bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐96: Predictive Neural Network for Parotid Glands Deformation Using IGRT and Dose Warping Systems
Authors: Maffei N , Guidi G , Vecchi C , Baldazzi G , Costi T
Published: July 2014

Purpose:

Tumor shrinkage and modification of patient anatomy may occur within the weeks of therapy. Dose warping algorithm and IGRT can usefully determinate morphological variations occurring during treatments and predict possible challenge or requirement of re‐planning. Using metaanalysis and developing Predictive Neural Network (PNN) is possible to collect information for perspective treatment evaluation of inappropriate dose delivery or to re‐planning care course.

Methods:

23 H' N patients, treated using Tomotherapy Unit, were post‐processed using deformable hybrid algorithms to generate contours evolution throughout the course. 750 MVCT were elaborated focusing on parotids. For each deformable image registration were generated deformable ROIs and re‐mapped to dose grid to accumulate dose. A nomogram was developed to evaluate Volume (V) and Dose (D) variations. Using Moving Average (MA) function and PNN were identified the weeks when re‐plan should be predictable.

Results:

The weekly nomogram provided the percentage of patients affected by V and D deviation respect to the first day of treatment. During 1st week of treatment 95% of patients have a ΔV=10% and ΔD=2%. In the 6th week, only 70% of cases remain inside that range. The PNN using Cluster Analysis and Support Vector Machines, highlighted the requirement of warping methods in clinical practice. Validated by 8 patients test cases, PNN allow to classify 2 statistical cluster related to original plan and re‐plan requirements. MA with period of 3 and 5 days, predict days of treatment where to locate physics replan. Best statistical benefits can be achieved with evaluation in the 4th week.

Conclusion:

Body morphing and organ motion during therapy can affect to the dose distribution and induce unexpected or late toxicities. Hybrid deformable registration allows to study the time to re‐plan or to adapt treatments. Monitoring large patient's database during treatments, thereby rising accuracy in nomograms, PNN and reproducibility of treatments.

The research is partially co‐funded by the MoH (GR‐2010‐2318757) and Tecnologie Avanzate S.r.1.(Italy)
SU‐E‐J‐107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis
Authors: Bredfeldt J , Liu Y , Conklin M , Keely P , Eliceiri K , Mackie T
Published: July 2014

Purpose:

Our goal is to develop and apply a set of optical and computational tools to enable large‐scale investigations of the interaction between collagen and tumor cells.

Methods:

We have built a novel imaging system for automating the capture of whole‐slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi‐automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups.

Results:

We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells.

Conclusion:

We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.
SU‐E‐J‐109: Testing the KV Imaging Center Congruence with Radiation Isocenter of Small MLC and SRS Cone Field On Two Machines
Authors: Fu  , Chen Y , Yu Y , Liu H
Published: July 2014

Purpose:

Orthogonal kV image pairs are used for target localization when fiducial markers are implanted. CBCT is used to verify cone SRS setup. Therefore it is necessary to evaluate the isocenter congruence between radiation fields and kV imaging center. This study used a simple method to evaluate the isocenter congruence, and compared the results for MLC and cone fields on two different Linacs.

Methods:

Varian OBI block was attached on the couch. It has a central 1mm BB with markers on three surfaces to align with laser. KV and MV images were taken at four cardinal angles. A 3×3cm2 MLC field and a 20mm cone field were irradiated respectively. On each kV image, the distance from BB center to the kV graticule center were measured. On the MV image of MLC field, the center of radiation field was determined manually, while for cone field, the Varian AM maintenance software was used to analyze the distance between BB and radiation field. The subtraction of the two distances gives the discrepancy between kV and radiation centers. Each procedure was repeated on five days at Trilogy and TrueBeam respectively.

Results:

The maximum discrepancy was found in the longitudinal direction at 180° gantry angel. It was 1.5±0.1mm for Trilogy and 0.6±0.1mm for TrueBeam. For Trilogy, although radiation center wobbled only 0.7mm and image center wobbled 0.8mm, they wobbled to the opposite direction. KV Pair using gantry 180° should be avoided in this case. Cone vs. kV isocenter has less discrepancy than MLC for Trilogy.

Conclusion:

Radiation isocenter of MLC and cone field is different, so is between Trilogy and TrueBeam. The method is simple and reproducible to check kV and radiation isocenter congruence.
SU‐E‐J‐110: TG 51 Dosimetry : With Or Without Lead
Authors: Shah M
Published: July 2014

TG‐51 Dosimetry: With or Without Lead.

Purpose:

In this project, an analytical method has been introduced for adjustment of the TG‐51 recommended KQ in order to produce accurate dosimetric data for high energy photons without the lead foil.

Methods:

These investigations were performed using a 30 cm × 30 cm × 30 cm CIVCO water tank, A12 EXRADIN Water proof Farmer Chamber, a Standard Imaging MAX 4000 electrometer, and 1 mm thick lead foil from Standard Imaging. Complete TG‐51 was performed every month with and without lead. The results were analyzed and an analytical model has been developed for comparing the values of KQ. TG‐51 Table I was used to obtain KQ values.

