bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐213: Visualization of Scans and Metrics for Longitudinal Informatics
Authors: Apte A , Veeraraghavan H , Tyagi N , Kijewski P , Deasy J
Published: June 2015

Purpose:

To present new tools for visualizing scans across multiple timepoints alongwith changes in critical metrics.

Method:

MR scans for a Lung cancer patient were acquired at four different timepoints. A display consisting of 2×2 panels was developed to show the four scans in Transverse, Sagittal or Coronal cut‐planes. In order to make sure the user is looking at the same scan‐section at different timepoints, two kinds of registration modes were built in: (1) Anatomical registration: Scans at different time points are rigidly registered to the first timepoint. Here, regular anatomical structures like bones are used to drive the registration. (2) Matching tumor mid‐plane plane: In this mode the mid‐planes of the tumors are matched to register the scans. This mode is useful to users who are interested in looking at the tumor shrinkage/growth longitudinally. Once image registration is performed according to the selected mode, the user can scroll across scan slices together at different timepoints. It is possible to make fine adjustments to the image registration by nudging a particular timepoint by few slices. It is also possible to evaluate the accuracy of registration by using the spotlight tool that highlights the region selected on one of the timepoints, on all the timepoints. Linear interpolation from the neighboring scan slices is used to display a cut‐plane located in between slices. The visualization is further improved using the sinc upsampling filter. Once the scans at different timepoints are registered, it is possible to extract metrics for the structures of interest. Currently, statistical features and the first and second order texture features could be extracted at different timepoints.

Results:

The new features and updates are built into CERR.

Conclusion:

The tool developed would help visualize scans taken longitudinally along with providing insight into how the critical metrics change over time.
SU‐E‐J‐219: A Dixon Based Pseudo‐CT Generation Method for MR‐Only Radiotherapy Treatment Planning of the Pelvis and Head and Neck
Authors: Maspero M. , Seevinck P.R. , Meijer G.J. , Lagendijk J.J.W. , Viergever M.A. , van den Berg C.A.T.
Published: June 2015

Purpose:

To develop an image processing method for MRI‐based generation of electron density maps, known as pseudo‐CT (pCT), without usage of model‐ or atlas‐based segmentation, and to evaluate the method in the pelvic and head‐neck region against CT.

Methods:

CT and MRI scans were obtained from the pelvic region of four patients in supine position using a flat table top only for CT. Stratified CT maps were generated by classifying each voxel based on HU ranges into one of four classes: air, adipose tissue, soft tissue or bone.A hierarchical region‐selective algorithm, based on automatic thresholding and clustering, was used to classify tissues from MR Dixon reconstructed fat, In‐Phase (IP) and Opposed‐Phase (OP) images. First, a body mask was obtained by thresholding the IP image. Subsequently, an automatic threshold on the Dixon fat image differentiated soft and adipose tissue. K‐means clustering on IP and OP images resulted in a mask that, via a connected neighborhood analysis, allowing the user to select the components corresponding to bone structures.The pCT was estimated through assignment of bulk HU to the tissue classes. Bone‐only Digital Reconstructed Radiographs (DRR) were generated as well. The pCT images were rigidly registered to the stratified CT to allow a volumetric and voxelwise comparison. Moreover, pCTs were also calculated within the head‐neck region in two volunteers using the same pipeline.

Results:

The volumetric comparison resulted in differences <1% for each tissue class. A voxelwise comparison showed a good classification, ranging from 64% to 98%. The primary misclassified classes were adipose/soft tissue and bone/soft tissue. As the patients have been imaged on different table tops, part of the misclassification error can be explained by misregistration.

Conclusion:

The proposed approach does not rely on an anatomy model providing the flexibility to successfully generate the pCT in two different body sites.

This research is founded by ZonMw IMDI Programme, project name: “RASOR sharp: MRI based radiotherapy planning using a single MRI sequence”, project number: 10‐104003010
SU‐E‐J‐229: Magnetic Resonance Imaging of Small Fiducial Markers for Proton Beam Therapy
Authors: Hu Y , James J , Panda A , Vargas C , Silva A , Liu W , Shen J , Ding X , Paden R , Hanson J , Wong W , Schild S , Bues M
Published: June 2015

Purpose:

For proton beam therapy, small fiducial markers are preferred for patient alignment due to less interference with the proton beam. Visualizing small fiducial markers can be challenging in MRI. This study intends to investigate MRI imaging protocols for better visualization of small fiducial markers.

