bluuebunny/crossref_metadata_2025 huggingface dataset:
Medical Physics Practice in Latin America: The Best of Times, The worst of Times
Authors: Fontenla Doracy P
Published: July 2013

Introduction: Our task is to present information on the status of the Medical Physics practice in the different countries in Latin America. Because of its fundamental value, we would like to focus on the status of education and professional medical physics issues in the different countries. Purpose: The IAEA is interested in promoting harmonization of Medical Physics practice worldwide. As a contribution, we must first learn about the diverse status of the medical physics practice, possibilities and needs in the different countries of Latin America. Methods and Materials: The LAASC maintains a close communication with all (or most) Latin American Countries through the valuable contributions of our Latin American Liaisons and Consultants. We also rely heavily on the information and cooperation with ALFIM (The umbrella association of all Medical Physics Associations of the different countries in Latin America). This presentation is based on their contributions. Results: There is a large diversity in the status of Medical Physics practice in the different countries of Latin America. As expected, this discrepancy is mostly due to the different economy status in Latin American countries. However, in many instances the economic deficiencies are somehow compensated by the great creativity of medical physicists in the region. A large credit must be given to the extensive work of the IAEA and IOMP in the education and practice of the profession of medical physics in different countries of Latin America. Conclusions: LAASC always places emphasis on divulging to the Latin America medical physicists the professional and educational opportunities as well as the medical physics literature available through the AAPM website. Through the realization of ISEP workshops it has also been possible to exchange scientific information on the state of the art of the practice of medical physics, and also to divulge AAPM programs available to countries in development, such as PIP, International Affiliate, access to Medical Physics Journal and AAPM protocols, to name a few. We believe that through cooperation and work together with ALFIM, the AAPM could contribute to make a sustainable impact to the medical physics profession in Latin American Countries.

Learning Objectives:

1. Understand the fundamental problems associated with the different medical physics practice levels encountered in Latin America

2. To become familiar with on‐going work in developing countries in Latin America in relation to medical physics education and professional recognition.

3. Learn how the AAPM could make a sustainable impact.
TU‐E‐WAB‐01: How Do We Couple Quantitative Imaging and Models of Tumor Response to Improve Multimodality Therapy?
Authors: Marks L , Moiseenko V , Stewart R , Jeraj R
Published: July 2013

Multimodal treatments of solid malignancies have grown in prominence as new trials seek to effectively combine radiation therapies and molecularly targeted therapies. However, inter‐patient and intra‐patient variations in therapeutic response have yielded inconclusive trial outcomes. Quantitative imaging has been suggested as a means of overcoming some of these variations through individualized target definition, dose escalation, and response assessment due to its predictive value for therapy outcomes. Nonetheless, current visualization techniques at limited spatial resolution provide a narrow view of the underlying molecular, cellular and multicellular processes that make up the comprehensive biological response to therapies. Additionally, temporal gaps among longitudinal imaging time points limit dynamic response assessment. Biological imaging‐based and multi‐scale modeling of tumor response to combination therapies is a new area of clinical research that seeks to bridge these visualization and temporal gaps. This symposium will summarize recent clinical trials that are guided by quantitative imaging, address challenges of linking molecular imaging to heterogeneous biological properties, enumerate models of target definition and biologically effective response to therapy, and propose integrative methods for imaging‐based models of multimodal therapy dynamics. Most importantly, the session will bring together clinicians and scientists alike specializing in imaging and dose‐response modeling to partake in a panel discussion around the thematic question: how do we couple quantitative imaging and models of tumor response to improve multimodal therapy?

Learning Objectives:

1. Understand the expanding role of quantitative imaging for therapy target definition, dose escalation, and response assessment.

2. Gain an improved understanding of how physical and biological mechanisms contribute to therapy response across relevant time and spatial scales.

3. Discuss strategies to integrate information from clinical trials with imaging and tumor modeling so as to personalize treatments and improve outcome assessment.

Supported in part by grant CA69579 (LBM)
TU‐E‐141‐04: Dose Reconstruction for DMLC Tracking and Gating in Adaptive Prostate Radiotherapy
Authors: Colvill E , Ng J , O' Brien R , Poulsen P , Booth J , Keall P
Published: July 2013

