bluuebunny/crossref_metadata_2025 huggingface dataset:
TH‐A‐116‐04: A Universal Predictive DVH Modeling Toolkit
Authors: Tan J , Appenzoller L , Yang D , Mutic S , Moore K
Published: July 2013

Purpose: The goal of this work was to develop an efficient DICOM‐based tool to create predictive DVH (pDVH) models for treatment plan automation and knowledge‐based IMRT quality control (QC) independent of treatment planning system (TPS). Methods: The tool was implemented in MATLAB. RT dose and structure files exported from different TPSs including Eclipse, Pinnacle, and Tomotherapy were used as the input to the pDVH DICOM tool. pDVH models based on the correlation of expected dose to the distance from a voxel to the PTV surface in a cohort of patients were created for multiple treatment sites (intact prostate, prostate bed, head‐and‐neck, endometrial, cervical). To accelerate the computation of a distance vector field for efficient model building, multiple techniques were employed including GPUs acceleration and parallel computation using multiple CPU cores. The tool has a full GUI and can also run in batch mode with minimal user interaction. It automatically identifies outliers for treatment plan quality control and pDVH model refinement. Identified outliers can be replanned and reimported to refine the accuracy of the DVH prediction. Results: The inclusion of GPUs reduces computation time by 200 times, requiring <1 second to compute an OAR vector field and only a few seconds to build a pDVH model. The pDVH DICOM tool has been used by multiple institutions and pDVH models for intact prostate and cervical cancer were created in less than one hour without GPU acceleration. Conclusion: We developed a universal DICOM‐based tool that provides clinicians a novel and highly efficient plan quality control mechanism by predicting OAR DVHs based on local institutional best practices. This technology can be used to asses inter‐institutional and inter‐TPS plan quality based on aggregate clinical data instead of contrived benchmark planning sets. Supported by a research agreement with Varian Medical Systems.
TH‐C‐137‐01: A Graphical Software Tool for TrueBeam Developer Mode
Authors: Yan Y , Mao W , Ouyang L , Solberg T
Published: July 2013

Purpose: To introduce a novel graphical software tool to assist scientists and engineers in building new applications in TrueBeam Developer's Mode. Methods: As an option, TrueBeam users are provided with access to the full set of capabilities within the XML‐based Developer's Mode. It is very laborious to compile a XML file, however, particularly when IMRT beams are included along with KV and MV imaging triggers. In this study, a choreographing software tool, TrueBeam TeachMod, was developed based on DICOMan‐TX, a DICOM utility tool and a research platform for radiation therapy and radiology, to facilitate exploring potential technologies that can ultimately be applied to clinics. A DICOM RT plan from a treatment planning system is translated into a XML file which is further complemented with imaging triggers and axis changes. The tool allows the user to interactively edit any axis at any beam control point. It also includes predefined treatment and KV/MV imaging procedures that can be frequently used to build scientific applications. Results: IMRT plans from either static or dynamic were transferred via DICOM connection to DICOManTX where True Beam Teach Mod produced the coordinate beam components. Trajectories of all axes were graphically rendered and edited as needed. The process requires less than five minutes to generate sophisticated XML instructions containing tens of thousands lines. A series of test application was successfully implemented on the True Beam unit. Conclusion: A XML composing tool is highly desirable for users who want to extensively explore the potential of the True Beam platform. It not only provides the users with a tool that could expedite their research and development but also opportunities for them to conduct unconventional techniques that are prohibitively time consuming without it.
TH‐C‐137‐02: Robotic Radiotherapy Using Intermediate Beam Energies
Authors: Dong P , Nguyen D , Long T , Ruan D , Romeijn E , Low D , Sheng K
Published: July 2013

Purpose: Intermediate energy (1–2MV) x‐rays have steeper depth dose drop‐off and sharper penumbra than commonly used 6MV x‐rays. Dosimetry benefits of these characteristics are studied on a robotic non‐coplanar planning and delivery platform. Methods: Dose of 1MV and 6MV x‐rays was calculated using the convolution/superposition algorithm with heterogeneity correction and Monte Carlo calculated dose kernels. The X‐ray spectrum was adjusted to match depth dose curves of published data. Thirty noncoplanar beams were selected by a pricing approach from a candidate beam pool, which consisted of 1162 uniformly distributed non‐coplanar beams minus beams leading to collision. The collision model was fit to individual treatment sites. Fluence optimization based on 5 mm MLC was performed after adding each beam. Identical objective functions for PTV and organs‐at‐risk (OARs) were employed in the 3 planning scenarios: 1 MV alone, 6 MV alone and the combination of 1 MV and 6 MV beams (1&6 MV) with the prescription dose covering 95% of the PTV. Four representative cases from the following anatomical sites were included in the study: head and neck, partial breast, lung and liver. Results: 1 MV and 1&6 MV plans provided superior OAR sparing for head, liver, partial breast and lung cases while maintaining the same PTV coverage. Compared with 6 MV plan, 1 MV plans reduced the integral dose by 25%, 23%, 19% and 9% for lung, breast, head and liver cases respectively. The plan quality of 1&6 MV plans, which primarily was slightly superior to that of the 1MV only plans. Conclusion: The dosimetric drawbacks of intermediate energy x‐rays are higher skin doses and shallower penetration when few of them are used on a coplanar platform but these drawbacks were effectively overcome on a highly non‐coplanar treatment planning platform, where its advantages of normal tissue sparing and sharp penumbra are manifested.
TH‐C‐137‐04: Separating the Dosimetric Consequences of Changing Tumor Anatomy Vs. Positional Uncertainty for Conventionally Fractionated Lung Cancer Patients
Authors: Grams M , de los Santos L Fong , Brown L , Olivier K , Brinkmann D
Published: July 2013