Results:

The dosimetric evaluations were obtained for Varian Linear accelerators Model 21ix and 21ex. These results indicates that the measured data with lead foil in place as recommended by TG‐51 is in excellent agreement (within 0.1%) with the calculated data obtained by the new model, from our dosimetry data without‐lead. If equation 15 of the TG‐51 report is used without any adjustments, it will lead to differences of about 1.6 % (on the average) in relative data which will Resultin differences of about 0.3 % (on the average) in the KQ Values. The KQ value for 18 MV obtained consistently with the equation of TG‐51 “with lead” and “without lead” were 0.971 and 0.974, respectively. The 0.3 % higher results for KQ without lead eventually will lead to 0.3% larger output. However, by considering this model the KQ value was found to be 0.971 for dosimetry without lead.

Conclusion:

The analytical model that was introduced in this project was able to reproduce the dosimetric data of the high energy linear accelerators to within 0.1% without the use of the lead foil.
SU‐E‐J‐112: The Impact of Cine EPID Image Acquisition Frame Rate On Markerless Soft‐Tissue Tracking
Authors: Yip S , Rottmann J , Berbeco R
Published: July 2014

Purpose:

Although reduction of the cine EPID acquisition frame rate through multiple frame averaging may reduce hardware memory burden and decrease image noise, it can hinder the continuity of soft‐tissue motion leading to poor auto‐tracking results. The impact of motion blurring and image noise on the tracking performance was investigated.

Methods:

Phantom and patient images were acquired at a frame rate of 12.87Hz on an AS1000 portal imager. Low frame rate images were obtained by continuous frame averaging. A previously validated tracking algorithm was employed for auto‐tracking. The difference between the programmed and auto‐tracked positions of a Las Vegas phantom moving in the superior‐inferior direction defined the tracking error (δ). Motion blurring was assessed by measuring the area change of the circle with the greatest depth. Additionally, lung tumors on 1747 frames acquired at eleven field angles from four radiotherapy patients are manually and automatically tracked with varying frame averaging. δ was defined by the position difference of the two tracking methods. Image noise was defined as the standard deviation of the background intensity. Motion blurring and image noise were correlated with δ using Pearson correlation coefficient (R).

Results:

For both phantom and patient studies, the auto‐tracking errors increased at frame rates lower than 4.29Hz. Above 4.29Hz, changes in errors were negligible with δ<1.60mm. Motion blurring and image noise were observed to increase and decrease with frame averaging, respectively. Motion blurring and tracking errors were significantly correlated for the phantom (R=0.94) and patient studies (R=0.72). Moderate to poor correlation was found between image noise and tracking error with R ‐0.58 and ‐0.19 for both studies, respectively.

Conclusion:

An image acquisition frame rate of at least 4.29Hz is recommended for cine EPID tracking. Motion blurring in images with frame rates below 4.39Hz can substantially reduce the accuracy of auto‐tracking.

This work is supported in part by the Varian Medical Systems, Inc.
SU‐E‐J‐113: The Influence of Optimizing Pediatric CT Simulator Protocols On the Treatment Dose Calculation in Radiotherapy
Authors: Zhang Y , Deng J , Zhang J , Hu Q , Tie J , Wu H
Published: July 2014

Purpose:

To investigate the possibility of applying optimized scanning protocols for pediatric CT simulation by quantifying the dosimetric inaccuracy introduced by using a fixed HU to density conversion.

Methods:

The images of a CIRS electron density reference phantom (Model 062) were acquired by a Siemens CT simulator (Sensation Open) using the following settings of tube voltage and beam current: 120 kV/190mA (the reference protocol used to calibrate CT for our treatment planning system (TPS)); Fixed 190mA combined with all available kV: 80, 100, and 140; fixed 120 kV and various current from 37 to 444 mA (scanner extremes) with interval of 30 mA. To avoid the HU uncertainty of point sampling in the various inserts of known electron densities, the mean CT numbers of the central cylindrical volume were calculated using DICOMan software. The doses per 100 MU to the reference point (SAD=100cm, Depth=10cm, Field=10×10cm, 6MV photon beam) in a virtual cubic phantom (30×30×30cm) were calculated using Eclipse TPS (calculation model: AcurosXB_11031) by assigning the CT numbers to HU of typical materials acquired by various protocols.

Results:

For the inserts of densities less than muscle, CT number fluctuations of all protocols were within the tolerance of 10 HU as accepted by AAPM‐TG66. For more condensed materials, fixed kV yielded stable HU with any mA combination where largest disparities were found in 1750mg/cc insert: HUreference=1801(106.6cGy), HUminimum=1799 (106.6cGy, errordose=0.00%), HUmaximum=1815 (106.8cGy, errordose=0.19%). Yet greater disagreements were observed with increasing density when kV was modified: HUminimum=1646 (104.5cGy, errordose=‐ 1.97%), HUmaximum=2487 (116.4cGy, errordose=9.19%) in 1750mg/cc insert.

Conclusion:

Without affecting treatment dose calculation, personalized mA optimization of CT simulator can be conducted by fixing kV for a better cost‐effectiveness of imaging dose and quality especially for children. Unless recalibrated, kV should be constant for all anatomical sites if diagnostic CT scanner is used as a simulator.