Purpose: To assess the accuracy of dynamic multileaf collimator (DMLC) tracking delivery and the comparison to gating as an intervention strategy for adaptive prostate radiotherapy. Methods: An experimentally benchmarked isocenter shift dose reconstruction method was used to retrospectively assess the dose distributions delivered during previously observed intensity modulated arc therapy (IMAT)(RapidArc) fractions and compared to the dose distributions which would have been delivered had DMLC tracking or gating intervention strategies been implemented. Delivered dose distributions were constructed by incorporating the monitored prostate motions with the patients original treatment plan, to mimic the treatment delivery. These were compared with dose distributions calculated for DMLC tracking delivery created using the same dose reconstruction method with the addition of MLC positions from dynalog files obtained during tracking simulation with the original motion input. The tracking dose distributions were then compared to the gated dose distributions, constructed using altered motion files created to mimic the application of a gating threshold of 3mm for 5 seconds. Results: The delivered dose distributions showed that dosimetric effects of intrafraction prostate motion were substantial for some fractions with a decrease of more than 5% from the planned PTV D95 value. Evaluation of dose distributions for DMLC tracking delivery showed that tracking was effective in improving the PTV D95 to within 2% of planned value for all fractions, while gating improved the PTV D95 to within 4% of the planned values. Conclusion: Intrafraction prostate motion can Result in a decrease in target coverage. The results of our study suggest that the implementation of DMLC tracking would allow for higher accuracy in the delivery of prostate cancer radiotherapy treatments beyond the accuracy achievable through gating intervention. This work is supported by an NHMRC Australia Fellowship
TU‐E‐141‐11: Respiratory‐Correlated Dose Rate Modulated Therapy as An Alternative to Gated Radiotherapy
Authors: Fontenot J
Published: July 2013

Purpose: To investigate the potential of respiratory‐correlated dose rate modulation (DRM) as a motion management strategy. Methods: Like to conventional gating, a DRM plan targets a specific phase of the respiratory cycle. Unlike conventional gating, the binary modes of a DRM plan are high dose and low dose rate. The low dose rate mode facilitates cine‐EPID imaging of the irradiated volume. When the target moves within the beam's eye view (BEV), high dose rate mode is enabled to delver the therapeutic dose. This approach is only possible with the ability of the linear accelerator to precisely changes dose rates that has accompanied commercial rotational IMRT deliveries. The dosimetric performance of DRM was investigated using a four‐dimensional simulation of dynamic respiratory phantom. Conventional and gated plans were constructed to target all phases and exhale phase of motion, respectively. The DRM plan was constructed as the sum of 2 plans: 1 targeting the exhale phase and 1 targeting all phases of motion, with the weighting determined by the minimum and maximum dose rate values. Results: Dose coverage and homogeneity in the target was similar over all plans. In the lung, V40Gy, V20Gy, and V10Gy were nearly identical for the gated and DRM plans, and 40% lower than the conventional plan. The value of V5Gy in the gated plan was 30% lower than the conventional plan and 15% lower than the DRM plan (500 MU/min maximum dose rate). If the maximum dose rate was increased (1500 MU/min), the advantage in V5Gy in the gated plan over the DRM plan was reduced to 2%. Conclusion: DRM plans produce dose distributions comparable to conventional gating, with the advantage of being able to localize the tumor within the BEV of the treatment fields.
TU‐G‐108‐01: Prediction in Clinical Response to Radiation Therapy for Head and Neck Cancer
Authors: Huang Z , Mayr N , Lo S , Winkler S , Yuh K , Lok C , Liu T , Stephenson S , McLawhorn R , Rasmussen K , Rice J , Yuh W
Published: July 2013

Purpose: A tumor control probability (TCP) model incorporating a generalized linear‐quadratic (gLQ) model was investigated to predict clinical outcome after radiation therapy for patients. The clinical utility of the model incorporating the following individually measured radiobiology parameters: intrinsic radiosensitivity, proliferation and number of clonogenic cells, was evaluated. The hypothesis in the study was that the incorporation of individually measured tumor parameters into the TCP model would increase its reliability in predicting treatment outcome compared with the use of average population derived data. Methods: 46 patients with head and neck tumors were included and most of them received both external beam radiotherapy and brachytherapy (as published in Acta Oncologica, 2009; 48:584–590). Primary tumor size was drawn from case records and pre‐treatment CT scans or MRI using two or three dimensional measurements to calculate tumor volume. Surviving fraction after 2 Gy (SF2), reflecting intrinsic radiosensitivity, was estimated by a soft‐agar clonogenic assay (as published in Int J Radiat Oncol Bio Phys, 2000; 46:13–19). Eighteen patients receiving external beam treatment alone were used to perform statistical analyses. Local control was determined by follow‐up records. Results: SF2 correlated significantly with tumor response (p=0.04, Mann‐Whitney test), but initial tumor volume did not (p=0.6). Eight of the 18 patients had a >95% calculated tumor control probability and none developed a local recurrence, yielding a negative predictive value of 100%, compared with 67% for population‐derived data. There was a statistically significant difference in local control levels between the patient group with >95% vs. <5% predicted probability of local control (p<0.001). Conclusion: The results suggest that incorporation of measured biological data within a TCP radiobiological model would improve its ability to predict radiation therapy outcome.
TU‐G‐108‐05: Assessment of Different Machine Learning Techniques for Multivariate Radiation Pneumonitis Modeling
Authors: Lee S , Bradley J , Ybarra N , Jeyaseelan K , Seuntjens J , El Naqa I
Published: July 2013

