforming the fluence to the dose domain required multiplying the fluence with a sparse matrix. Exploiting this property made it feasible to solve the problem using CVX, a MATLAB‐based convex modeling framework. The fluence was stratified into even step sizes, and the MLC segments, limited to 300, were calculated. The patients studied were replanned using Eclipse with the same beam angles.

Results:

Compared to the original 4π plan, the stratified 4π plan increased the maximum/mean dose for, in Gy, by 1.0/0.0 (brainstem), 0.5/0.2 (chiasm), 0.0/0.0 (spinal cord), 1.9/0.3 (L eye), 0.7/0.2 (R eye), 0.4/0.4 (L lens), 0.3/0.3 (R lens), 1.0/0.8 (L Optical Nerve), 0.5/0.3 (R Optical Nerve), 0.3/0.2 (L Cochlea), 0.1/0.1 (R Cochlea), 4.6/0.2 (brain), 2.4/0.1 (brain‐PTV), 5.1/0.9 (PTV). Compared to Eclipse, which generated an average of 607 segments, the stratified plan reduced (−) or increased (+) the maximum/mean dose, in Gy, by −10.2/−4.1 (brainstem), −10.5/−8.9 (chiasm), +0.0/−0.1 (spinal cord), −4.9/−3.4 (L eye), −4.1/−2.5 (R eye), −2.8/−2.7 (L lens), −2.1/−1.9 (R lens), −7.6/−6.5 (L Optical Nerve), −8.9/−6.1 (R Optical Nerve), −1.3/−1.9 (L Cochlea), −1.8/−1.8 (R Cochlea), +1.7/−2.1 (brain), +3.2/−2.6 (brain‐PTV), +1.8/+0.3 Gy (PTV. The stratified plan was also more homogeneous in the PTV.

Conclusion:

This novel solver can transform complicated fluence maps into significantly fewer deliverable MLC segments than the commercial system while achieving superior dosimetry.

Funding support partially contributed by Varian.
TU‐C‐17A‐07: FusionARC Treatment with Adaptive Beam Selection Method
Authors: Kim H , Li R , Lee R , Xing L
Published: July 2014

Purpose:

Recently, a new treatment scheme, FusionARC, has been introduced to compensate for the pitfalls in single‐arc VMAT planning. It basically allows for the static field treatment in selected locations, while the remaining is treated by single‐rotational arc delivery. The important issue is how to choose the directions for static field treatment. This study presents an adaptive beam selection method to formulate fusionARC treatment scheme.

Methods:

The optimal plan for single‐rotational arc treatment is obtained from two‐step approach based on the reweighted total‐variation (TV) minimization. To choose the directions for static field treatment with extra segments, a value of our proposed cost function at each field is computed on the new fluence‐map, which adds an extra segment to the designated field location only. The cost function is defined as a summation of equivalent uniform dose (EUD) of all structures with the fluence‐map, while assuming that the lower cost function value implies the enhancement of plan quality. Finally, the extra segments for static field treatment would be added to the selected directions with low cost function values. A prostate patient data was applied and evaluated with three different plans: conventional VMAT, fusionARC, and static IMRT.

Results:

The 7 field locations, corresponding to the lowest cost function values, are chosen to insert extra segment for step‐and‐shoot dose delivery. Our proposed fusionARC plan with the selected angles improves the dose sparing to the critical organs, relative to static IMRT and conventional VMAT plans. The dose conformity to the target is significantly enhanced at the small expense of treatment time, compared with VMAT plan. Its estimated treatment time, however, is still much faster than IMRT.

Conclusion:

The fusionARC treatment with adaptive beam selection method could improve the plan quality with insignificant damage in the treatment time, relative to the conventional VMAT.
TU‐C‐17A‐08: Improving IMRT Planning and Reducing Inter‐Planner Variability Using the Stochastic Frontier Method: Validation Based On Clinical and Simulated Data
Authors: Gagne MC , Tremblay D , Varfalvy N , Archambault L
Published: July 2014

Purpose:

Intensity modulated radiation therapy always requires compromises between PTV coverage and organs at risk (OAR) sparing. We previously developed metrics that correlate doses to OAR to specific patients’ morphology using stochastic frontier analysis (SFA). Here, we aim to examine the validity of this approach using a large set of realistically simulated dosimetric and geometric data.

Methods:

SFA describes a set of treatment plans as an asymmetric distribution with respect to a frontier defining optimal plans. Eighty head and neck IMRT plans were used to establish a metric predicting the mean dose to parotids as a function of simple geometric parameters. A database of 140 parotids was used as a basis distribution to simulate physically plausible data of geometry and dose. Distributions comprising between 20 and 5000 were simulated and the SFA was applied to obtain new frontiers, which were compared to the original frontier.