Purpose: To differentiate between the dosimetric consequences of positional uncertainty and changing tumor anatomy for conventionally fractionated lung cancer treatment plans. Methods: Modified IMRT photon treatment plans were retrospectively generated for 12 lung cancer patients (prescription range = 45–66 Gy). 10 of 12 patients had centrally located tumors, and the average internal target volume (ITV) was 119.5 cc (standard deviation = 133.3 cc). To simulate a large and immediate tumor response, all voxels corresponding to tumor within the ITV were reassigned an electron density equivalent to lung. The original, unchanged treatment plan was then calculated on this modified image set. To simulate positional uncertainty, isocenters in each plan were shifted by 2 mm and 5 mm in each of 6 directions: superior/inferior, left/right, anterior/posterior. Shifted plans were calculated for both the original and modified ITV. Paired t‐tests were used to compare the original and modified plans, both with and without simulated positional uncertainty. For the purposes of this abstract, changes to the volume of the PTV receiving 95% of prescription dose (PTV V95%), mean lung dose, the percent volume of lung receiving 20 Gy (Lung V20%), the volume in ccs of esophagus receiving 55 Gy (V55Gy(cc)), and the volume in ccs of spinal cord receiving 45 Gy (V45Gy(cc)) are reported. Results: Reduced tumor volumes alone resulted in minimal dosimetric impact. Shifts of 2 and 5 mm reduced PTV coverage by 0.54% and 3.3% respectively when compared to the original (unshifted) plan. When comparing modified and original plans which are both shifted in the same direction, small increases in dose were seen. Conclusion: The dosimetric consequences of positional uncertainty are larger than volumetric tumor reduction. Adaptive replanning techniques based solely on the perceived dosimetric consequences of shrinking tumor anatomy are unwarranted for conventionally fractionated lung cancer patients.
TH‐C‐137‐07: Population‐Based IMRT Beam Aperture Shape Inference Using Retrospective Treatment Plan and Patient Data
Authors: Stanojevic D , D' Souza W , Meyer R , Zhang H
Published: July 2013

Purpose: Existing IMRT aperture selection process is not only highly computationally expensive, but also lacks mechanism that would utilize historical data on high‐quality apertures. The purpose of our aperture classification model, apClass, is to alleviate both aforementioned issues by employing advanced machine‐learning algorithms to “learn” the high‐quality aperture patterns and, subsequently, “correlate” the identified aperture patterns to anatomic features specific to each and every patient. Methods: We have evaluated our ap Class model on ten advanced head‐and‐neck cancer patients. For each of the patients, key anatomic features and aperture shapes were retrieved from the existing physician‐approved clinical treatment plans. A greedy heuristic procedure was then used to generate a library of small area aperture envelopes. Those generated aperture envelopes represent the key tenet of our model: we recognize that patients with similar anatomic features are likely to benefit from “somewhat” similar aperture shapes. Our notion of “aperture envelope”, therefore, refers to a continuous range of mlc leaf movements which those similar aperture shapes might share. Once the library of aperture envelopes was constructed, we have implemented advanced multi‐target classification that seeks to identify similarities between anatomy of each individual patient (organ/tumor volumes and organ/tumor distances) and types of high‐quality aperture envelopes that can be used for IMRT planning. Results: Our case study of ten head‐and‐neck cancer patients indicated strong similarities between aperture patterns used in the clinical treatment plans. We were able to generate 189 aperture envelopes from over 2600 apertures used in the original clinical treatment plans and our multi‐target classification tests indicated near 80% mean 5‐fold validation accuracies for the top 25% generated aperture envelopes. Conclusion: We have presented an innovative aperture classification model with promising potential for efficient utilization of historical clinical treatment plans for new patient IMRT planning.
TH‐C‐137‐12: Comparison of Dose‐Volume and Dose‐Mass Inverse Optimization in NSCLC
Authors: Mihaylov I , Moros E
Published: July 2013

Purpose: To compare dose‐volume (Dvh) and dose‐mass (Dmh) optimized inverse treatment plans for lung cancers. Methods: A retrospective investigation on fourteen NSCLC cases was performed. For each case two IMRT plans were generated. The plans used the same beam angles, maximum number of segments per plan, minimum segment area and MUs per segment. The only difference was in the Dvh and Dmh quadratic objective cost functions used for inverse optimization. Both plans were normalized such that 95% of the PTV was covered by the same prescription dose. After prescription was achieved doses to organs at risk (OARs) were iteratively lowered until the standard deviation of the dose across the PTV was ∼ 3.5%. Plan quality was evaluated by several dose and mass indices (DIs and MIs). A DI/MI is represented by the dose to be delivered to certain volume/mass of an anatomical structure. Tallied DIs/MIs were for cord1%, heart33%, lungs20%, lungs30%, and esophagus50%. Statistical significance in the differences among those indices was quantified with two‐tailed paired t‐tests. Results: On average Dmh optimized plans performed better than Dvh plans. Statistically significant dose reduction in heart DI33, lung DI20, lung DI30, esophagus DI50, and cord D1 were 7%, 5%, 6%, 26%, and 27% respectively. The statistically significant differences for heart MI33, lung MI20, lung MI30, esophagus MI50, and cord MI1 were 8%, 6.3%, 6%, 25%, and 27% respectively. Conclusion: The findings herein indicate that on average Dmh based optimization results in better OAR sparing than Dvh optimization. The results however are case dependent, where Dmh does not always outperform Dvh. Those preliminary studies on a cohort of lung patients suggest that the existence and the degree of OAR sparing with Dmh optimization is location and tumor size dependent on patient‐by‐patient basis. Supported by NIH grant R01 CA163370
TH‐C‐108‐01: Radiobiological Models in Brachytherapy Treatment Planning and Evaluation
Authors: Chen Z , Carlson DJ
Published: July 2013