Purpose: We intended to perform an independent verification of different machine learning methods for inferring radiation pneumonitis (RP) risk from patient‐specific biological and dosimetric informationMethods: 29 NSCLC patients who received chemoradiation were recruited from two institutions (22 from WUSTL, 7 from McGill). Blood samples were collected from each patient before and during radiotherapy (RT). From each sample the concentration of the five following candidate biomarkers were measured: alpha‐2‐macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF‐β), interleukin‐6 (IL‐6), and osteopontin (OPN). Dimensionality of the raw biomarker data was reduced by a semi‐supervised variable selection scheme. The reduced biomarker variables along with three known dosimetric RP predictors (mean lung dose, V20, tumor position in superior‐inferior direction of the lung) were used as features for classifying high RP risk (CTCAE grade 2 or higher) patients. Four different machine learning methods (Bayesian Network, Logistic Regression, Naive Bayes, Support Vector Machine) were used for training classifiers on the WUSTL subset and were tested with respect to classification performance on the McGill subset. Results: The following 4 biomarker variables were chosen by the variable filtering: (1) pre‐RT concentration level of α2M, (2) ratio of pre‐to intra‐RT levels of α2M, (3) intra‐RT ACE level, (4) intra‐RT TGFβ level. The best performance was achieved by Support Vector Machine in terms of classification accuracy (85.71%) and the area under the ROC curve (AUC) (0.9167). Bayesian Network recorded the same accuracy and slightly lower AUC (0.8750). Less accurate models were Naive Bayes and logistic regression in which overfitting was predominant with the largest difference in classification performance between the training and the testing dataset. Conclusion: Preliminary results from this ongoing study suggests the need of a machine learning approach capable of modeling inter‐relation between physical and biological variables where commonly used multivariate logistic regression would be inappropriate.
TU‐G‐108‐06: Anatomical Localization of Late Rectal Toxicity Predictors in Prostate Radiotherapy
Authors: Foo K , Ebert MA , Carolan MG , Haworth A , Bulsara M , Joseph D , Denham JW
Published: July 2013

Purpose: To determine dosimetric predictors of late rectal toxicity after prostate radiotherapy, differentiating between dose to rectum and anal canal. Methods: Clinical and 3D dosimetric data for 738 patients were prospectively collected in a clinical trial (TROG 03.04 “RADAR”) of variable‐duration hormone therapy with conformal radiotherapy using rectal DVH constraints of V65<40%, V70<30%, V75<5%. Dose‐volume histograms were generated for whole anorectum, anal canal, rectum and matched to longitudinal toxicity (measured 6‐monthly). Outcome measures were: bleeding, urgency/tenesmus (grade 1+, 2+, 3), pooled Common Toxicity Criteria version 2 proctitis (grade 1+, 2+), incontinence, bowel bother, diarrhea, and anorectal pain. Both prevalence (at 30 months post radiotherapy) and actuarial incidence (time‐to‐failure) endpoints were analyzed. Rectal mean dose and V5–V70 were used as factors in multivariate and univariate logistic regression (prevalence) and Cox proportional hazards (incidence) analyses. Results: Median follow‐up was 6.5 years post‐treatment. Multivariate models produced erratic effect estimates due to multicollinearity arising from constrained DVH shape as mandated by the trial protocol. Univariate analysis identified that pain, pooled proctitis, incontinence were predicted most by anal canal dose. Bleeding and diarrhea were not subvolume dependent. Bowel bother was not associated with any factor. Urgency/tenesmus association with subvolume depended on the outcome grade used. Conclusion: Doses to different subvolumes of the rectum are responsible for different types of toxicity. Different grades of the same toxicity are predicted by different subvolumes and DVH points. For datasets planned with dose constraints, multicollinearity of DVH points limits the usefulness of multivariate analysis. Further work will estimate DVH constraints to limit toxicity, and Lyman model parameters from the same data. Funding: National Health and Medical Research Council (Australian Government), Hunter Prostate Cancer Alliance, Trans‐Tasman Radiation Oncology Group. Conflicts of interest: none declared
TU‐G‐141‐01: BEST IN PHYSICS (JOINT IMAGING‐THERAPY)‐A Novel 4D CT Acquisition and Analysis Technique to Generate Low Noise Artifact‐Free Images at User Selected Breathing Phases
Authors: Thomas D , White B , Gaudio S , Jani S , Lee P , Lamb J , Low D
Published: July 2013