Methods:

Two carbon and two coil‐shaped gold markers were placed into a gel phantom. Both carbon markers had a diameter of 1mm and a length of 3mm. Both gold markers had a length of 5mm. One gold marker had a diameter of 0.5mm and the other had a diameter of 0.75mm. T1 VIBE, T2 SPACE, TrueFISP and susceptibility weighted (SW) images were acquired. To improve marker contrast, high spatial resolution was used to reduce partial volume effect. Slice thickness was 1.5mm for all four sequences and in‐plane resolution was 0.6mm for TrueFISP, 0.7mm for T1 VIBE, and 0.8mm for T2 SPACE and SW. For comparison purpose, a 3D T1 VIBE image set at 3mm slice thickness and 1.2mm in‐plane resolution was also acquired.

Results:

All markers were visible in all high‐resolution image sets. In each image set, marker‐induced signal void was the smallest (in diameter) for carbon markers, followed by the 0.5mm gold marker and the largest for the 0.75mm gold marker. The SW images had the largest marker‐induced signal void. However, those might be confused by susceptibility‐gradient‐induced signal voids. T1 VIBE had good visualization of markers with nicely defined edges. T2 SPACE had reasonable visualization of markers but edges were slightly blurred. TrueFISP had good visualization of markers only if they were not masked by banding artifacts. As a comparison, all markers were hardly visible in the standard resolution T1 VIBE images.

Conclusion:

3D high‐resolution T1 VIBE and SW have great potential in providing good visualization of small fiducial markers for proton beam therapy.
SU‐E‐J‐230: Evaluation of ViewRay 0.35 T MRI Normal Structure Segmentation
Authors: Paliwal B , Asprey W , Yan Y , Saenz D , Bayouth J
Published: June 2015

Purpose:

In order to take advantage of the high resolution soft tissue imaging available in MR images, we investigated 3D images obtained with the low field 0.35 T MR in ViewRay to serve as an alternative to CT scans for radiotherapy treatment planning. In these images, normal and target structure delineation can be visualized. Assessment is based upon comparison with the CT images and the ability to produce comparable contours.

Methods:

Routine radiation oncology CT scans were acquired on five patients. Contours of brain, brainstem, esophagus, heart, lungs, spinal cord, and the external body were drawn. The same five patients were then scanned on the ViewRay TrueFISP‐based imaging pulse sequence. The same organs were selected on the MR images and compared to those from the CT scan. Physical volume and the Dice Similarity Coefficient (DSC) were used to assess the contours from the two systems. Image quality stability was quantitatively ensured throughout the study following the recommendations of the ACR MR accreditation procedure.

Results:

The highest DSC of 0.985, 0.863, and 0.843 were observed for brain, lungs, and heart respectively. On the other hand, the brainstem, spinal cord, and esophagus had the lowest DSC. Volume agreement was most satisfied for the heart (within 5%) and the brain (within 2%). Contour volume for the brainstem and lung (a widely dynamic organ) varied the most (27% and 19%).

Conclusion:

The DSC and volume measurements suggest that the results obtained from ViewRay images are quantitatively consistent and comparable to those obtained from CT scans for the brain, heart, and lungs. MR images from ViewRay are well‐suited for treatment planning and for adaptive MRI‐guided radiotherapy. The physical data from 0.35 T MR imaging is consistent with our geometrical understanding of normal structures.
SU‐E‐J‐233: Effect of Brachytherapy Seed Artifacts in T2 and Proton Density Maps in MR Images
Authors: Mashouf S , Fatemi‐Ardekani A , Song W
Published: June 2015

Purpose:

This study aims at investigating the influence of brachytherapy seeds on T2 and proton density (PD) maps generated from MR images. Proton density maps can be used to extract water content. Since dose absorbed in tissue surrounding low energy brachytherapy seeds are highly influenced by tissue composition, knowing the water content is a first step towards implementing a heterogeneity correction algorithm using MR images.

Methods:

An LDR brachytherapy (IsoAid Advantage Pd‐103) seed was placed in the middle of an agar‐based gel phantom and imaged using a 3T Philips MR scanner with a 168‐channel head coil. A multiple echo sequence with TE=20, 40, 60, 80, 100 (ms) with large repetition time (TR=6259ms) was used to extract T2 and PD maps.

Results:

Seed artifacts were considerably reduced on T2 maps compared to PD maps. The variation of PD around the mean was obtained as −97% to 125% (±1%) while for T2 it was recorded as −71% to 24% (±1%).