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit drastically different spatial and temporal dose delivery patterns from one another and from external beam radiotherapy. Typical photon energies vary from 0.02 MeV to 1 MeV and decay half‐lives vary from days to years. Treatments can consist of single‐fraction continuous low‐dose‐rate irradiations over a period of days or months to multi‐fraction high‐dose‐rate irradiations of different dose fractionations. The clinical impact of such diverse spatial‐temporal variations cannot be adequately assessed using traditional physical dose‐based indices alone because these indices do not take into account the complex interplay between the dynamics of dose delivery and the biological response of the irradiated cells. Radiobiological models that capture the interacting effects of dose delivery with cellular biology can potentially provide a useful clinical tool for comparing and optimizing the relative clinical effectiveness of different brachytherapy treatment strategies. These models have been used increasingly by clinical medical physicists over the last decade in research settings. Currently, there are several different models presented in the literature with a range of applications and limitations. Information on model parameters, uncertainties, and their impact on model predictions are also scattered in the literature. The primary goal of this educational course is to provide a comprehensive review of existing radiobiological models and parameters, and their use in the evaluation of selected brachytherapy modalities. Particular emphasis will be placed on discussion of model limitations that may hinder the proper interpretation of model predictions and practical implementation.

Learning Objectives:

1. To understand the rationale and basis of existing radiobiological models used in the evaluation of brachytherapy treatments.

2. To recognize the underlying assumptions and limitations of existing models and their proper application in the relative comparison of competing brachytherapy modalities.

3. To be aware of the pitfalls regarding the selection, use, and interpretation of radiobiological models in treatment planning and evaluation

ZC would like to acknowledge partial support of NIH grant R01 CA134627
TH‐C‐WAB‐01: BEST IN PHYSICS (JOINT IMAGING‐THERAPY)‐Semi‐Automated Probabilistic Segmentation of Head and Neck Anatomy Through Structure Specific Feature Selection From Multi‐Sequence MRI
Authors: Veeraraghavan H , Folkert M , Deasy J , Traughber M
Published: July 2013

Purpose: To develop semi‐automatic methods for robust segmentation of head and neck anatomy through structure‐specific image features selection from multiple MRI sequences. Methods: We developed a semi‐automatic approach for probabilistic segmentation of head and neck anatomy that combines several candidate segmentations using random forests and structure‐specific image features selection from multiple MRI sequences. Starting from user‐drawn line segments on a single axial image, in each tree, structure segmentations are generated from composite feature images computed at the tree split nodes. Composite features are computed through random combinations of the MRI sequences and image features, including texture and Laplacian of Gaussian gradients. We used seven different MRI pulse sequences obtained from a Phillips scanner, including Dixon water only T1, Dixon fat only T1, Dixon in‐phase, Dixon opposed‐phase, T1‐weighted, T2‐weighted and diffusion weighted whole body imaging with background body signal suppression (DWIBS). The highest quality anatomical structure segmentation in a tree is extracted from all available split node segmentations with segmentation quality measured using within‐segment statistics including kurtosis, edge quality, and inter‐segment dissimilarity. The joint segmentation with probabilistic score is obtained by combining all the generated tree segmentations. Results: We used our approach to segment anatomical structures in head and neck images using the seven MRI sequences listed above. The algorithm returned the segmentations, their probabilistic scores, and the highest quality feature images for each structure. We evaluated our approach by computing DICE overlap scores with manually segmented ground truths and obtained an overall segmentation accuracy of 80% for the various radiotherapeutically‐relevant anatomic structures. Conclusion: We developed a semi‐automatic, random forest‐based probabilistic segmentation of head and neck anatomy by combining multiple MRI pulse sequences. Our approach achieves accurate segmentation for various radiotherapeutically‐relevant anatomic structures. Additionally, it extracts the highest quality composite feature images for obtaining optimal segmentation for the same structures. Phillips
TH‐C‐WAB‐04: Atlas Ranking and Selection for Multi‐Atlas Segmentation
Authors: Yang J , Beadle B , Garden A , Balter P , Court L
Published: July 2013