This work was partially supported by Capital Medical Development Scientific Research Fund of China.
SU‐E‐J‐115: Using Markov Chain Modeling to Elucidate Patterns in Breast Cancer Metastasis Over Time and Space
Authors: Comen E , Mason J , Nieva J , Newton P , Kuhn P , Norton L , Venkatappa N , Jochelson M
Published: July 2014

Purpose:

Traditionally, breast cancer metastasis is described as a process wherein cancer cells spread from the breast to multiple organ systems via hematogenous and lymphatic routes. Mapping organ specific patterns of cancer spread over time is essential to understanding metastatic progression. In order to better predict sites of metastases, here we demonstrate modeling of the patterned migration of metastasis.

Methods:

We reviewed the clinical history of 453 breast cancer patients from Memorial Sloan Kettering Cancer Center who were non‐metastatic at diagnosis but developed metastasis over time. We used the variables of organ site of metastases as well as time to create a Markov chain model of metastasis. We illustrate the probabilities of metastasis occurring at a given anatomic site together with the probability of spread to additional sites.

Results:

Based on the clinical histories of 453 breast cancer patients who developed metastasis, we have learned (i) how to create the Markov transition matrix governing the probabilities of cancer progression from site to site; (ii) how to create a systemic network diagram governing disease progression modeled as a random walk on a directed graph; (iii) how to classify metastatic sites as ‘sponges’ that tend to only receive cancer cells or ‘spreaders’ that receive and release them; (iv) how to model the time‐scales of disease progression as a Weibull probability distribution function; (v) how to perform Monte Carlo simulations of disease progression; and (vi) how to interpret disease progression as an entropy‐increasing stochastic process.

Conclusion:

Based on our modeling, metastatic spread may follow predictable pathways. Mapping metastasis not simply by organ site, but by function as either a ‘spreader’ or ‘sponge’ fundamentally reframes our understanding of metastatic processes. This model serves as a novel platform from which we may integrate the evolving genomic landscape that drives cancer metastasis. PS‐OC Trans‐Network Project Grant Award for “Data Assimilation and ensemble statistical forecasting methods applied to the MSKCC longitudinal metastatic breast cancer cohort.”
SU‐E‐J‐116: Validation of Deformable Image Registration Within the 4D Lung Using Two Commercial Algorithms and the User Guided Tool Reg Refine
Authors: Johnson P , Padgett K , Dogan N
Published: July 2014

Purpose:

To quantify the registration error between different phases of breathing using two commercially available deformable image registration algorithms. A secondary goal was to better understand the Reg Refine tool developed by MIM software and provide practical recommendations for its use.

Methods:

The 4D patient data sets of the POPI model were used as phantoms for this work. DIR was performed using MIM Maestro and the SmartAdapt module available within Eclipse. For each of six patients, 100 landmarks were deformed between inhalation and exhalation with their final location compared to the ground truth previously established by expert identification. The base rigid registration was varied to assess the effect on the DIR. For MIM, the Reg Refine tool was also used to try and improve the registration. The number of locked points was varied in addition to varying their location and the size of the window used for local box based alignment.

Results:

The average displacement for 5 of 6 patients was 6 – 8 mm. For these patients the mean error was low (1 – 1.5 mm) no matter which algorithm was used. SmartAdapt was less sensitive to the initial rigid registration, although both algorithms showed improvement when rigidly aligned to the base of the lung versus the apex. Reg Refine further improved accuracy albeit marginally. For the remaining patient the average displacement was larger (14 mm) and the Reg Refine tool showed marked improvement in reducing the mean and maximum errors from 5 and 35.5 mm to 1.1 and 4.8 mm using as little as ten points.

Conclusion:

The residual registration error using both SmartAdapt and MIM Maestro for 4D lung patients has been quantified on the order of 1 – 1.5 mm using the POPI model. Reg Refine was also shown effective at improving registrations within MIM Maestro.
SU‐E‐J‐117: Verification Method for the Detection Accuracy of Automatic Winston Lutz Test
Authors: Tang A , Chan K , Fee F , Chau R
Published: July 2014

Purpose:

Winston Lutz test (WLT) has been a standard QA procedure performed prior to SRS treatment, to verify the mechanical iso‐center setup accuracy upon different Gantry/Couch movements. Several detection algorithms exist,for analyzing the ball‐radiation field alignment automatically. However, the accuracy of these algorithms have not been fully addressed. Here, we reveal the possible errors arise from each step in WLT, and verify the software detection accuracy with the Rectilinear Phantom Pointer (RLPP), a tool commonly used for aligning treatment plan coordinate with mechanical iso‐center.

Methods:

WLT was performed with the radio‐opaque ball mounted on a MIS and irradiated onto EDR2 films. The films were scanned and processed with an in‐house Matlab program for automatic iso‐center detection. Tests were also performed to identify the errors arise from setup, film development and scanning process. The radioopaque ball was then mounted onto the RLPP, and offset laterally and longitudinally in 7 known positions (0, ±0.2, ±0.5, ±0.8 mm) manually for irradiations. The gantry and couch was set to zero degree for all irradiation. The same scanned images were processed repeatly to check the repeatability of the software.

Results:

Miminal discrepancies (mean=0.05mm) were detected with 2 films overlapped and irradiated but developed seperately. This reveals the error arise from film processer and scanner alone. Maximum setup errors were found to be around 0.2mm, by analyzing data collected from 10 irradiations over 2 months. For the known shift introduced using the RLPP, the results agree with the manual offset, and fit linearly (R2>0.99) when plotted relative to the first ball with zero shift.