Purpose: To develop a novel 4DCT technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove motion‐induced artifacts. Methods: Five patients were imaged under free breathing conditions 25 successive times in alternating directions with a 64‐slice CT scanner using a low dose fast helical protocol. A pneumatic bellows around the abdomen was used to as a breathing surrogate. The lungs were segmented from each image. Deformable registration was used to register the first to the subsequent 24 segmented images. Voxel‐based motion model parameters were determined using a published breathing motion model. A low‐noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical noise by a factor of 5. The motion model was used to deform the low noise image to any user‐selected breathing phase. Accurate HU values were assigned to each voxel in the reconstructed images. Results: Images produced using the model at user‐selected breathing phases did not suffer from motion artifacts. The mean discrepancy between the breathing motion model results and the measured positions corresponding to each scan was determined to be 0.7mm (standard deviation of 0.4mm). In each patient, regions near to the myocardium exhibited mean discrepancies greater than 1 mm, which were likely due to uncompensated cardiac motion. Conclusion: The proposed technique can be employed as a clinical 4DCT technique providing motion artifact free images at user‐selected breathing phases. It is robust in the presence of irregular breathing, and allows the entire imaging dose to contribute to the resulting image quality, providing motion artifact free images at a patient dose similar to or less than current 4DCT techniques. We are currently modifying the protocol to work on 16‐slice CT scanners.

This work supported in part by NIH R01CA096679
TU‐G‐141‐03: An On‐Board 4D‐CBCT Reconstruction Technique Using Limited‐Angle Projections Based On Motion Modeling and Free‐Form Deformation (MM‐FD)
Authors: Zhang Y , Segars P , Yin F , Ren L
Published: July 2013

Purpose: To develop an on‐board 4D‐CBCT(OB‐4D‐CBCT) reconstruction technique using prior information and limited‐angle projections for 4D inter/intra‐fractional target verificationMethods: The OB‐4D‐CBCT at each phase is considered as a deformation of planning 4D‐CT at one selected phase (prior image). The MM‐FD technique solves the deformation field maps(DFMs) with a two‐step approach: 1. MM: a principal component analysis‐based method is applied to the planning 4D‐CT to extract a motion model. Coarse estimation of DFMs are obtained by optimizing motion model parameters to meet data‐fidelity constraint for limited‐angle on‐board projections. 2. FD: the coarse DFMs are further fine‐tuned by free‐form deformation based on data‐fidelity constraint and deformation‐energy minimization, using constrained‐optimization algorithm ASD‐POCS. OB‐4D‐CBCT is then reconstructed by deforming prior volume based on final DFMs. The 4D Digital Extended‐cardiac‐torso(XCAT) Phantom was used to evaluate MM‐FD. A lung patient with 3‐cm diameter lesion was simulated to have various anatomical and respirational changes from 4D‐CT to OB‐4D‐CBCT including respiration amplitude change, lesion size change, lesion average‐position change, and phase shift between lesion and body respiratory cycle. The lesions were contoured in both the reconstructed and ‘ground‐truth’ OB‐4D‐CBCT for comparison. 3D volume percentage‐difference (VD) and center‐of‐mass shifts(COMS) were calculated for evaluation. The MM‐FD technique was compared with MM‐only and FD‐only techniques. Results: For all patient scenarios, the mean original VD/COMS between prior volume and true OB‐4D‐CBCT were 137.65%/15.5mm. Using orthogonal‐15‐degree scan angle, the mean VD/COMS between reconstructed and true OB‐4D‐CBCT for MM‐only, FD‐only and MM‐FD techniques were 64.67%/4.9mm, 98.80%/12.1mm and 20.90%/1.3mm, respectively. For orthogonal‐30‐degree scan angle, the corresponding results were 63.87%/4.9mm, 79.92%/9.9mm and 15.23%/0.5mm. For single‐view 30‐and 60‐degree projections, the mean VD/COMS for MM‐FD technique were 32.67%/2.9mm and 19.67%/1.1mm, respectively. Conclusion: The MM‐FD technique substantially improves the reconstruction accuracy for OB‐4D‐CBCT using limited‐angle projections. It can potentially improve the inter/intra‐fractional 4D‐localization accuracy for lung SBRT. Research partially supported by grant from Varian Medical Systems
TU‐G‐141‐04: Phase‐Selected 4d CBCT Acquisition Based On An Internal‐External Motion Correlation Model
Authors: Fast MF , Wisotzky E , Oelfke U , Nill S
Published: July 2013