Purpose: To improve multi‐atlas segmentation by ranking and selecting a subset of optimal atlas candidates resembling the image to be segmented in terms of local anatomy similarity. Methods: Thirteen planning CT images of head and neck cancer patients were acquired (resolution: 1.0×1.0×2.5mm3) and the esophagus were contoured manually by a head‐and‐neck oncologist. We randomly chose one of the 13 set of images as the test; the remaining 12 became the atlases. Deformable registration was first applied to transform each of the atlas esophagus contours to the test image. We used Kullback‐Leibler divergence to measure the similarity of local intensity histograms between the test image and each atlas, and the measurements were used to rank the atlases. Deformed contours from the most to least similar were added sequentially and the overlap ratio was examined. We identified a subset of optimal atlas candidates by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused to produce the final segmentation. Results: The rank of the atlases determined by the Kullback‐Leibler divergence was consistent with the human visual observation. The overlap ratio analysis identified a subset of 5 optimal atlas candidates automatically. By comparing the segmentation generated from the 5 optimal atlases with the manual contour, it achieved a Dice similarity coefficient of 67.2% and a mean surface distance of 2.3mm, whereas the segmentation generated from all 12 atlases had these values of 31.4% and 4.0mm. Contour fusion relies on contour agreement. If more than half of the atlases produce incorrect deformed contours, it is necessary to identify the optimal atlases to improve the contour fusion process in the multi‐atlas segmentation. Conclusion: Ranking and selecting a subset of optimal atlas candidates using local anatomy similarity improves the multi‐atlas segmentation.
TH‐C‐WAB‐08: Modeling of the Dice Coefficient for PET Segmentation Studies
Authors: McGurk R , Smith V , Bowsher J , Lee J , Das S
Published: July 2013

Purpose: The Dice coefficient (DC) is a common accuracy metric in PET segmentation studies that is a measure of overlap between the segmented volume and ground truth. This work presents a methodology to model DC as a function of object shapes, sizes, contrasts, noise levels and filters. Methods: Five spherical volumes (1.1–26.5ml) and two irregular volumes (16&32cc) were imaged for 1, 2 and 5 minutes at high (∼8:1) and low (∼4:1) contrast. A Gaussian filter (5mm FWHM (G5)), and a bilateral filter (7mm spatial FWHM with adaptive intensity range kernel (B7)) were applied with and without a 3 mm FWHM Gaussian pre‐smoothing step (filters G3G5 and G3B7). Adaptive and thresholding at 40%‐max was used for segmentation and DC values produced by comparing the segmentation with a ground truth defined from a high‐resolution CT. Generalized estimating equations (GEE) were used to fit models describing DC as functions of object size, contrast, scan duration, filter and segmentation method and compared with parameters estimated from ordinary least‐squares fitting. Results: DC was most affected by sphere size (13mm vs.37mm,ΔDC=−0.22,p<0.0001), followed by contrast (4:1 vs.8:1,(ΔDC=−0.07,p<0.0001), then scan duration (1 vs.5min,ΔDC=−0.06,p<0.0001). DC values were most improved with a combination of G3G5 filter and ADP segmentation (G3G5&ADP vs G5&40%,ΔDC=+0.04,p<0.0001) for spheres. Scan duration had the biggest effect for irregular shapes (1 vs.5 min ΔDC=−0.07,p<0.0001). No combination of filter and segmentation method produced significantly higher DC values than those for the G5 filter and 40% thresholding. The significance of explanatory variables changed in five cases for spheres, and three for irregular shapes between the GEE and OLS model fits. Conclusion: DC was most affected by object size for spheres and scan duration for irregular volumes. The GEE framework accounts for the bounded, correlated and heteroscedastic nature of DC values, and are recommended for the analysis of DC. Ross McGurk is supported by a New Zealand Bright Futures Top Achiever Doctoral Scholarship, Grant Number DKUX09001.
TH‐C‐WAB‐11: MRI‐Based Treatment Planning with Pseudo CT Generated Through Atlas Registration
Authors: Uh J , Merchant T , Hua C
Published: July 2013

Purpose: To develop a magnetic resonance imaging (MRI)‐based treatment planning method without the need of acquiring patient‐specific computed tomography (CT) dataset. Methods: A pseudo CT, providing electron density information for dose calculation, was generated by deforming atlas CT images of other patients. Two approaches, arithmetic averaging and Gaussian process regression (Hofmann 2008), were used to combine the deformed images into a single dataset. The required deformation was derived from registering corresponding atlas MR images to those of the subject of interest. We tested this method on 7 patients aged 8–12 years with common pediatric brain tumors. We used a leave‐one‐out procedure, i.e., the 6 pairs of MR/CT images other than those of each patient of interest served as atlas images. The original patient‐specific CT was replaced with the pseudo CT and the dose distribution was recalculated to quantify the difference. We used Chi‐evaluation (Bakai 2003) for dose distribution comparison. Doses to the planning target volume (PTV) and organs at risk (OARs) were compared. Results: The pseudo CT generated by arithmetic averaging showed a high similarity to the patient‐specific CT (correlation coefficient, 0.75–0.85). The calculated dose distribution was in close agreement with original dose. Nearly the entire patient volume (98.4 – 99.8%) satisfied the criteria of Chi‐evaluation (<3mm and 3% maximum dose). Dose indices of V95 and D95 in PTV differed by up to 2.2%. The average differences in maximum and mean doses to OARs ranged from 0.3% to 1.0%. The pseudo CT by Gaussian process showed similar performances for the images included in this study (P=0.27, Chi‐evaluation) despite longer computation time. Conclusion: MR‐based treatment planning with pseudo CT generated through atlas registration is feasible for pediatric brain tumor patients. The doses calculated from pseudo CT agreed well with those from patient‐specific CT.
TH‐C‐141‐02: Imaging of Targeted Cancer Therapy Using Multifunctional Liposomal Nanoprobes and MRI
Authors: Kuo Y , Hung C , Raghavan S , D' Souza W
Published: July 2013