Conclusion:

PD maps which are required for heterogeneity corrections are susceptible to artifacts from seeds. Seed artifacts on T2 maps, however, are significantly reduced due to not being sensitive to B0 field variation.
SU‐E‐J‐235: Audiovisual Biofeedback Improves the Correlation Between Internal and External Respiratory Motion
Authors: Lee D , Greer P , Ludbrook J , Paganelli C , Pollock S , Kim T , Keall P
Published: June 2015

Purpose:

External respiratory surrogates are often used to predict internal lung tumor motion for beam gating but the assumption of correlation between external and internal surrogates is not always verified resulting in amplitude mismatch and time shift. To test the hypothesis that audiovisual (AV) biofeedback improves the correlation between internal and external respiratory motion, in order to improve the accuracy of respiratory‐gated treatments for lung cancer radiotherapy.

Methods:

In nine lung cancer patients, 2D coronal and sagittal cine‐MR images were acquired across two MRI sessions (pre‐ and mid‐treatment) with (1) free breathing (FB) and (2) AV biofeedback. External anterior‐posterior (AP) respiratory motions of (a) chest and (b) abdomen were simultaneously acquired with physiological measurement unit (PMU, 3T Skyra, Siemens Healthcare Erlangen, Germany) and real‐time position management (RPM) system (Varian, Palo Alto, USA), respectively. Internal superior‐inferior (SI) respiratory motions of (c) lung tumor (i.e. centroid of auto‐segmented lung tumor) and (d) diaphragm (i.e. upper liver dome) were measured from individual cine‐MR images across 32 dataset. The four respiratory motions were then synchronized with the cine‐MR image acquisition time. Correlation coefficients were calculated in the time variation of two nominated respiratory motions: (1) chest‐abdomen, (2) abdomen‐diaphragm and (3) diaphragm‐lung tumor. The three combinations were compared between FB and AV biofeedback.

Results:

Compared to FB, AV biofeedback improved chest‐abdomen correlation by 17% (p=0.005) from 0.75±0.23 to 0.90±0.05 and abdomen‐diaphragm correlation by 4% (p=0.058) from 0.91±0.11 to 0.95±0.05. Compared to FB, AV biofeedback improved diaphragm‐lung tumor correlation by 12% (p=0.023) from 0.65±0.21 to 0.74±0.16.

Conclusions:

Our results demonstrated that AV biofeedback significantly improved the correlation of internal and external respiratory motion, thus suggesting the need of AV biofeedback in respiratory‐gated treatments.
SU‐E‐J‐239: Influence of RF Coil Materials On Surface and Buildup Dose From a 6MV Photon Beam
Authors: Ghila A , Fallone B , Rathee S
Published: June 2015

Purpose:

In order to perform real time tumour tracking using an integrated Linac‐MR, images have to be acquired during irradiation. MRI uses RF coils in close proximity to the imaged volume. Given current RF coil designs this means that the high energy photons will be passing through the coil before reaching the patient. This study experimentally investigates the dose modifications that occur due to the presence of various RF coil materials in the treatment beam.

Methods:

Polycarbonate, copper or aluminum tape, and Teflon were used to emulate the base, conductor and cover respectively of a surface RF coil. These materials were placed at various distances from the surface of polystyrene or solid water phantoms which were irradiated in the presence of no magnetic field, a transverse 0.2T magnetic field, and a parallel 0.2T magnetic field. Percent depth doses were measured using ion chambers.

Results:

A significant increase in surface and buildup dose is observed. The surface dose is seen to decrease with an increasing separation between the emulated coil and the phantom surface, when no magnetic field is present. When a transverse magnetic field is applied the surface dose decreases faster with increasing separation, as some of the electrons created in the coil are curved away from the phantom's surface. When a parallel field is present the surface dose stays approximately constant for small separations, only slightly decreasing for separations greater than 5cm, since the magnetic field focuses the electrons produced in the coil materials not allowing them to scatter.

Conclusion:

Irradiating a patient through an RF coil leads to an increase in the surface and buildup doses. Mitigating this increase is important for the successful clinical use of either a transverse or a parallel configuration Linac‐MR unit.

This project is partially supported by an operating grant from the Canadian Institute of Health Research (CIHR MOP 93752)
SU‐E‐J‐241: Wavelet‐Based Temporal Feature Extraction From DCE‐MRI to Identify Sub‐Volumes of Low Blood Volume in Head‐And‐Neck Cancer
Authors: You D , Aryal M , Samuels S , Eisbruch A , Cao Y
Published: June 2015

Purpose:

A previous study showed that large sub‐volumes of tumor with low blood volume (BV) (poorly perfused) in head‐and‐neck (HN) cancers are significantly associated with local‐regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel‐wise contrast‐enhanced temporal features of dynamic contrastenhanced (DCE) MRI in HN cancers for predicting LRF.