Results:

It was possible to simulate distributions consistent with the original dataset. Below 160 organs, the SFA could not always describe distributions as asymmetric: a few cases showed a Gaussian or half‐Gaussian distribution. In order to converge to a stable solution, the number of organs in a distribution must ideally be above 100, but in many cases stable parameters could be achieved with as low as 60 samples of organ data. Mean RMS value of the error of new frontiers was significantly reduced when additional organs are used.

Conclusion:

The number of organs in a distribution showed to have an impact on the effectiveness of the model. It is always possible to obtain a frontier, but if the number of organs in the distribution is small (< 160), it may not represent de lowest dose achievable. These results will be used to determine number of cases necessary to adapt the model to other organs.
TU‐C‐17A‐09: Multi‐Case Knowledge‐Based IMRT Treatment Planning in Head and Neck Cancer: Are Six Heads Better Than One?
Authors: Grzetic S , Lutzky C , Das S , Lo J
Published: July 2014

Purpose:

HNC IMRT treatment planning is a challenging process that relies heavily on the planner’s experience. Previously, we used the single, best match from a library of manually planned cases to semi‐automatically generate IMRT plans for a new patient. The current multi‐case Knowledge Based Radiation Therapy (MC‐KBRT) study utilized different matching cases for each of six individual organs‐at‐risk (OARs), then combined those six cases to create the new treatment plan.

Methods:

From a database of 103 patient plans created by experienced planners, MC‐KBRT plans were created for 40 (17 unilateral and 23 bilateral) HNC “query” patients. For each case, 2D beam’s‐eye‐view images were used to find similar geometric “match” patients separately for each of 6 OARs. Dose distributions for each OAR from the 6 matching cases were combined and then warped to suit the query case’s geometry. The dose‐volume constraints were used to create the new query treatment plan without the need for human decision‐making throughout the IMRT optimization. The optimized MC‐KBRT plans were compared against the clinically approved plans and Version 1 (original KBRT) using the dose metrics: mean, median, and maximum (brainstem and cord+5mm) doses.

Results:

Compared to Version 1, MC‐KBRT had no significant reduction of the dose to any of the OARs in either unilateral/bilateral cases. Compared to the manually‐planned unilateral cases, there was significant reduction of the oral cavity mean/median dose (>2Gy) at the expense of the contralateral parotid. Compared to the manually‐planned bilateral cases, reduction of dose was significant in the ipsilateral parotid, larynx, and oral cavity (>3Gy mean/median) while maintaining PTV coverage.

Conclusion:

MC‐KBRT planning in head and neck cancer generates IMRT plans with equivalent dose sparing to manually created plans. MC‐KBRT using multiple case matches does not show significant dose reduction compared to using a single match case with dose warping.
TU‐C‐17A‐10: Patient Features Based Dosimetric Pareto Front Prediction In Esophagus Cancer Radiotherapy
Authors: Wang J , Jin X , Zhao K , Peng J , Hu W
Published: July 2014

Purpose:

The purpose of this study is to study the feasibility of the dosimetric pareto front (PF) prediction based on patient anatomic and dosimetric parameters for esophagus cancer patients.

Methods:

Sixty esophagus patients in our institution were enrolled in this study. A total 2920 IMRT plans were created to generated PF for each patient. On average, each patient had 48 plans. The anatomic and dosimetric features were extracted from those plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose and PTV homogeneous index (PTVHI) were recorded for each plan. The principal component analysis (PCA) was used to extract overlap volume histogram (OVH) features between PTV and other critical organs. The full dataset was separated into two parts include the training dataset and the validation dataset. The prediction outcomes were the MHD and MLD for the current study. The spearman rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The PF was fit by the the stepwise multiple regression method. The cross‐validation method was used to evaluation the model.

Results:

The mean prediction error of the MHD was 465 cGy with 100 repetitions. The most correlated factors were the first principal components of the OVH between heart and PTV, and the overlap between heart and PTV in Z‐axis. The mean prediction error of the MLD was 195 cGy. The most correlated factors were the first principal components of the OVH between lung and PTV, and the overlap between lung and PTV in Z‐axis.