Conclusion:

We systematically reveal the possible errors arise from each step in WLT, and introduce a simple method to verify the detection accuracy of our in‐house software using a clinically available tool.
SU‐E‐J‐119: What Effect Have the Volume Defined in the Alignment Clipbox for Cervical Cancer Using Automatic Registration Methods for Cone‐ Beam CT Verification?
Authors: Wang W , Yang H , Wang Y , Jia H , Xie Y
Published: July 2014

Purpose:

To investigate the impact of different clipbox volumes with automated registration techniques using commercially available software with on board volumetric imaging(OBI) for treatment verification in cervical cancer patients.

Methods:

Fifty cervical cancer patients received daily CBCT scans(on‐board imaging v1.5 system, Varian Medical Systems) during the first treatment week and weekly thereafter were included this analysis. A total of 450 CBCT scans were registered to the planning CTscan using pelvic clipbox(clipbox‐Pelvic) and around PTV clip box(clipbox‐ PTV). The translations(anterior‐posterior, left‐right, superior‐inferior) and the rotations(yaw, pitch and roll) errors for each matches were recorded. The setup errors and the systematic and random errors for both of the clip‐boxes were calculated. Paired Samples t test was used to analysis the differences between clipbox‐Pelvic and clipbox‐PTV.

Results:

. The SD of systematic error(σ) was 1.0mm, 2.0mm,3.2mm and 1.9mm,2.3mm, 3.0mm in the AP, LR and SI directions for clipbox‐Pelvic and clipbox‐PTV, respectively. The average random error(Σ)was 1.7mm, 2.0mm,4.2mm and 1.7mm,3.4mm, 4.4mm in the AP, LR and SI directions for clipbox‐Pelvic and clipbox‐PTV, respectively. But, only the SI direction was acquired significantly differences between two image registration volumes(p=0.002,p=0.01 for mean and SD). For rotations, the yaw mean/SD and the pitch SD were acquired significantly differences between clipbox‐Pelvic and clipbox‐PTV.

Conclusion:

The defined volume for Image registration is important for cervical cancer when 3D/3D match was used. The alignment clipbox can effect the setup errors obtained. Further analysis is need to determine the optimal defined volume to use the image registration in cervical cancer. Conflict of interest: none.
SU‐E‐J‐121: Measuring Prompt Gamma Emission Profiles with a Multi‐Stage Compton Camera During Proton Beam Irradiaition: Initial Studies
Authors: Polf J , Brown S , Mann J , Mackin D , Zheng Y , McCleskey M , Beddar S , He Z
Published: July 2014

Purpose:

Recent studies have suggested that the characteristics of prompt gammas (PG) emitted during proton beam irradiation are advantageous for determining beam range during treatment delivery. The purpose of this work was to determine the feasibility of determining the proton beam range from PG data measured with a prototype Compton camera (CC) during proton beam irradiation.

Methods:

Using a prototype multi‐stage CC the PG emission from a water phantom was measured during irradiation with clinical proton therapy beams. The measured PG emission data was used to reconstruct an image of the PG emission using a backprojection reconstruction algorithm. One dimensional (1D) profiles extracted from the PG images were compared to: 1) PG emission data measured at fixed depths using collimated high purity Germanium and Lanthanum Bromide detectors, and 2) the measured depth dose profiles of the proton beams.

Results:

Comparisons showed that the PG emission profiles reconstructed from CC measurements agreed very well with the measurements of PG emission as a function of depth made with the collimated detectors. The distal falloff of the measured PG profile was between 1 mm to 4 mm proximal to the distal edge of the Bragg peak for proton beam ranges from 4 cm to 16 cm in water. Doses of at least 5 Gy were needed for the CC to measure sufficient data to image the PG profile and localize the distal PG falloff.

Conclusion:

Initial tests of a prototype CC for imaging PG emission during proton beam irradiation indicated that measurement and reconstruction of the PG profile was possible. However, due to limitations of the operational parameters (energy range and count rate) of the current CC prototype, doses of greater than a typical treatment dose (∼2 Gy) were needed to measure adequate PG signal to reconstruct viable images.

Funding support for this project provided by a grant from DoD.
SU‐E‐J‐123: Targeting Accuracy of Image‐Guided Radiosurgery for Intracranial Lesions
Authors: Huang Y , Wen N , Zhao B , Kim J , Gordon J , Chetty I
Published: July 2014

Purpose:

To evaluate the setup accuracies of image‐guided intracranial radiosurgery across several different linear accelerator platforms.

Methods:

A CT scan with a slice thickness of 1.0 mm was acquired of a Rando head phantom (The Phantom Laboratory) in a U‐frame mask (BrainLAB AG). The phantom had three embedded BBs, simulating a central, left, and anterior lesion. The phantom was setup with each BB placed at the radiation isocenter under image guidance. Four different setup procedures were investigated: (1) NTX_ExacTrac: 6 degree‐of‐freedom (6D) correction on a Novalis Tx (BrainLAB AG) with ExacTrac localization (BrainLAB AG); (2) NTX_CBCT: 4D correction on the Novalis Tx with cone‐beam computed tomography (CBCT); (3) TrueBeam_CBCT: 4D correction on a TrueBeam (Varian) with CBCT; (4) Edge_CBCT: 6D correction on an Edge (Varian) with CBCT. The experiment was repeated 5 times with different initial setup error at each BB location on each platform, and the mean (μ) and one standard deviation (σ) of the residual error was compared. The congruence between radiation and imaging isocenters on each platform was evaluated by acquiring Winston Lutz (WL) images of a WL jig followed by imaging using ExacTrac or CBCT. The difference in coordinates of the jig relative to radiation and imaging isocenters was then recorded.