Purpose: X‐ray projections acquired with linac‐based scanners for 4d CBCT reconstruction are traditionally binned into breathing phases ‘after‐the‐fact’, i.e. after the image acquisition. This approach leads to an uneven spreading of projections over breathing phases, which compromises the image quality. We have therefore developed a novel approach based on actively triggered projections employing the forward‐predicted position of the tumor at the time of image acquisition. Methods: The prediction is based on a high‐frequency external motion sensor and uses linear regression to compensate for the effective latency of the external motion sensor and the x‐ray imaging chain (160 ms). An internal‐to‐external motion model is established from the first 40 images, by correlating the external chest‐wall displacement (AP) with the internal motion (SI) derived from the images (using an ‘Amsterdam shroud’). We used a lung phantom moving on a sinusoidal trajectory (15 mm SI amplitude) with a 3.5/5.0 s period. The AP motion (5 mm) was phase‐shifted by 13°. The gantry rotation times were adapted depending on the breathing period during prediction training (first 30 s). Results: The first 40–50 projections were acquired at 6.25 Hz to cover 1–2 breathing cycles. During post‐processing, these images were binned and used alongside the actively triggered frames. For the 4d CBCT mode, we were able to evenly spread the projections over all breathing phases. Motion‐induced artifacts were effectively mitigated and the tumor‐to‐lung contrast became more evenly distributed over the respiratory phases. We also demonstrated a quasi‐4d mode consisting of actively triggered peak‐exhale/peak‐inhale images only. By increasing the image frequency for the quasi‐4d mode, it was possible to reduce the gantry rotation to 2.5–3.5 min without compromising the number of projections. Conclusion: Our new approach to 4d CBCT imaging allows for active phase selection as opposed to the traditional retrospective phase binning.
TU‐G‐141‐08: Impact of Audiovisual Biofeedback Respiratory Training On 4D‐PET Image Quality
Authors: Yang J , Yamamoto T , Gopalan S , Berger J , Johnston E , Chung M , Eclov N , Diehn M , Loo B , Graves E , Keall P
Published: July 2013

Purpose: To test the hypothesis that audiovisual biofeedback (AV) respiratory training improves 4D‐PET image quality. Methods: In an IRB‐approved study, two 4D‐PET scans, with AV and free breathing, were acquired sequentially for 10 lung cancer patients. AV was performed by guiding a patient to follow a waveform with period and displacement determined by averaging the respiratory cycles of the patient. The acquisition time for the second scan was increased to compensate for activity decaying. 4D‐PET images were created by phase‐based gating with six bins. The 4D‐PET images with AV were compared to those with free breathing by quantifying (1) SUVmax and (2) tumor motion range. The motion range was quantified through the center of mass of a segmented tumor volume at each phase. To investigate the correlation of respiratory regularity and SUVmax, we separated tumors into two groups: (1) tumors with large motion (>5 mm, spatial resolution) and (2) the others. We tested the hypothesis using the two‐tailed paired t‐test. Results: For tumors with large motion, the average SUVmax with AV and free breathing was 11.5 and 10.8, respectively (p = 0.03); for the other tumors, the average SUVmax with AV and free breathing was 14.4 and 12.4, respectively (p < 0.001). For tumors with large motion, the average motion range with AV and free breathing was 9.1 and 7.4 mm (p = 0.36) in the superior‐inferior direction, respectively. However, the results were patient‐specific. In the 7 tumors with large motion, SUVmax with AV was improved for 3 tumors, worsen for 2 tumors, not correlated with respiratory regularity for 2 tumors. Conclusion: A 10‐patients study demonstrated statistically significant improvement of SUVmax with AV for tumors with large motion. Futures studies will focus on optimizing the interface for improved compliance and recruiting patients who have tumors with large motion.

Research supported by the Kwanjeong Education Foundation in Korea, NIH/NCI 2 R01 CA 093626, and Stanford BioX Interdisciplinary Initiatives
TU‐G‐141‐09: Real Time Estimation of Prostate Tumor Rotation and Translation with a KV Imaging System Based On An Iterative Closest Point Algorithm
Authors: Nasehi J , O' Brien R , Poulsen P , Keall P
Published: July 2013

Purpose: During cancer radiotherapy a small translation or rotation of the target could be important and change the delivered dose of the treatment. In this work, we implement and test an algorithm to estimate prostate cancer rotation and translation in real‐time using a kV imaging system. Methods: We developed a method based on the iterative closest point (ICP) algorithm and the three dimensional target positions for estimating rotation and shape deformation of prostate in real time. For evaluating our algorithm, 11748 kV images acquired from ten patients during intensity modulated arc therapy treatment (one fraction for each patient were used). The three fiducial markers coordinates were calculated from two dimensional kV images and used as input to the ICP algorithm for investigation of the rotation around three axes. Results: The root mean square error (RMSE) of the rotation matrix was calculated for ICP algorithm and the value was less than 0.16mm. Correlation between all six degrees of freedom shows that the highest correlation belongs to the anterior‐posterior (AP), and superior‐inferior translation with 0.67, and Roll and Yaw rotation with cor=−0.33 has the second highest correlation in our study. The maximum standard deviation (as expected and previously reported) belongs to AP and Roll. Conclusion: a 2D to 6D estimation method has been developed and applied to a 10‐patient dataset. Given the widespread availability of cancer radiotherapy systems with single x‐ray imagers, this method could have a major impact on prostate IGRT, and potentially other cancer sites where three or more implanted markers are routinely placed. Acknowledgement: This work is supported by an NHMRC Australia Fellowship. Jin‐Aun Ng, Jeremy Booth, Thomas Eade, Andrew Kneebone and Walther Fledelius are acknowledged for clinical implementation of the kV intra‐treatment monitoring program.
TU‐G‐103‐09: Measurement of Planar Average Equilibrium Dose of CT
Authors: Liang Y , Dutta A , Jambawalikar S , So J , Lynch D
Published: July 2013