Conclusion:

It is feasible to use patients anatomic and dosimetric features to generate a predicted PF. Additional samples and further studies were required to get a better prediction model.
TU‐C‐17A‐12: Towards a Passively Optimized Phase‐Space Monte Carlo (POPMC) Treatment Planning Method: A Proof of Principle
Authors: Yang Y M , Zankowski C , Svatos M , Bednarz B
Published: July 2014

Purpose:

The advent of on‐line/off‐line adaptive, and biologically‐conformal radiation therapy has led to a need for treatment planning solutions that utilize voxel‐specific penalties, requiring optimization over a large solution space that is performed quickly, and the dose in each voxel calculated accurately. This work proposes a “passive” optimization framework, which is executed concurrently during Monte Carlo dose calculation, evaluating the cost/benefit of each history during transport, and creates a passively optimized fluence map.

Methods:

The Monte Carlo code Geant4 v9.6 was used for this study. The standard voxel geometry implementation was modified to support the passive optimization framework, with voxel‐specific optimization parameters. Dose‐benefit functions, which will increase a particle history’s weight upon dose deposition, were defined in a central collection of voxels to effectively create target structures. Histories that deposit energy to voxels are reweighted based on a voxel’s dose multiplied by its cost/benefit value. Upon full termination of each history, the dose contributions of that history are reweighted to reflect a contribution proportional to the history’s final weight. A parallel‐planar 1.25 MeV photon fluence is transported through the geometry, and re‐weighted at each dose deposition step. The resulting weight is tallied with the incident spatial and directional coordinates in a phase‐space distribution.

Results:

A uniform incident fluence was reweighted during MC dose calculations to create an optimized fluence map which would generate dose profiles in target volumes that exhibit the same dose characteristics as the prescribed optimization parameters. An optimized dose profile, calculated concurrently with the phase‐space, reflects the resulting dose distribution.

Conclusion:

This study demonstrated the feasibility of passively optimizing an incident fluence map during Monte Carlo dose calculations. The flexibility of the voxel‐specific optimization framework allows a variety and combination of optimization parameters to be calculated for each voxel at every transportation step.

This work is partially supported by Varian. This work is partially supported by the NIH.
TU‐C‐12A‐01: Measurement of Skeletal Muscle Lipids in Type 2 Diabetes Using in Vivo Proton MR Spectroscopy
Authors: Valaparla S , Boone G , Ripley E , Abdul‐Ghani M , Duong T , Clarke G
Published: July 2014

Purpose:

To quantify and compare the intramyocellular (IMCL), extramyocellular (EMCL) lipids and total fat fraction in human vastus lateralis (VL) muscle between lean controls and type 2 diabetic (T2DM) subjects using long echo time in vivo proton MR spectroscopy (1H‐MRS)

Methods:

1H‐MRS single voxel (15 × 15 × 15 mm3) stimulated acquisition mode (STEAM) was performed in right vastus lateralis m. on 10 lean controls (age: 28.3±3.94 yrs, BMI: 24.25±3.20 kg/m2) and 7 type 2 diabetic (age: 54.28±6.42 yrs, BMI: 31.34±3.13 kg/m2) subjects with Siemens 3T MRI and four‐channel flex coil. Unsuppressed water spectra (NSA = 16) with TR/TE = 3000/30 msec, TM = 10 msec, BW = 2000 Hz, and water‐suppressed spectra (NSA = 128) with TR/TE = 3000/270 msec, TM = 10 msec, and fixed water suppression BW = 50 Hz were acquired. Spectral intensity ratios of IMCL‐CH2, EMCL‐CH2 and total lipid (IMCL + EMCL) with unsuppressed water signal (W) were converted into absolute concentrations expressed in mmol/kg. Fat fraction (100 × F/(W+F)) was calculated, where F includes the signal intensities of IMCL and EMCL methylene (CH2)n, peaks only.

Results:

Comparison of IMCL (controls: 11.70 ± 6.7, T2DM: 21.74 ± 10.2, p ≤ 0.01), EMCL (controls: 22.89 ± 18.42, T2DM: 77.21 ± 33.4, p ≤ 0.001) and total lipid (64.35 mmol/kg less in controls, p ≤ 0.001) showed statistical significance using two‐tailed student t‐test. Fat fraction (%) exhibited considerable inter‐individual variability for controls (3.14 ± 2.09; range: 1.34 – 7.04) and T2DM (9.34 ± 2.88; range: 4.15 – 13.67) and deemed significant (p ≤ 0.05

Conclusion:

Single voxel STEAM 1H‐MRS at long TE provides a robust non‐invasive method for characterizing lipids within localized muscle regions, with well‐resolved IMCL/EMCL peak separation. Regional lipid estimate and fat fraction in VL m. was significantly different in T2DM compared to lean controls.