Results:

Averaged over all three BB locations, the residual vector setup errors (μ±σ) of the phantom in mm were 0.6±0.2, 1.0±0.5, 0.2±0.1, and 0.3±0.1 on NTX_ExacTrac, NTX_CBCT, TrueBeam_CBCT, and Edge_CBCT, with their ranges in mm being 0.4∼1.1, 0.4∼1.9, 0.1∼0.5, and 0.2∼0.6, respectively. And imaging isocenter was found stable relative to radiation isocenter, with the congruence to radiation isocenter in mm being 0.6±0.1, 0.7±0.1, 0.3±0.1, 0.2±0.1, respectively, on the four systems in the same order.

Conclusion:

Millimeter accuracy can be achieved with image‐guided radiosurgery for intracranial lesions based on this set of experiments.
SU‐E‐J‐124: FDG PET Metrics Analysis in the Context of An Adaptive PET Protocol for Node Positive Gynecologic Cancer Patients
Authors: Nawrocki J , Chino J , Light K , Vergalasova I , Craciunescu O
Published: July 2014

Purpose:

To compare PET extracted metrics and investigate the role of a gradient‐based PET segmentation tool, PET Edge (MIM Software Inc., Cleveland, OH), in the context of an adaptive PET protocol for node positive gynecologic cancer patients.

Methods:

An IRB approved protocol enrolled women with gynecological, PET visible malignancies. A PET‐CT was obtained for treatment planning prescribed to 45–50.4Gy with a 55– 70Gy boost to the PET positive nodes. An intra‐treatment PET‐CT was obtained between 30–36Gy, and all volumes re‐contoured. Standard uptake values (SUVmax, SUVmean, SUVmedian) and GTV volumes were extracted from the clinician contoured GTVs on the pre‐ and intra‐treament PET‐CT for primaries and nodes and compared with a two tailed Wilcoxon signed‐rank test. The differences between primary and node GTV volumes contoured in the treatment planning system and those volumes generated using PET Edge were also investigated. Bland‐Altman plots were used to describe significant differences between the two contouring methods.

Results:

Thirteen women were enrolled in this study. The median baseline/intra‐treatment primary (SUVmax, mean, median) were (30.5, 9.09, 7.83)/(16.6, 4.35, 3.74), and nodes were (20.1, 4.64, 3.93)/(6.78, 3.13, 3.26). The p values were all < 0.001. The clinical contours were all larger than the PET Edge generated ones, with mean difference of +20.6 ml for primary, and +23.5 ml for nodes. The Bland‐Altman revealed changes between clinician/PET Edge contours to be mostly within the margins of the coefficient of variability. However, there was a proportional trend, i.e. the larger the GTV, the larger the clinical contours as compared to PET Edge contours.

Conclusion:

Primary and node SUV values taken from the intratreament PET‐CT can be used to assess the disease response and to design an adaptive plan. The PET Edge tool can streamline the contouring process and lead to smaller, less user‐dependent contours.
SU‐E‐J‐126: Respiratory Gating Quality Assurance: A Simple Method to Achieve Millisecond Temporal Resolution
Authors: McCabe B , Wiersma R
Published: July 2014

Purpose:

Low temporal latency between a gating on/off signal and a linac beam on/off during respiratory gating is critical for patient safety. Although, a measurement of temporal lag is recommended by AAPM Task Group 142 for commissioning and annual quality assurance, there currently exists no published method. Here we describe a simple, inexpensive, and reliable method to precisely measure gating lag at millisecond resolutions.

Methods:

A Varian Real‐time Position Management™ (RPM) gating simulator with rotating disk was modified with a resistive flex sensor (Spectra Symbol) attached to the gating box platform. A photon diode was placed at machine isocenter. Output signals of the flex sensor and diode were monitored with a multichannel oscilloscope (Tektronix™ DPO3014). Qualitative inspection of the gating window/beam on synchronicity were made by setting the linac to beam on/off at end‐expiration, and the oscilloscope's temporal window to 100 ms to visually examine if the on/off timing was within the recommended 100‐ms tolerance. Quantitative measurements were made by saving the signal traces and analyzing in MatLab™. The on and off of the beam signal were located and compared to the expected gating window (e.g. 40% to 60%). Four gating cycles were measured and compared.

Results:

On a Varian TrueBeam™ STx linac with RPM gating software, the average difference in synchronicity at beam on and off for four cycles was 14 ms (3 to 30 ms) and 11 ms (2 to 32 ms), respectively. For a Varian Clinac™ 21EX the average difference at beam on and off was 127 ms (122 to 133 ms) and 46 ms (42 to 49 ms), respectively. The uncertainty in the synchrony difference was estimated at ±6 ms.