Purpose: To develop a multifunctional liposomal nanoprobe that selectively binds to epidermal growth factor receptor (EGFR) over‐expressing tumor cells with an encapsulating a magnetic resonance (MR) imaging contrast agent, gadolinium (Gd) and conjugating an EGFR‐specific monoclonal antibody within liposome particles. The function of the probe is to target cancer overexpressing cells so that uptake of anti‐EGFR targeted therapeutic agents may be imaged. We tested our nanoprobe using a head and neck cancer cell line and Cetuximab, an anti‐EGFR drug. Methods: Encapsulation of Gd within liposome particles was optimized by combining Dipalmitoylphosphatidylcholine (DPPC), cholesterol, 1,2‐dioleoyl‐sn‐glycero‐3‐phospho‐ethanolamine‐N‐(biotinyl)(sodium salt) (DOPE‐Biotin), and Diethylenetriaminepentaacetic acid‐bis(stearylamide)‐Gd (DTPA‐BSA‐Gd). Avidin was included in the liposomal membrane to conjugate with biotin‐modulated anti‐EGFR antibody. The probes were characterized for size, surface potential and structural integrity with dynamic light scattering (DLS) and transmission electronic microscopy (TEM). In‐vitro targeting specificity of the probe was evaluated using fluorescence microscopy by comparing the binding capacity of a fluorescence‐labeled probe in a human head and neck cancer cell line, 15B (high EGFR overexpression) and a human embryonic kidney cell line, HEK293 (low EGFR control). Finally, the in vitro applicability of MRI contrast Gd within the probe was demonstrated by detecting cell‐bound particles with MR imaging. Results: Using TEM scanning, the probes were near spherical in shape and identical in particle size (∼100nm). The surface potential was +5.13mV. The EGFR‐specific targeting assay with fluorescence‐labeled probes showed significantly high occupancy of the probe on the surface of 15B cells in comparison with HEK293 cells. The T1 relaxation time of this EGFR‐specific probe attached to15B cells was measured to be 379.3 ms, shorter than that of control vector without anti‐EGFR conjugation (516.2 ms). Conclusion: We have demonstrated targeting specificity of a nanoprobe in discriminating high and low EGFR‐expressing cells. The incorporation of Gd revealed the cell‐targeting under MR imaging.
TH‐C‐141‐05: A Simulation Study to Investigate the Potential of Using Magnetic Resonance Elastography (MRE) to Differentiate Recurrent Tumor and Radiation Necrosis
Authors: Feng Y , Bayly P , Huang J , Robinson C , Du D , Mutic S , Shimony J , Leuthardt E , Okamoto R , Hu Y
Published: July 2013

Purpose: Investigate the potential of magnetic resonance elastography (MRE) to differentiate and delineate tissues with different viscoelastic properties, with the purpose of applying MRE to differentiate and delineate recurrent brain tumor and radiation necrosis. Methods: To simulate tumor or radiation necrosis inside normal brain tissue, a 10 mm radius cylindrical inclusion was modeled inside a 150×150 mm2 rectangular prismatic domain. Typical storage modulus (2.5 kPa) and loss modulus (0.5 kPa) of brain tissue were assigned to the prismatic region. Varying tissue properties were assigned to the cylindrical region (storage modulus 0.5–5 kPa, with loss factor 0.2), mimicking the shear modulus difference between recurrent tumor and radiation necrosis. Wave propagation (30–100 Hz) was simulated in this domain, using the frequency‐domain analysis in the structural mechanics module in COMSOL Multiphysics software (COMSOL, Burlington, MA). A 3D inversion method was used to estimate tissue properties. Results: In all the simulation results, the cylindrical lesion can be identified based on the modulus difference between the region and its surroundings. The lesion boundary can be delineated based on the normalized residual error (NRE) map of the fitting procedure used for modulus estimation. The estimated complex shear modulus is close to the true value when the applied frequency is or above 60 Hz. The estimate of the storage modulus is more accurate than the loss modulus. NRE is typically maximal at the boundary of the inclusion (lesion). Conclusion: This study demonstrates the potential of MRE to differentiate lesions with different shear moduli. The results show the possibility of using MRE to differentiate recurrent tumor from radiation necrosis, based on the difference between their moduli, and the potential of lesion delineation based on NRE at boundaries. Physical phantom studies and ex vivo mechanical testing of the tissue will be used in the future for further verification.
TH‐C‐141‐07: T2‐Weighted 4D‐MRI with Combined Phase and Amplitude Sorting
Authors: Liu Y , Chang Z , Czito B , Bashir M , Yin F , Cai J
Published: July 2013