Purpose: The planar average equilibrium dose of CT is measured using the methodology suggested by AAPM report No. 111, and compared with the traditional weighted average estimate and the new proposed equal weighted average estimate. Methods: The study used a 320 slices Toshiba Aquilion ONE scanner, three CTDI body phantoms with total length of 46.5 cm (two of them are nested 3 piece CTDI phantoms with diameters of 10, 16, and 32 cm for each piece), a Radcal Accu‐Pro electrometer, and a Radcal 0.6 cc farmer chamber. In the helical mode (120 kVp, 200 mAs, nominal beam width 0.5×16 mm, pitch 0.938), we measured the cumulative dose DL(z=0) at the center of the phantoms and 12 clock position at radius of 4, 7, and 15 cm from the center, for scan length L=100, 200, 300, and 400 mm. The procedure was repeated for pitch setting of 0.641 and 1.438 and nominal beam width of 0.5×160 mm and 0.5×64 mm at L=400 mm. Results: The data obtained at four scan lengths were fit to estimate the equilibrium Dose (Deq) at radius of 0, 4, 7 and 15 cm from the phantom center. Three models (linear, quadratic, and exponential) were then used to fit DL(z=0) and Deq as a function of radius to phantom center for various scan length, pitch, and beam width combinations. We calculated the planar average cumulative (and equilibrium) dose using the above models and compared the results with the traditional weighted average estimate (Dcenter/3+2Dedge/3) and the new proposed equal weighted average estimate (Dcenter/2+Dedge/2). The planar average dose differs from the traditional weighted estimate within 2%, and equal weighted estimate within 10%. Conclusion: The traditional weighted average method provides a more accurate estimate to the CT planar average equilibrium dose than the proposed equal weighted average method.
TU‐G‐134‐01: 4D‐MRI Using Body Area as Internal Respiratory Surrogate: Initial Patient Results
Authors: Yang J , Yin F , Wang H , Chang Z , Czito B , Bashir M , Cai J
Published: July 2013

Purpose: We have previously developed a novel 4D‐MRI technique using body area (BA) as internal respiratory surrogate. This study is to demonstrate our initial patient results via validating the accuracy of tumor motion measurements and the improvement of tumor‐to‐tissue contrast‐to‐signal ratio (CNR) in the 4D‐MRI. Methods: Five patients with cancer (s) in the liver were enrolled in an IRB‐approved study. All patients underwent 3D/4D CT and MR simulation for treatment planning. 4D‐MRI image were generated by firstly acquiring multiple‐slice multiple‐phase cine‐MR images in the axial plane and then retrospectively binning the images based on respiratory phases. Respiratory signals were determined by tracking changes of the BA of axial MR images. Single‐slice 2D cine MR images were also acquired across the center of the tumor in orthogonal planes (axial, sagittal, and coronal). For data analysis, tumor motion trajectories in the superior‐inferior (SI), anterior‐posterior (AP), and medial‐lateral (ML) directions, were determined from 4D‐MRI and compared to those determined from cine‐MR images, which were used as references. The correlation coefficients (CC) and the differences in tumor motion amplitude were determined between the two. Tumor‐to‐liver CNR was compared between 4D‐MRI and 4D‐CT. Results: 4D‐MRI clearly revealed respiratory motion in all patients. Tumor motion trajectories were comparable between 4D‐MRI and cine‐MR: the mean (± standard deviation (SD)) CC was 0.97(±0.03), 0.97(±0.02), and 0.99(±0.04) in the SI, AP and ML directions, respectively. The mean (±SD) difference in tumor motion amplitude was 0.61(±0.17)mm, 0.32(±0.17)mm, 0.14(±0.06)mm in the SI, AP and ML directions, respectively. The mean tumor‐to‐tissue CNR were improved from 1.52(±2.04) in 4D‐CT images to 7.57(±5.64) in 4D‐MRIs. Conclusion: 4D‐MRI using BA as respiratory surrogate and axial image acquisition has been successfully demonstrated in patients. It is a promising technique for more accurately imaging tumor respiratory motion with improved soft‐tissue contrast. This work is partly supported by funding from NIH (1R21CA165384‐01A1) and a research grant from the Golfers Against Cancer (GAC) Foundation.
TU‐G‐134‐04: Multifunctional Molecular Probes for Targeting and Imaging of Epidermal Growth Factor Receptors in Vivo
Authors: Hung C , Kuo Y , Choi E , Mistry N , Raghavan S , Lapidus R , Xu S , Zhuo J , Suntharalingam M , Gullapalli R , D' Souza W
Published: July 2013