Methods:

Our model development consists of training and testing stages. The training stage includes preprocessing of individual‐voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, feature selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast‐enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel‐wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub‐volume classification. Voxel‐wise classification accuracy was computed to evaluate performance of the model.

Results:

Average classification accuracies were 87.2% for training (10‐fold crossvalidation) and 82.5% for testing. The lowest and highest accuracies (patient‐wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub‐volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub‐volume boundaries.

Conclusion:

This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrastenhanced features of DCE‐MRI in other tumors.

We would like to acknowledge NIH for funding support: UO1 CA183848
SU‐E‐J‐252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi‐Automated Segmentation Methods
Authors: Lee M , Woo B , Kim J , Jamshidi N , Kuo M
Published: June 2015

Purpose:

Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi‐automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM).

Methods:

MRI examinations with T1 post‐gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi‐automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automatically from the segmented tumor. The inter‐observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility.

Results:

Inter‐observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE‐T1W1.

Conclusion:

Comparison of two semi‐automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.
SU‐E‐J‐256: Predicting Metastasis‐Free Survival of Rectal Cancer Patients Treated with Neoadjuvant Chemo‐Radiotherapy by Data‐Mining of CT Texture Features of Primary Lesions
Authors: Zhong H , Wang J , Shen L , Hu W , Wan J , Zhou Z , Zhang Z
Published: June 2015

Purpose:

The purpose of this study is to investigate the relationship between computed tomographic (CT) texture features of primary lesions and metastasis‐free survival for rectal cancer patients; and to develop a datamining prediction model using texture features.

Methods:

A total of 220 rectal cancer patients treated with neoadjuvant chemo‐radiotherapy (CRT) were enrolled in this study. All patients underwent CT scans before CRT. The primary lesions on the CT images were delineated by two experienced oncologists. The CT images were filtered by Laplacian of Gaussian (LoG) filters with different filter values (1.0–2.5: from fine to coarse). Both filtered and unfiltered images were analyzed using Gray‐level Co‐occurrence Matrix (GLCM) texture analysis with different directions (transversal, sagittal, and coronal). Totally, 270 texture features with different species, directions and filter values were extracted. Texture features were examined with Student's t‐test for selecting predictive features. Principal Component Analysis (PCA) was performed upon the selected features to reduce the feature collinearity. Artificial neural network (ANN) and logistic regression were applied to establish metastasis prediction models.

Results:

Forty‐six of 220 patients developed metastasis with a follow‐up time of more than 2 years. Sixtyseven texture features were significantly different in t‐test (p<0.05) between patients with and without metastasis, and 12 of them were extremely significant (p<0.001). The Area‐under‐the‐curve (AUC) of ANN was 0.72, and the concordance index (CI) of logistic regression was 0.71. The predictability of ANN was slightly better than logistic regression.

Conclusion:

CT texture features of primary lesions are related to metastasisfree survival of rectal cancer patients. Both ANN and logistic regression based models can be developed for prediction.
SU‐E‐J‐257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets
Authors: Chetvertkov M , Siddiqui F , Chetty I , Kim J , Kumarasiri A , Liu C , Gordon J
Published: June 2015

Purpose:

Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART).

Methods:

Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction‐to‐fraction changes were superimposed on the DVFs. A patient‐specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment.

Results:

For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient's body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction‐to‐fraction changes in patient's anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change.

Conclusion:

The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction‐to‐fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population‐based PCA models that can be used to prospectively identify significant anatomical changes early in treatment.

This work is supported in part by a grant from Varian Medical Systems, Palo Alto, CA.
SU‐E‐J‐258: Prediction of Cervical Cancer Treatment Response Using Radiomics Features Based On F18‐FDG Uptake in PET Images
Authors: Altazi B , Fernandez D , Zhang G , Biagioli M , Moros E , Moffitt H. Lee
Published: June 2015

Purpose:

Radiomics have shown potential for predicting treatment outcomes in several body sites. This study investigated the correlation between PET Radiomics features and treatment response of cervical cancer outcomes.

Methods:

our dataset consisted of a cohort of 79 patients diagnosed with cervical cancer, FIGO stage IB‐IVA, age range 25–86 years, (median age at diagnosis: 50 years) all treated between: 2009–14 with external beam radiation therapy to a dose range between: 45–50.4 Gy (median= 45 Gy), concurrent cisplatin chemotherapy and MRI‐based brachytherapy to a dose of 20–30 Gy (median= 28 Gy). Metabolic Tumor Volume (MTV) in patient's primary site was delineated on pretreatment PET/CT by two board certified Radiation Oncologists. The features extracted from each patient's volume were: 26 Co‐occurrence matrix (COM) Feature, 11 Run‐Length Matrix (RLM), 11 Gray Level Size Zone Matrix (GLSZM) and 33 Intensity‐based features (IBF). The treatment outcome was divided based on the last follow up status into three classes: No Evidence of Disease (NED), Alive with Disease (AWD) and Dead of Disease (DOD). The ability for the radiomics features to differentiate between the 3 treatments outcome categories were assessed by One‐Way ANOVA test with p‐value < 0.05 was to be statistically significant. The results from the analysis were compared with the ones obtained previously for standard Uptake Value (SUV).