Purpose: T2‐weighted MR provides excellent delineation of malignant liver lesions due to its superior tumor‐to‐tissue contrast. This study aims to develop a novel T2‐weighted retrospective 4D‐MRI technique for imaging organ/tumor respiratory motion with improved soft‐tissue contrast. Method and Materials: Determine the number of repeated scans (NR) required obtaining sufficient phase information for each slice is the critical component in developing this technique and needs substantial testing with many samples. To do that, computer simulations using RPM respiratory signals of 29 cancer patients were performed to derive the relationships between NR and the following factors: number of slice to scanned (Ns), number of respiratory phases of the 4D‐MRI (Np), and starting phase at image acquisition (P0). Assuming T2‐w HASTE/SSFSE MR sequence to be used to acquire raw images for 4D‐MRI, frame rate of 2 frames/s was used in the simulation. To validate our technique, 4D‐MRI acquisition and reconstruction were simulated on a 4D digital human phantom using parameters derived from the above studies. Retrospective sorting of 4D‐MRI was achieved using a novel phase and amplitude hybrid sorting algorithm by effectively utilizing redundant images. Result: Percentage of complete acquisition of all required phases (Cp) increased as NR increased in an inverse‐exponential (Cp=100*[1‐exp(−0.11*NR)],when Ns=50,Np=10) fashion. NR to achieve 95% completion (Cp=95%) of all required phases, defined as the NR needed for 4D‐MRI, is linearly proportional to Np (Nr∼2.86*Np, r=1.0) but independent of NS and P0. Simulated 4D‐MRI on the digital phantom showed clear pattern of respiratory motion. Tumor motion trajectories measured on 4D‐MRI were comparable to the average input signal, with a mean relative difference in motion amplitude of 16%, presumably due to breathing irregularity. Conclusions: A novel T2‐weighted 4D‐MRI technique based on HASTE/SSFSE sequence have been developed and validated. Future evaluation on patients is desired. This work is partly supported by funding from NIH (1R21CA165384‐01A1) and a research grant from the Golfers Against Cancer (GAC) Foundation.
TH‐C‐141‐11: Evaluation of MR Images as the Planning and Reference Dataset for Daily CBCT‐Based IGRT of the Prostate
Authors: Doemer A , Nurushev T , Siddiqui M , Elshaikh M , Movsas B , Chetty I
Published: July 2013

Purpose: An important question rarely discussed in the CT vs. MRI simulation debate is whether MRI reference images provide an adequate image‐set to use with daily localization such as cone‐beam CT (CBCT). This study compares clinical couch shifts based on daily CBCT images to shifts measured from MR images as the reference dataset for prostate IMRT treatment. Methods: Eight patients undergoing a pilot study had MR imaging along with CT simulation with the intent of evaluating a MR‐simulation process. Patients had T1, T2 and bTFE (balanced Turbo Field Echo) sequences. The remainder of the treatment planning process continued using traditional procedures using CT. Therapists used only the CT scan as a reference for localization. Retrospectively, an observer measured shifts between daily CBCT images and MR reference images. Results: The differences in shift positions for the cohort between therapists and the observer are −0.16cm ± 0.25cm (AP), 0.04cm ± 0.19cm (SI), and — 0.01cm ± 0.14cm (LR). The mean group error for the therapists and the observer were less than 2 mm in all directions. Based on these shifts, the calculated margins for the therapists would be 0.87cm (AP), 0.65cm (SI), and 0.71cm (LR) and for the observer would be 1.1cm (AP), 0.66cm (SI), and 0.70cm (LR). For SI and LR directions both sets of margins are very close to one another. An outlier impacted the AP margin difference by 2.3mm and should be investigated further. This initial analysis suggests that each modality can be considered clinically sufficient for daily localization. Conclusion: The results of this study suggest that MR reference image‐sets can be used for daily image‐guided localization of prostate cancers with at least the same accuracy as current methods. MR simulation provides substantial soft‐tissue contrast and can improve tissue targeting in radiation oncology, as a Result further investigation is warranted.
TH‐C‐103‐01: Motion‐Map Constrained Image Reconstruction for 4DCBCT Reconstruction in IGRT
Authors: Park C , Kim J , Song B , Song W
Published: July 2013

Purpose: We developed a novel low‐dose 4DCBCT reconstruction algorithm, named Motion‐Map Constrained Image Reconstruction (MCIR), that allows reconstruction of high quality and high phase resolution 4DCBCT images with no more than the imaging dose as well as the X‐ray projections used in a standard free‐breathing 3DCBCT (FB‐3DCBCT) scan. Methods: The unknown 4DCBCT volume at each phase was mathematically modeled as combination of FB‐3DCBCT and motion‐map matrix with phase‐specific update vector. The motion‐map matrix, which is the key innovation of MCIR algorithm, was defined as the matrix that distinguishes voxels between ones that are moving and stationary. This 4DCBCT model was then reconstructed with compressed sensing (CS) reconstruction framework such that the voxels with high motion would be aggressively updated by the input phase information and the voxels with less motion would be minimally updated to preserve the FB‐3DCBCT. To evaluate the performance of our proposed MCIR algorithm, we have tested on both numerical phantoms and lung cancer patient CBCT scans. The results were then compared with the 1) clinical FB‐3DCBCT reconstructed using FDK, 2) 4DCBCT reconstructed using FDK, and 3) 4DCBCT reconstructed using the best‐known algorithm to date: PICCS. Results: Examination of the MCIR algorithm showed that high phase resolution 4DCBCT with sets up to 20 phases using typical patient FB‐3DCBCT scan could be reconstructed without compromising the image quality. Moreover, both in numerical as well as in real patient data sets, the MCIR algorithm outperformed the other algorithms. This is achieved without using any a priori data, such as the planning CT. Conclusion: This work demonstrates the potential for providing high‐quality 4DCBCT information during on‐line IGRT and ART environment, without sacrificing imaging dose. Proposed algorithm is also entirely compatible to GPU programming, which could sufficiently speed up the process to achieve within clinically usable time‐frame (<30 sec).
TH‐C‐103‐02: Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for 4D Cone‐Beam CT
Authors: Wang J , Gu X
Published: July 2013