Purpose: To demonstrate the epidermal growth factor receptor (EGFR) targeting specificity in‐vivo of a multifunctional molecular probe by combining receptor‐targeting monoclonal antibody (mAb) with the positive contrast agent Gadolinium (Gd) in dendrimer nanoclusters. Methods: The molecular probe comprised two functional subunits: anti‐EGFR antibodies for tumor targeting and dendrimer nanoclusters (DNCs) loaded with Gd for MRI contrast. DNCs were formed by crosslinking G5 dendrimers using Bis‐N‐succinimidyl‐(pentaethylene glycol) ester and sulfo‐NHS‐LC‐biotin. DiethyleneTriaminePentaacetic Acid (DTPA) anhydride was used to bind Gd to the DNC in alkaline conditions. Biotinylated anti‐EGFR mAb(C225) was mixed with the DNC, followed by the addition of avidin, a cross‐linker that facilitates probe synthesis. The probe was injected through the tail vein into nude mice that were subcutaneously implanted with human head and neck cancer (TU159) xenografts. A 3‐dimensional FLASH‐MRI time course study was performed on a 7T scanner, such that volumetric images were acquired every minute in the first 5 min after the injection, every 5 min in the first hour, and after 4 hours. Region of interest (ROI) analysis was performed using ImageJ. Results: An increase in image contrast was observed in the ROI corresponding to the targeted tumor following the tail vein injection of the probe. The time course study showed that the image contrast (IC) at the target region increased substantially in the first 5 min after probe injection and was stable through the first 240 min. In a control experiment, the IC increase was observed on the mouse with the same injected dose of gadodiamine; however in this case, IC subsided after the first 30 min. Conclusion: The molecular nanoprobe is able to 1) selectively target the EGF receptors in vivo and 2) allow drug uptake to be imaged while using less toxic probe doses.
TU‐G‐134‐07: Application of Regularization for Improving Reconstruction of Dynamic Contrast‐Enhanced MRI of Spinal Cord Injury
Authors: Hui C , Herrera J , Narayana P
Published: July 2013

Purpose: DCE‐MRI allows quantitative assessment of vascular permeability and is the preferred method for investigating blood‐spinal cord barrier (BSCB) permeability in spinal cord injury (SCI). However, substantial variability in the permeability parameters has been observed in DCE‐MRI analysis. We hypothesized that reconstructing DCE‐MRI data with regularization will reduce the variability. Methods: Previous studies have shown that the use of iterative reconstruction with regularization can reduce noise in MRI data. In this study, total variation and wavelet sparsity were used as regularization functions. Reconstruction was done by minimizing the regularization functions using Bregman iteration. Simulations were performed to see if noise reduction resulting from regularization can improve accuracy of permeability parameters. Simulated DCE‐MRI data were generated with 5% noise. The corresponding dynamic images were reconstructed using inverse Fourier transform (IFT) and also with regularization. The accuracy of the permeability parameters computed from their respective reconstructions was compared. Finally, in‐vivo DCE‐MRI experiments were performed in 12 animals to determine the extent of BSCB compromise after experimental SCI. Permeability parameters were calculated from the images reconstructed with the two different methods. The inter‐subject variability in permeability parameters was compared between the two reconstruction methods. Results: From the simulations, the relative root mean square (RMS) error in KAtrans was 36% using IFT. Reconstruction with regularization reduced the error of the reconstructed images which resulted in a reduction of RMS error in KAtrans to 23%. In our in‐vivo SCI study, the inter‐subject standard deviation of average KAtrans was 39% using IFT. Regularization can produce noticeably smoother images and KAtrans maps. As a Result, reconstruction with regularization reduced the inter‐subject standard deviation of KAtrans to 34%. Conclusion: Iterative reconstruction with regularization can improve quality of the reconstructed images in DCE‐MRI. The resulting images yield more accurate permeability parameters and can reduce the inter‐subject variability in DCE‐MRI.
WE‐A‐105‐01: Preparing for the ABR Therapy Exam
Authors: Becker S , Hipp E , Culberson W
Published: July 2013

Preparing for all three parts of the Therapy ABR Physics boards is more than just studying as much material as possible. There will always be material that is missed and gaps in one's knowledge. Therefore it is crucial to understand how the materials relate to each other and to clinical experiences in order to fill those gaps.

Each part of the board exam presents its own difficulties: Part I: Determining what material to study, most of which might have been learned a long time ago.

Part II: Solving a vast array of clinical calculations rapidly by hand, which are usually done by a computer.

Part III: Gaining crucial clinical experiences and being able to explain orally how they are performed and what they mean.