American Heart Association Southwest Affiliate Pre‐doctoral Fellowship
TU‐C‐12A‐03: Functional MRI of Esophageal Cancer: Repeatability and Inter‐Observer Reproducibility of Geometrically Corrected ADC Maps
Authors: van Lier A , van Rossum P , Lips I , Philippens M , Meijer G , van den Berg C , van Vulpen M , Lagendijk J
Published: July 2014

Purpose:

In preparation to treatment response monitoring with diffusion weighted imaging (DWI) of neoadjuvant chemoradiation of esophageal cancer, we determined the repeatability and inter‐observer reproducibility of the apparent diffusion coefficient (ADC). Geometrical corrections were performed prior to analysis, to enable multi‐parametric analysis on the same ROI.

Methods:

Ten patients with biopsy‐proven esophageal cancer underwent an MRI exam before, during and after neoadjuvant chemoradiotherapy. The scan protocol consisted of an anatomical T2w scan (TE/TR = 100/1938ms), a DWI scan (b = 0, 200, 800 s/mm2, voxel size 3.25×3.25mm) and a B0 map to monitor magnetic field distortions (dual echo, dual acquisition, TE1/TE2 = 4.6/9.2ms). DWI scans were obtained with a single shot echo‐planar read‐out (BW_phaseenc ∼ 30 Hz/pixel). Two acquisitions were performed with opposing phase‐encoding gradient directions, which results in opposing geometrical distortions. Subsequently, both DWI scans were geometrically corrected using the B0 map, and ADC maps were calculated. For analysis of the inter‐observer reproducibility, two clinicians delineated the tumor volume on the T2w image. The ROIs were transferred to the ADC map. Finally, the repeatability was determined by comparing the ADC within one ROI between the two DWI scans in an exam. The inter‐observer reproducibility was determined by comparing the ADC values within the two ROIs.

Results:

The average geometrical distortion over all ROIs was 2.67mm (range: 0.02‐17.66mm). The repeatability of geometrical corrected ADC maps was 10.0%, the inter‐observer reproducibility 7.9%. Both percentages are 95%‐confidence intervals (1.96x standard deviation of median ADC).

Conclusion:

Geometrical correction was required as considerable distortions were observed. Corrected ADC maps of esophageal cancer are associated with a modest repeatability error and inter‐observer reproducibility error, which are in the same range compared to previous reports in other malignancies. In future, we will incorporate repeatability and reproducibility data in MRI‐based (multi‐parametric) treatment response models of esophageal cancer.
TU‐C‐12A‐07: Characterization of Longitudinal Reproducibility of Quantitative Diffusion Imaging Data Acquired with Four Different Protocols Using a Phantom
Authors: Li X , Buzzelli M , Randazzo W , Yanasak N
Published: July 2014

Purpose:

To characterize and compare the longitudinal reproducibility of diffusion imaging data acquired with four different protocols using a phantom.

Methods:

The Diffusive Quantitative Imaging Phantom (DQIP) was constructed using fifteen cylindrical compartments within a larger compartment, filled with deionized water doped with CuSO4 and NaCl. The smaller compartments contained arrays of hexagonal or cylindrical glass capillaries of varying inner diameters, for differing restraint of water diffusion. The sensitivity of diffusion imaging metrics to signal‐to‐noise ratio (SNR) was probed by doping compartments with differing ratios of deuterium oxide to H2O. A cork phantom enclosure was constructed to increase thermal stability during scanning and a cork holder was made to reproduce scanner positioning. Four different protocols of DWI (diffusion weighted imaging) and DTI (Diffusion tensor imaging) imaging were assembled on a GE Excite HDx 3.0T MRI scanner to collect imaging data over 9‐10 days. Data was processed with in‐house software created in Matlab to obtain fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values.

Results:

All DTI and DWI sequences showed good longitudinal stability of mean FA and ADC values per compartment, exhibiting low standard deviation ∼9%. A t‐test was performed to compare mean FA values from the DTI clinical protocol to those of the DTI special protocol, indicating significantly different values in the majority of compartments. ANOVA performed on ADC values for all DTI and DWI sequences also showed significantly different values in a majority of compartments.

Conclusion:

This work has the potential for quantifying systemic variations between diffusion imaging sequences from different platforms. Characterization of DWI and DTI performance were done over four sequences with predictable results. This data suggests that the DQIP phantom may be a reliable method of monitoring day‐to‐day and scan‐to‐scan variation in diffusion imaging sequences from different platforms.

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