Purpose: Image reconstruction and motion model estimation in four dimensional cone‐beam CT (4D‐CBCT) are handled as two sequential steps conventionally. Due to limited number of projections at each phase, the image quality of 4D‐CBCT is degraded by the view‐aliasing artifacts and the accuracy of subsequent motion modeling is compromised by inferior 4D‐CBCT. The objective of this work is to enhance the image quality of 4D‐CBCT and accuracy of motion model estimation through developing a novel strategy that is able to perform Simultaneous Motion Estimation and Image Reconstruction (SMEIR). Methods: The proposed SMEIR algorithm consists of two alternating steps: 1) model‐based iterative image reconstruction to obtain motion‐compensated CBCT (mCBCT) and 2) motion model estimation through the deformable registration of mCBCT and projections of individual phase of 4D‐CBCT. The iterative reconstruction is based on the simultaneous algebraic reconstruction (SART) technique coupled with total variation regularization. During the forward‐and back‐projection of SART, all projection data are used for the reconstruction of mCBCT by utilizing the estimated deformable vector fields (DVF). The DVF is estimated by matching the forward projection of the deformed mCBCT and projections of other phases of 4D‐CBCT. A 4D NCAT phantom is used to quantitatively evaluate the performance of SMEIR algorithm with comparison to FDK and total variation (TV) minimization algorithm. Results: When all projections are used for image reconstruction by FDK, motion blurring artifacts are presented in 3D‐CBCT, leading to 33.1% relative reconstruction error. View aliasing artifacts are presented in 4D‐CBCT reconstructed by FDK, where the relative error is 46.2%. When TV minimization is used, the relative error is 19.8%. Image quality of 4D‐CBCT is substantially improved by using the proposed SMEIR algorithm and relative error is reduced to 7.6%. Conclusion: The proposed SMEIR algorithm is able to estimate motion model and reconstruct motion‐compensated 4D‐CBCT with high accuracy.
TH‐C‐103‐04: Computed Tomography Image Reconstruction with a Cylindrical Voxelization Method
Authors: Kim J , Ionascu D , Zhang T
Published: July 2013

Purpose: To accelerate the image reconstruction process for iterative algebraic reconstruction techniques using a cylindrical voxelization method. Methods: Iterative image reconstruction has many advantages, such as noise reduction and mitigation of approximate cone reconstruction artifacts, over analytical image reconstruction. But its high computational cost prohibits its use in many clinical applications, such as image guided radiotherapy (IGRT). The system matrix used in iterative reconstruction is often too large to store on the hard drive or in RAM. The cylindrical geometry is a natural fit for the circular scanning trajectory employed in volumetric CT methods such as cone beam computed tomography (CBCT) and tetrahedron beam computed tomography (TBCT). In this study, the iterative simultaneous algebraic reconstruction technique (SART) algorithm using a cylindrical voxelization method is developed for TBCT. The new algorithm takes advantage of the symmetries of the axial scanning geometry used in TBCT and greatly reduces the size of the system matrix. An additional step is needed for interpolating the cylindrical data onto a Cartesian grid. The reconstruction algorithms were tested with numerical phantoms as well as patient CT data. Results: The system matrix with the cylindrical voxelization method is compact and can be easily stored in the computer RAM. SART reconstruction using the cylindrical voxelization method was implemented for the TBCT. The FOM results for the cylindrical voxelization method were slightly better than for the Cartesian voxelization. The cylindrical method was able to provide a speedup of 40 times over the traditional Cartesian method and to provide a reduction of two orders of magnitude in the storage requirement for the system matrix. Conclusion: Image reconstruction using a cylindrical voxelization method was able to provide a significant improvement in the reconstruction speed while maintaining the image quality provided by the conventional reconstruction method using a Cartesian image grid. This work is partially supported by NIH SBIR contract #HHSN261201100045C.
TH‐C‐103‐06: Non‐Coplanar Cone Beam CT Reconstruction with Limited Angle of Projections Using Rigid Image Registration Facilitated Prior Image Constrained Compressed Sensing
Authors: Meng B , Li R , Han B , Cheng J , Xing L
Published: July 2013

Purpose: The purpose of this work is to develop a non‐coplanar cone beam CT (CBCT) image reconstruction method from limited angle of projections. Methods: Planning CT or CBCT acquired at neutral position (without couch rotation/translation) serves as the prior images for successive reconstruction. During the course of radiation therapy, subsequent CBCT scans are acquired from limited angle of projections. An iterative reconstruction algorithm, using prior image constrained compressed sensing (PICCS) framework and rigid image registration, is incorporated to reconstruct the patient image under non‐coplanar geometry. The prior reconstructed image is rotated/translated according to the nominal couch rotation/translation to serve as the prior input of the compressed sensing based iterative optimization. First, The PICCS framework can be solved iteratively using the total variation minimization step and the iterative image reconstruction step with simultaneous algebraic reconstruction technique (SART). Then, rigid image registration between the reconstructed image and the rotated/translated prior image is applied to further calibrate the rotation/translation of the prior image. The iterations of PICCS algorithm and rigid image registration continue until the registration results are below the predetermined threshold. The proposed reconstruction algorithm is evaluated with both digital phantom simulations and experimental data. Results: The proposed CBCT reconstruction algorithm significantly improved the image quality from projections with angular range as small as 72 degrees. It can reduce the rigid translational setup errors under from 8 mms to below 1 mm and the rigid rotational setup error from 5 degrees to below 1 degree. Compared with the original PICCS algorithm alone, the rigid registration step helps to improve the convergence of algorithm significantly. Conclusions: The proposed algorithm provides an accurate remedy for solving the problem of non‐coplanar CBCT reconstruction from limited angle of projections by taking advantage of the combination of PICCS framework and rigid image registration.
TH‐C‐103‐12: Iterative Reconstruction for Dual Energy CT Using Accelerated Barrier Optimization Compressed Sensing (ABOCS)
Authors: Dong X , Niu T , Zhu L
Published: July 2013