Conclusion:

This new gating QA method is easy to implement and allows for fast qualitative inspection and quantitative measurements for commissioning and TG‐142 annual QA measurements.
SU‐E‐J‐131: Automatic Detection of Gold Fiducial Markers On MR Images Using the RASOR Technique
Authors: Seevinck P , van den Berg C A T , Moerland M A , Philippens M , Lagendijk J
Published: July 2014

Purpose:

The detection of gold fiducial markers on MR images is important for CT‐MR registration and will be key to upcoming MRI‐only treatment planning. Visualization of gold fiducials with MRI is, however, challenging as seeds lead to small signal voids that are difficult to detect on regular MR scans. Here, we demonstrate that by using a balanced radial SSFP sequence in combination with an off‐resonance reconstruction, gold fiducials can be given a positive contrast facilitating automatic detection.

Methods:

Three gold fiducials (HA2 Medizintechnik, Germany, 1x 5 mm²) were placed in three orthogonal orientations in an agarose gel phantom. We used a 3D stack of stars transient balanced gradient echo (tbGE) sequence with SPAIR fat suppression (TR/TE = 3.3/1.63ms, 1×1×2 mm³ acquired voxels, BW=1085Hz/vox). To create positive contrast at the seed locations the RASOR technique employs an additional off‐resonance reconstruction at 3000 Hz. Background‐suppression was obtained by subtracting the on‐resonance image twice from the off‐resonance image.

Results:

The short echo time of the sequence combined with a “balanced” gradient scheme resulted in well localized and sharp signal voids at fiducial locations. By employing RASOR reconstruction the gold fiducials were depicted hyperintense and allowed determination of the locatation and orientation of the fiducials. The radial readout ensures accurate localization as any susceptibility induced geometrical shift for a radial readout line, is balanced by an opposite radial readout. Thus the centre‐of‐gravity of the hyperintensity reflects the true position of the fiducials.

Conclusion:

A radial balanced sequence in combination with short TR and TR and high spatial resolution, results in accurate depiction of gold fiducials while being standardly available on MR systems. The RASOR reconstruction results in a positive contrast facilitating automatic detection. This enables automatic registration between CT and MRI based on fiducial markers, and in the may warrant an MRI‐only planning approach.
SU‐E‐J‐134: An Augmented‐Reality Optical Imaging System for Accurate Breast Positioning During Radiotherapy
Authors: Nazareth D , Malhotra H , French S , Hoffmann K , Merrow C
Published: July 2014

Purpose:

Breast radiotherapy, particularly electronic compensation, may involve large dose gradients and difficult patient positioning problems. We have developed a simple self‐calibrating augmented‐reality system, which assists in accurately and reproducibly positioning the patient, by displaying her live image from a single camera superimposed on the correct perspective projection of her 3D CT data. Our method requires only a standard digital camera capable of live‐view mode, installed in the treatment suite at an approximately‐known orientation and position (rotation R; translation T).

Methods:

A 10‐sphere calibration jig was constructed and CT imaged to provide a 3D model. The (R,T) relating the camera to the CT coordinate system were determined by acquiring a photograph of the jig and optimizing an objective function, which compares the true image points to points calculated with a given candidate R and T geometry. Using this geometric information, 3D CT patient data, viewed from the camera's perspective, is plotted using a Matlab routine. This image data is superimposed onto the real‐time patient image, acquired by the camera, and displayed using standard live‐view software. This enables the therapists to view both the patient's current and desired positions, and guide the patient into assuming the correct position. The method was evaluated using an in‐house developed bolus‐like breast phantom, mounted on a supporting platform, which could be tilted at various angles to simulate treatment‐like geometries.

Results:

Our system allowed breast phantom alignment, with an accuracy of about 0.5 cm and 1 ± 0.5 degree. Better resolution could be possible using a camera with higher‐zoom capabilities.

Conclusion:

We have developed an augmented‐reality system, which combines a perspective projection of a CT image with a patient's real‐time optical image. This system has the potential to improve patient setup accuracy during breast radiotherapy, and could possibly be used for other disease sites as well.
SU‐E‐J‐139: Fuzzy Clustering Segmentation of Glioblastoma in T1‐MRI Imaging for Clinical Trials
Authors: Cordova J S , Schreibmann Eduard , Hadjipanayis Constantinos G. , Holder Chad A. , Bansal Vivek , Julio Sepulvedad , Hasan Danish , Guo Ying , Fox Tim H. , Crocker Ian R. , Shu Hui‐Kuo G. , Shim Hyunsuk
Published: July 2014

Purpose:

Generating brain tumor volume measurements in a reproducible and efficient manner is a difficult, yet necessary, component of response assessment. The purpose of this study was to adapt and validate a multilevel Fuzzy C‐means clustering algorithms for ROI tumor segmentation to allow consistent volumetric comparisons at multiple sites.

Methods:

Preoperative contrast‐enhanced T1W images from 37 glioblastoma cases were segmented using Fuzzy C‐means clustering‐based methods and compared to manually contoured volumes created by specialists. The same was done post‐operatively, using subtracted images to eliminate intrinsically T1‐hyperintense material (blood). Volume computations based on the MacDonald criteria were also used for comparison. Agreement and inter‐rater variability between volumes produced with each method was assessed by determining the concordance correlation coefficient (CCC).

Results:

The MacDonald criteria method had poor agreement (CCC=0.350–0.972) with manual contouring pre‐ and postoperatively, while the proposed semi‐automated methods exhibited very high agreement (CCC=0.839–0.995) with manual contouring before and after resection. Fuzzy C‐means clustering with three classes was the most robust semi‐automated method, showing better inter‐rater agreement than the MacDonald criteria method for both pre‐ (CCC of 0.990 and 0.975, respectively) and post‐operative cases (CCC of 0.983 and 0.576, respectively). Post‐operative inter‐rater agreement was significantly different between these methods (p < 0.001).