Results:

Based on patients last clinical follow‐up; 52 showed NED, 17 AWD and 10 DOD. Radiomics Features were able to classify the patients based on their treatment response. A parallel analysis was done for SUV measurements for comparison.

Conclusion:

Radiomics features were able to differentiate between the three different classes of treatment outcomes. However, most of the features were only able to differentiate between NED and DOD class. Also, The ability or radiomics features to differentiate types of response were more significant than SUV.
SU‐E‐J‐273: Skin Temperature Recovery Rate as a Potential Predictor for Radiation‐Induced Skin Reactions
Authors: Biswal N C , Wu Z , Sun J , Chu J
Published: June 2015

Purpose:

To assess the potential of dynamic infrared imaging to evaluate early skin reactions during radiation therapy in cancer patients.

Methods:

Thermal images were captured by our home‐built system consisting of two flash lamps and an infrared (IR) camera. The surface temperature of the skin was first raised by ∼ 6 °C from ∼1 ms short flashes; the camera then captured a series of IR images for 10 seconds. For each image series, a basal temperature was recorded for 0.5 seconds before flash was triggered. The temperature gradients (ε) were calculated between a reference point (immediately after the flash) and at a time point of 2sec, 4sec and 9sec after that. A 1.0 cm region of interest (ROI) on the skin was drawn; the mean and standard deviations of the ROIs were calculated. The standard ε values for normal human skins were evaluated by imaging 3 healthy subjects with different skin colors. All of them were imaged on 3 separate days for consistency checks.

Results:

The temperature gradient, which is the temperature recovery rate, depends on the thermal properties of underlying tissue, i.e. thermal conductivity. The average ε for three volunteers averaged over 3 measurements were 0.64±0.1, 0.72±0.2 and 0.80±0.3 at 2sec, 4sec and 9sec respectively. The standard deviations were within 1.5%–3.2%. One of the volunteers had a prior small skin burn on the left wrist and the ε values for the burned site were around 9% (at 4sec) and 13% (at 9sec) lower than that from the nearby normal skin.

Conclusion:

The temperature gradients from the healthy subjects were reproducible within 1.5%–3.2 % and that from a burned skin showed a significant difference (9%–13%) from the normal skin. We have an IRB approved protocol to image head and neck patients scheduled for radiation therapy.
SU‐E‐J‐275: Review ‐ Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy
Authors: Lu W , Wang J , Zhang H
Published: June 2015

Purpose:

To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy.

Methods:

We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation.

Results:

Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi‐automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi‐automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi‐modality segmentation methods have been shown to improve accuracy compared to single‐modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response.

Conclusion:

This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response.

This work was supported in part by the National Cancer Institute Grant R01CA172638.
SU‐E‐T‐08: A Beam Source Model for Monte Carlo Simulations of a Double‐Scattering Proton Beam Delivery System Using Beam Current Modulation
Authors: Shin J , Merchant T E , Lee S , Li Z , Shin D , Farr J B
Published: June 2015

Purpose:

To reconstruct phase‐space information upstream of patient specific collimators for Monte Carlo simulations using only radiotherapy planning system data.

Methods:

The proton energies are calculated based on residual ranges, e.g., sum of prescribed ranges in a patient and SSD. The Kapchinskij and Vladimirskij (KV) distribution was applied to sample proton's x‐y positions and momentum direction and the beam shape was assumed to be a circle. Free parameters, e.g., the initial energy spread and the emittance of KV distribution were estimated from the benchmarking with commissioning data in a commercial treatment planning system for an operational proton therapy center. The number of histories, which defines the height of individual pristine Bragg peaks (BP) of Spread‐out Bragg peak (SOBP), are weighted based on beam current modulation and a correction factor is applied to take into account the fluence reduction as the residual range decreases due to the rotation of the range modulator wheel. The timedependent behaviors, e.g., the changes of the residual range and histories per a pristine BP, are realized by utilizing TOPAS (Tool for Particle Simulation).