Purpose: On an onboard cone‐beam CT (CBCT) system, the poly‐energetic beam generated by current commercial x‐ray tubes hardens as it penetrates the object. The beam‐hardening effect results in CT image artifacts of up to 100 HU, especially around dense objects (e.g. bones). With conventional dual energy method, two basis materials images are decomposed from the data with two different x‐ray spectra (e.g. with low and high tube kVp energies), and then are synthesized for a given single energy, which is free of beam‐hardening artifacts. However, noise in the dual energy projections are propagated through the logarithm and decomposition processes, which results in high noise level and severe streaking artifacts in the basis material images, thus severely degraded the image quality of the synthesized. Methods: In this work, we implement a published accelerated barrier optimization compressed sensing (ABOCS) algorithm, which minimized the data fidelity and the total variance (TV) norm of the two basis material images. Results: In both digital and physical head phantom, our proposed approach significantly suppresses the noise signals and the streaking artifacts. In the digital phantom, the root of mean square errors (RMSEs) of the two basis material image are reduced from 0.92 (Al image), 0.14(water image) to 0.08, 0.03. Conclusion: In this work, we proposed an iterative image reconstruction method for dual energy decomposition. In the both digital and physical head phantoms, the proposed method greatly suppressed the streaking artifacts and reduced the noise level.
TH‐C‐144‐06: TPSPET ‐ A TPS‐Based Approach for In‐Vivo Dose Verification with PET in Proton Therapy
Authors: Frey K , Bauer J , Unholtz D , Kurz C , Kraemer M , Bortfeld T , Parodi K
Published: July 2013

Purpose: PET (positron‐emission tomography)‐based treatment verification uses the irradiation‐induced tissue activation inside the patient. The comparison between measured PET data and corresponding predictions, obtained under the assumption of a correct treatment delivery, is clinically used to validate the proton beam range and the dose application. In contrast to the typically performed time‐consuming Monte Carlo (MC) simulations, TPSPET is introduced as a new approach using the algorithms of the clinical treatment planning system (TPS). The calculation yields β+‐emitter distributions being fully consistent with the treatment planning dose. Methods: Positron‐emitter basic data are obtained from the convolution of TPS reference depth‐dose profiles in water with reaction‐channel dependent filter functions. This database and fast post‐processing enables the calculation of three‐dimensional β+‐emitter distributions based on the same algorithms as used for the planning of the treatment dose. Validation of this TPSPET concept is performed in phantom and patient studies against MC simulations and one‐dimensional filtering of three‐dimensional dose distributions. Results: TPS and MC are based on intrinsically different calculation models, which affect dose and positron‐emitter density predictions. Especially in the case of small fields and tissue inhomogeneities, pronounced limitations of the one‐dimensional filtering of three‐dimensional dose distributions are observed, which can be overcome by TPSPET. In the case of extended patient treatment, good agreement is achieved between TPSPET and MC. Conclusion: In contrast to previously introduced methods, TPSPET provides a faster implementation and is not limited by sensitivity to lateral field extensions. The calculations are performed without modifying the TPS pencil beam algorithm, resulting in positron‐emitter predictions, which are consistent to the planned treatment dose. All these aspects confirm TPSPET as a promising alternative to full‐
TH‐C‐144‐07: Proton Lung Planning Based On Beam Specific PTV
Authors: Flampouri S , Hoppe B , Li Z
Published: July 2013

Purpose: To investigate the optimal use of beam specific PTV (bsPTV) in passively scattered proton lung treatments. Methods: An algorithm is developed and implemented to calculate bsPTV for lung cancer targets. The uncertainties in proton treatments, including setup errors, machine delivery variability, breathing motion, proton range uncertainties and their combinations are taken into account. The effect of breathing motion on the proton range is calculated from 4DCT. Margins can be added for translational and rotational setup errors and breathing motion changes during the course of treatment as well as for their effect on beam range of each treatment field. It is notable that the range uncertainties are calculated not as a function of the total range but taking into account the individual voxel HU uncertainty along a proton beamlet. bsPTVs are calculated for beams delivered on actual lung proton treatments. Plans based on bsPTVs (bsPlan)are generated and compared to conventional proton plans (origPlan). A range of TPS parameters and normalization methods are investigated. Results: All bsPlans increase the treated volume compared to origPlans. When the origPlans and bsPlans are created to compensate for similar uncertainties and are normalized the same way, dose distributions produced are similar (Gamma index>1 to 7–12% of voxels for a 3 field plan). When the bsPlans are normalized per field the maximum dose as well as the irradiated volume increase. Conclusion: A tool to calculate beam specific PTVs for proton treatments is developed to include the uncertainties involved. The volumes created can be used for beam angle optimization (smaller bsPTV corresponds to smaller uncertainty). bsPTVs can be the plan targets without the need of extra margins or they can be used for conventional proton plan evaluation (compared to produced dose distribution).