Conclusion:

The proposed semi‐automated segmentation methods allow tumor volume measurements of MR images in a reliable and reproducible fashion necessary for measuring treatment response in glioblastoma patients in multicenter clinical trials.
SU‐E‐J‐148: Evaluating Tumor Response with a Commercially Available Deformable Registration System
Authors: Bowling J , Ramsey C
Published: July 2014

Purpose:

The purpose of this study is to present a method for evaluating the response to treatment using a commercially available deformable image registration software package (Velocity Medical Systems) and repeat PET/CT imaging. This technique can be used to identify volumes that are risk for tumor recurrence.

Methods:

Response to treatment was evaluated using PET/CT images acquired prior‐to and post‐treatment for radiation therapy patients treated with concurrent chemotherapy. Velocity (Version 3.0.1) was used to deform the initial PET/CT to the post treatment PET/CT. The post‐treatment PET images were then subtracted from the pre‐treatment PET images. The resulting re‐sampled image is a three‐dimensional SUV difference map that shows pixels with increasing SUV values. SUV values increases greater than 2.5 in the post treatment images were identified for additional follow‐up.

Results:

A total of 5 Lung patients were analyzed as part of this study. One lung patient in the cohort had an SUV increase of +3.28 that was identified using the SUV difference map. This volume of increased uptake was located outside the treatment field and adjacent to the 35 Gy isodose line. The remaining four patients all had SUV decreases inside the planning target volume, and no unexpected areas of increase outside the irradiated volumes. All five patients were analyzed using standard tools inside the Velocity application.

Conclusion:

The response to treatment can easily be measured using serial PET/CT images and a commercially available deformable image registration. This provides both the radiation oncologists and medical oncologists with a quantitative assessment of their treatment to use in patient follow‐up.
SU‐E‐J‐154: Deformable Image Registration Based Delivered Dose Estimation for Head and Neck Radiotherapy
Authors: Kumarasiri A , Liu C , Chetvertkov M , Gordon J , Siddiqui F , Chetty I , Kim J
Published: July 2014

Purpose:

To estimate the accumulated dose to targets and organs at risk (OAR) for head and neck (H' N) radiotherapy using 3 deformable image registration (DIR) algorithms.

Methods:

Five H' N patients, who had daily CBCTs taken during the course of treatment, were retrospectively studied. All plans had 5 mm CTV‐to‐PTV expansions. To overcome the small field of view (FOV) limitations and HU uncertainties of CBCTs, CT images were deformably registered using a parameter‐optimized B‐spline DIR algorithm (Elastix, elastix.isi.uu.nl) and resampled onto each CBCT with a 4 cm uniform FOV expansion. The dose of the day was calculated on these resampled CT images. Calculated daily dose matrices were warped and accumulated to the planning CT using 3 DIR algorithms; SmartAdapt (Eclipse/Varian), Velocity (Velocity Medical Solutions), and Elastix. Dosimetric indices for targets and OARs were determined from the DVHs and compared with corresponding planned quantities.

Results:

The cumulative dose deviation was less than 2%, on average, for PTVs from the corresponding plan dose, for all algorithms/patients. However, the parotids show as much as a 37% deviation from the intended dose, possibly due to significant patient weight loss during the first 3 weeks of treatment (15.3 lbs in this case). The mean(±SD) cumulative dose deviations of the 5 patients estimated using the 3 algorithms (SmartAdapt, Velocity, and Elastix) were (0.8±0.9%, 0.5±0.9%, 0.6±1.3%) for PTVs, (1.6±1.9%, 1.4±2.0%, 1.7±1.9%) for GTVs, (10.4±12.1%, 10.7±10.6%, 6.5±10.1%) for parotid glands, and (4.5±4.6%, 3.4±5.7%, 3.9±5.7%) for mucosa, respectively. The differences among the three DIR algorithms in the estimated cumulative mean doses (1SD (in Gy)) were: 0.1 for PTVs, 0.1 for GTVs, 1.9 for parotid glands, and 0.4 for mucosa.

Conclusion:

Results of this study are suggestive that more frequent plan adaptation for organs, such as the parotid glands, might be beneficial during the course of H' N RT.

This study was supported in part by a research grant from Varian Medical Systems, Palo Alto, CA
SU‐E‐J‐155: Utilizing Varian TrueBeam Developer Mode for the Quantification of Mechanical Limits and the Simulation of 4D Respiratory Motion
Authors: Moseley D , Dave M
Published: July 2014

Purpose:

Use Varian TrueBeam Developer mode to quantify the mechanical limits of the couch and to simulate 4D respiratory motion.

Methods:

An in‐house MATLAB based GUI was created to make the BEAM XML files. The couch was moved in a triangular wave in the S/I direction with varying amplitudes (1mm, 5mm, 10mm, and 50mm) and periods (3s, 6s, and 9s). The periods were determined by specifying the speed. The theoretical positions were compared to the values recorded by the machine at 50 Hz. HD videos were taken for certain tests as external validation. 4D Respiratory motion was simulated by an A/P MV beam being delivered while the couch moved in an elliptical manner. The ellipse had a major axis of 2 cm (S/I) and a minor axis of 1 cm (A/P).