Results:

Benchmarking simulations for selected SOBPs ranging 7.5 cm to 15.5 cm matched within 2 mm in range and up to 5 mm in SOBP width against measurement data in water phantom. We found this model tends to underestimate entrance dose by about 5 % in comparison to measurement. This was attributed to the situation that the energy distribution used in the model was limited in its granularity at the limit of single energy spectrum for the narrow angle modulator steps used in the proximal pull back region of the SOBPs.

Conclusion:

Within these limitations the source modeling method proved itself an acceptable alternative of a full treatment head simulation when the machine geometry and materials information are not available.
SU‐E‐T‐10: A Clinical Implementation and the Dosimetric Evidence in High Dose Rate Vaginal Multichannel Applicator Brachytherapy
Authors: Syh J , Syh J , Patel B , Zhang J , Wu H , Rosen L
Published: June 2015

Purpose:

The multichannel cylindrical applicator has a distinctive modification of the traditional single channel cylindrical applicator. The novel multichannel applicator has additional peripheral channels that provide more flexibility both in treatment planning process and outcomes. To protect by reducing doses to adjacent organ at risk (OAR) while maintaining target coverage with inverse plan optimization are the goals for such novel Brachytherapy device. Through a series of comparison and analysis of reults in more than forty patients who received HDR Brachytherapy using multichannel vaginal applicator, this procedure has been implemented in our institution.

Methods:

Multichannel planning was CT image based. The CTV of 5mm vaginal cuff rind with prescribed length was well reconstructed as well as bladder and rectum. At least D95 of CTV coverage is 95% of prescribed dose. Multichannel inverse plan optimization algorithm not only shapes target dose cloud but set dose avoids to OAR's exclusively. The doses of D2cc, D5cc and D5; volume of V2Gy in OAR's were selected to compare with single channel results when sole central channel is only possibility.

Results:

Study demonstrates plan superiorly in OAR's doe reduction in multi‐channel plan. The D2cc of the rectum and bladder were showing a little lower for multichannel vs. single channel. The V2Gy of the rectum was 93.72% vs. 83.79% (p=0.007) for single channel vs. multichannel respectively. Absolute reduced mean dose of D5 by multichannel was 17 cGy (s.d.=6.4) and 44 cGy (s.d.=15.2) in bladder and rectum respectively.

Conclusion:

The optimization solution in multichannel was to maintain D95 CTV coverage while reducing the dose to OAR's. Dosimetric advantage in sparing critical organs by using a multichannel applicator in HDR Brachytherapy treatment of the vaginal cuff is so promising and has been implemented clinically.
SU‐E‐T‐17: A Comparison of Forward and Field in Field Intensity Modulation Radiotherapy Planning for Breast Cancer
Authors: Liu T , Sun T , Chen J , Zhang G
Published: June 2015

Purpose:

To compare the dosimetric difference in forward intensity modulation radiotherapy(fIMRT) and field in field IMRT (FIF IMRT)planning for breast cancer.

Methods:

Ten patients received radiotherapy are selected.For each patient,two treatment plans(fIMRT and FIF IMRT) were designed with Varian Eclipse ver11.0 treatment planning system.Evaluate the dose parameters of targets, organs at risk (OAR), monitor units and treatment time, using dose‐volume histogram (DVH).

Results:

There were no significant difference were found in conformal index (CI), homogeneity index (HI) of PTV,V5,V10,V20,V30,V40,V50 of heart, lung and monitor unit(MU)(P>0.05).The differences were significant in the treatment time(fIMRT=8.3min,FIF IMRT=2.5,p<0.05).

Conclusion:

FIF IMRT is equal to fIMRT in dosimetril evaluation. Due to much less delivery time,FIF IMRT is an efficient technique in treating patients by reduceing the uncomfortable influnce which could effect the treatment.
SU‐E‐T‐20: A Correlation Study of 2D and 3D Gamma Passing Rates for Prostate IMRT Plans
Authors: Zhang D , Wang B , Deng X , Ma C
Published: June 2015

Purpose:

To investigate the correlation between the two‐dimensional gamma passing rate (2D %GP) and three‐dimensional gamma passing rate (3D %GP) in prostate IMRT quality assurance.

Methods:

Eleven prostate IMRT plans were randomly selected from the clinical database and were used to obtain dose distributions in the phantom and patient. Three types of delivery errors (MLC bank sag errors, central MLC errors and monitor unit errors) were intentionally introduced to modify the clinical plans through an in‐house Matlab program. This resulted in 187 modified plans. The 2D %GP and 3D %GP were analyzed using different dose‐difference and distance‐toagreement (1%‐1mm, 2%‐2mm and 3%‐3mm) and 20% dose threshold. The 2D %GP and 3D %GP were then compared not only for the whole region, but also for the PTVs and critical structures using the statistical Pearson's correlation coefficient (γ).

Results:

For different delivery errors, the average comparison of 2D %GP and 3D %GP showed different conclusions. The statistical correlation coefficients between 2D %GP and 3D %GP for the whole dose distribution showed that except for 3%/3mm criterion, 2D %GP and 3D %GP of 1%/1mm criterion and 2%/2mm criterion had strong correlations (Pearson's γ value >0.8). Compared with the whole region, the correlations of 2D %GP and 3D %GP for PTV were better (the γ value for 1%/1mm, 2%/2mm and 3%/3mm criterion was 0.959, 0.931 and 0.855, respectively). However for the rectum, there was no correlation between 2D %GP and 3D %GP.

Conclusion:

For prostate IMRT, the correlation between 2D %GP and 3D %GP for the PTV is better than that for normal structures. The lower dose‐difference and DTA criterion shows less difference between 2D %GP and 3D %GP. Other factors such as the dosimeter characteristics and TPS algorithm bias may also influence the correlation between 2D %GP and 3D %GP.
SU‐E‐T‐22: A Deterministic Solver of the Boltzmann‐Fokker‐Planck Equation for Dose Calculation
Authors: Hong X , Paganetti H , Gao H
Published: June 2015

Purpose:

The Boltzmann‐Fokker‐Planck equation (BFPE) accurately models the migration of photons/charged particles in tissues. While the Monte Carlo (MC) method is popular for solving BFPE in a statistical manner, we aim to develop a deterministic BFPE solver based on various state‐of‐art numerical acceleration techniques for rapid and accurate dose calculation.

Methods:

Our BFPE solver is based on the structured grid that is maximally parallelizable, with the discretization in energy, angle and space, and its cross section coefficients are derived or directly imported from the Geant4 database. The physical processes that are taken into account are Compton scattering, photoelectric effect, pair production for photons, and elastic scattering, ionization and bremsstrahlung for charged particles.While the spatial discretization is based on the diamond scheme, the angular discretization synergizes finite element method (FEM) and spherical harmonics (SH). Thus, SH is used to globally expand the scattering kernel and FFM is used to locally discretize the angular sphere. As a Result, this hybrid method (FEM‐SH) is both accurate in dealing with forward‐peaking scattering via FEM, and efficient for multi‐energy‐group computation via SH. In addition, FEM‐SH enables the analytical integration in energy variable of delta scattering kernel for elastic scattering with reduced truncation error from the numerical integration based on the classic SH‐based multi‐energy‐group method.

Results:

The accuracy of the proposed BFPE solver was benchmarked against Geant4 for photon dose calculation. In particular, FEM‐SH had improved accuracy compared to FEM, while both were within 2% of the results obtained with Geant4.

Conclusion:

A deterministic solver of the Boltzmann‐Fokker‐Planck equation is developed for dose calculation, and benchmarked against Geant4.

Xiang Hong and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500).
SU‐E‐T‐23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support
Authors: Thwaites D , Holloway L , Bailey M , Barakat S , Carolan M , Delaney G , Field M , Dekker A , Lustberg T , Miller A , Soest J van , Vinod S , Walsh S
Published: June 2015

Purpose:

Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi‐institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs.

Methods:

Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open‐source rapid learning systems were installed, for data extraction and mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway.

Results:

For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM‐predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two‐year overall survival was 65% in curatively and 18% in palliatively treated patients.

Conclusion:

The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.
SU‐E‐T‐24: A Dose Volume Comparison in Stereotactic Body Radiation Therapy Using Flattened and Un‐Flattened Photon Beams
Authors: Pangam S
Published: June 2015

Purpose:

Aim of this study is to compare the dose volume characteristics of 6X FFF (flattening Free Filter) Arc and 6X FB (flattened Beam) arc photon plans in SBRT technique.

Methods:

Eight patients who received linear Accelearator‐based SBRT were retrospectively included in this study. A dose of 50 Gy was given to the target in five fractions. Same data set was used to generate plans for both FFF and FB. ITV was generated using maximum intensity projection and critical structures were derived using average intensity projection. PTV obtained by giving 0.5cm margin to ITV.

Results:

While both modalities can provide satisfactory target dose coverage, the dose to PTV was more heterogeneous in FFF than 6X FB plans in all cases. The doses in all plans were well below institutional constraints for both modalities. Comparing the results of Homogeneity Index(HI), Conformity Index(CI), PTV‐D80% volume, D50% volume and D20% volume (Table‐1 ) for both techniques, found all the indices are within limits of RTOG guidelines but the 6X FFF is superior in sparing normal tissues in compare with FB. In all cases studied, more treatment time was required for FB treatment delivery for a given prescription. The results indicate that for large dose delivery FFF is preferable as volumetric parameters like HI and CI are better and dose can be delivered in a short span of time.