Results:

The path planned by the TrueBeam deviated from the theoretical triangular form as the speed increased. Deviations were noticed starting at a speed of 3.33 cm/s (50mm amplitude, 6s period). The greatest deviation occurred in the 50mm‐ 3s sequence with a correlation value of −0.13 and a 27% time increase; the plan essentially became out of phase. Excluding these two, the plans had correlation values of 0.99. The elliptical sequence effectively simulated a respiratory pattern with a period of 6s. The period could be controlled by changing the speeds or the dose rate.

Conclusion:

The work first shows the quantification of the mechanical limits of the couch and the speeds at which the proposed plans begin to deviate. These limits must be kept in mind when programming other couch sequences. The methodology can be used to quantify the limits of other axes. Furthermore, the work shows the possibility of creating 4D respiratory simulations without using specialized phantoms or motion‐platforms. This can be further developed to program patient‐specific breathing patterns.
SU‐E‐J‐157: Improving the Quality of T2‐Weighted 4D Magnetic Resonance Imaging for Clinical Evaluation
Authors: Du D , Caruthers S , Glide‐Hurst C , Low D , Mutic S , Hu Y
Published: July 2014

Purpose:

To develop an imaging technique that enables us to acquire T2‐ weighted 4D Magnetic Resonance Imaging (4DMRI) with sufficient spatial coverage, temporal resolution and spatial resolution for clinical evaluation.

Methods:

T2‐weighed 4DMRI images were acquired from a healthy volunteer using a respiratory amplitude triggered T2‐weighted Turbo Spin Echo sequence. 10 respiratory states were used to equally sample the respiratory range based on amplitude (0%, 20%i, 40%i, 60%i, 80%i, 100%, 80%e, 60%e, 40%e and 20%e). To avoid frequent scanning halts, a methodology was devised that split 10 respiratory states into two packages in an interleaved manner and packages were acquired separately. Sixty 3mm sagittal slices at 1.5mm in‐plane spatial resolution were acquired to offer good spatial coverage and reasonable spatial resolution. The in‐plane field of view was 375mm × 260mm with nominal scan time of 3 minutes 42 seconds. Acquired 2D images at the same respiratory state were combined to form the 3D image set corresponding to that respiratory state and reconstructed in the coronal view to evaluate whether all slices were at the same respiratory state. 3D image sets of 10 respiratory states represented a complete 4D MRI image set.

Results:

T2‐weighted 4DMRI image were acquired in 10 minutes which was within clinical acceptable range. Qualitatively, the acquired MRI images had good image quality for delineation purposes. There were no abrupt position changes in reconstructed coronal images which confirmed that all sagittal slices were in the same respiratory state.

Conclusion:

We demonstrated it was feasible to acquire T2‐weighted 4DMRI image set within a practical amount of time (10 minutes) that had good temporal resolution (10 respiratory states), spatial resolution (1.5mm × 1.5mm × 3.0mm) and spatial coverage (60 slices) for future clinical evaluation.
SU‐E‐J‐166: Comprehensive Evaluation of Three Deformation Image Registration Algorithms Using Digital Phantoms Created From Patients with Head and Neck Cancer
Authors: Shen Z , Bzdusek K , Shang Q , Xia P
Published: July 2014

Purpose:

To create digital phantoms using paired planning and replanning CTs from patients with H' N cancer and to comprehensively evaluate three commonly‐used deformable image registration (DIR) algorithms with known ROIs and volumetric doses from the established phantoms.

Methods:

We have identified five H' N patients with adaptive‐IMRT. For each patient, manually‐adjusted meshes were created for selected physicianapproved ROIs. These meshes were applied to drive the thin‐plate spline algorithm to deform the planning CT to match the replanning CT. The digital phantom consisted of the planning CT and simulated replanning CT, which were linked by a known deformation vector field (DVF). Three DIR algorithms (Fast‐Symmetric Demons, B‐Spline, and intensity‐based) were applied to these five digital phantoms. The images, ROIs, and volumetric doses were mapped from the planning CT to the simulated replanning CT using the DVFs computed by these three DIRs and compared to those using the known DVF.

Results:

The average normalized cross‐correlation (NCC) value between planning and replanning CTs with rigid registration was 0.924, indicating large anatomical changes. The NCC values were improved to 0.998, 0.974, and 0.996, with Demons, B‐Spline, and intensity‐based DIRs, respectively. The mean Dice coefficients for selected ROIs (e.g. PTV, brainstem, spinal cord, mandible, parotids) were in the range of 0.87‐0.95, 0.84–0.94, and 0.85–0.95 for Demons, B‐Spline, and intensity‐based DIRs, respectively. The mean slice‐wise Hausdorff distances for selected ROIs were in the range of 1.8–4.9 mm, 2.2–5.4 mm, and 2.4–6.6 mm for Demons, B‐Spline, and intensity‐based DIRs, respectively. The average percentage of voxels in the external contours that have dose errors <2Gy were 88.6%, 85.7%, and 90.7% for Demons, B‐Spline, and intensity‐based DIRs, respectively.

Conclusion:

All three DIR algorithms were very comparable. The deformed images, ROIs, and volumetric doses obtained by these three DIRs agreed well with those mapped from the known DVF.

This work was supported by a research grant from Philips Healthcare. Karl Bzdusek is an employee of Philips Healthcare.