Conclusion:

Both Flattened and Unflattened beam SBRT systems can provide adequate dose coverage for target tumor. While the unflattened beams deliver less normal tissue dose than Flattened beams in all cases. The magnitude of differences in normal tissue dose between both modalities was due to beam characterization of the beams. Flattened beam requires more Monitor Units to deliver similar target prescription to the tumor than unflattened beam SBRT systems. The results of this study may provide a general guideline for patient and treatment modality selection based on volumetric, tumor control and normal tissue sparing considerations.
SU‐E‐T‐29: A Dosimetric Study of Volumetric Modulated Arc Therapy with Simultaneous Integrated Boost for Rectal Cancer
Authors: Sun T , Lin X , Yin Y , Liu T
Published: June 2015

Purpose:

To compare the dosimetric differences among fixed field intensity‐modulated radiotherapy (IMRT) and double‐arc volumetricmodulated arc therapy (VMAT) plans with simultaneous integrated boost in rectal cancer.

Methods:

Ten patients with rectal cancer previously treated with IMRT were included in this analysis. For each patient, two treatment techniques were designed for each patient: the fixed 7 fields IMRT and double‐arc VMAT with RapidArc technique. The treatment plan was designed to deliver in one process with simultaneous integrated boost (SIB). The prescribed doses to the planning target volume of the subclinical disease (PTV1) and the gross disease (PTV2) were 45 Gy and 55 Gy in 25 fractions, respectively. The dose distribution in the target, the dose to the organs at risk, total MU and the delivery time in two techniques were compared to explore the dosimetric differences.

Results:

For the target dose and homogeneity in PTV1 and PTV2, no statistically differences were observed in the two plans. VMAT plans showed a better conformity in PTV1. VMAT plans reduced the mean dose to bladder, small bowel, femur heads and iliac wings. For iliac wings, VMAT plans resulted in a statistically significant reduction in irradiated volume of 15 Gy, 20 Gy, 30 Gy but increased the 10 Gy irradiated volume. VMAT plans reduced the small bowel irradiated volume of 20 Gy and 30 Gy. Compared with IMRT plans, VMAT plans showed a significant reduction of monitor units by nearly 30% and reduced treatment time by an average of 70%

Conclusion:

Compared to IMRT plans, VMAT plans showed the similar target dose and reduced the dose of the organs at risk, especially for small bowel and iliac wings. For rectal cancer, VMAT with simultaneous integrated boost can be carried out with high quality and efficiency.
SU‐E‐T‐30: A Factor for Converting Dose to a Gold Nanoparticle Mixture to a Biologically‐Relevant Dose
Authors: Koger B , Kirkby C
Published: June 2015

Purpose:

Monte Carlo studies of gold nanoparticle (GNP) dose enhancement on macroscopic scales in radiotherapy have modeled GNPs in tissue as a homogeneous mixture of gold and tissue. Using an explicit model of GNPs randomly positioned in a small volume (1 µm3) of tissue, this study aims to quantify the dose to the biologically relevant component of a goldtissue mixture, enabling a conversion from macroscopically‐scored dose.

Methods:

Using the PENELOPE Monte Carlo code with the penEasy package, we modeled a 1 µm3 volume containing either a tissue‐gold mixture or GNPs suspended in ICRU 4‐component tissue at various gold concentrations (0, 5, 10, and 15 mg Au/g tissue) and GNP diameters (20, 30, 40, 50 nm). The volume was irradiated with monoenergetic photon and electron beams, ranging from 110 eV to 6 MeV. Interaction forcing was utilized to increase simulation efficiency. Energy deposition was scored in the tissue for each case and was converted to dose. For each scenario, we calculated a conversion factor, the ratio of dose‐to‐tissue to dose‐to‐mixture as a function of energy.

Results:

The conversion factor was plotted as a function of energy for both photons and electrons. For electrons, the conversion factor was relatively unaffected by any of the parameters, including energy, ranging between 0.98–1.02. For photons, the factor was very energy dependent, with a range of 0.49–1.02. The factor was lowest for 10–100 keV photons. The conversion factor generally decreased with increasing GNP concentration and increasing GNP size.

Conclusion:

With a large variation in the conversion factor with incident energy, dose deposition is dependent on the spectrum incident on a volume. By scoring the energy spectrum in a given volume, one can provide a scenario‐specific conversion factor, allowing fast, detailed Monte Carlo simulations without the need for explicit GNP‐definition.