All three require different skill sets and preparation, from memorizing vast amounts of material, to rapidly recognizing and solving calculations, to being able to easily and confidently respond to oral questions about all aspects of working in the clinic and why those clinical methods and procedures are performed that way. This symposium is not a comprehensive review of all required study material. Instead it focuses on the previously mentioned problems and required skill sets that are needed for each part of the board exam. Expert Medical Physicists and Medical Faculty will share their experiences and methods to help students best prepare for the challenges of each individual exam along with helping students cope with the anxiety of the exam process.

Learning Objectives:

1. How to Prepare for Part 1 by determining study material

2. How to Prepare for Part 2 by learning to solve clinical calculations rapidly

3. How to Prepare for Part 3 by determining critical clinical experiences and learning how to answer questions orally
WE‐A‐134‐03: A Kernel‐Based Method for Non‐Rigid Tumor Tracking in KV Image Sequence
Authors: Zhang X , Homma N , Ichiji K , Takai Y , Narita Y , Abe M , Sugita N , Yoshizawa M
Published: July 2013

Purpose: To develop a fast algorithm to track the non‐rigid lung tumor motion in KV X‐ray image sequence for image‐guided radiation therapy (IGRT). Methods: The KV X‐ray image sequence was acquired on the Varian On‐Board Imager (OBI) KV imaging system. As a pre‐processing, a histogram equalization was employed to enhance the tumor contrast in the images. In the first frame, a target model containing tumor area was delineated manually, and its feature space was represented by its histogram weighted with an isotropic kernel. In the subsequent frames, the tumor location was estimated by maximizing a Bhattacharyya coefficient which measures the similarity between the target candidates in the current frame and the target model in the previous frame. The numerical solution of maximizing the Bhattacharyya coefficient was performed by using a mean‐shift algorithm. Results: We implemented four conventional template matching algorithms to compare their performance with the proposed method. Experiments were conducted on four lung tumor kV image sequences of resolution 0.26 mm/pixel. Each sequence consists of 100 frames. The ground truths of the tumor motion were obtained by manual localization. Experimental results demonstrated that the proposed algorithm was superior to the conventional template matching algorithms in terms of its accuracy and computational cost. Conclusion: This study aims at developing a robust and fast algorithm used for tracking the lung tumor for real‐time IGRT. Due to the histogram representation of the target feature, the proposed method is robust against the tumor's shape deformation. In addition, the proposed tracking algorithm is based on a kernel gradient estimation and its computational cost is much lower than that of the conventional template matching algorithms that involve in exhaustive search procedures. The proposed method shows the effectiveness of tracking tumor in KV image sequence and a promising prospect for MV image sequence.
WE‐A‐134‐05: Comparison Between a Clinical Protocol and a Fast Automatic Update for Correlation Model Retraining During Gimballed Tumour Tracking: Impact On Margins and Target Dose Coverage
Authors: Poels K , Depuydt T , Verellen D , De Ridder M
Published: July 2013

Purpose: to evaluate the reduction of correlation errors for both the clinical user‐dependent and an automatic correlation model (CM) retraining procedure based on patient data acquired during real‐time tumour tracking (RTTT). Methods: During clinical RTTT, tumour positions were detected using 0.5 Hz orthogonal kV fluoroscopy. Correlation models were retrained by three scenarios: (1) the current clinical procedure with a treatment interruption and rebuild of the correlation model using 20s fluoroscopy, (2) a single CM for the entire treatment without retraining and (3) a continuous retrained CM using an input of 0.5 Hz orthogonal verification images acquired during RTTT. The correlation errors (CE) were calculated by the difference between predicted and detected target positions. For all three retraining procedures, PTV margins were calculated using margin recipes. With the dose reconstruction by convolution of a static planned dose (5 mm PTV‐margin) with a point spread function based on CE, the coverage of the CTV periphery was evaluated for RTTT with the different CM update approaches Results: The clinical CM update procedure showed a 0.9 mm margin reduction compared with omission of intra‐fraction retraining. An additional 0.9 mm margin reduction was achieved by on‐the‐fly CM updating. Without interruptions for correlation remodelling, the treatment time could be reduced by 20% (1−5 minutes). On average, RTTT treatments showed a reduction of 2.4±3.1% for CTV D99 when using a clinical update and was equal to reconstructed CTV D99 values for a single CM treatment (1.6±3.3%) and for continuous CM update (1.6±2.0%). For 2 patients with a larger dose‐loss (>5%) on the CTV periphery using the clinical RTTT routine, the continuous CM approach reduced the dose‐loss to 2%. Conclusion: A fast automatic update of the correlation model reduced PTV‐margins and maintained an adequate CTV dose coverage for a large variety of breathing motion during RTTT. Corporate funding by BrainLab AG and Hercules Funding by the Flemish Government.
WE‐A‐134‐11: Registration of Clinical Volumes to Beams‐Eye‐View Images for Real‐Time Tracking
Authors: Bryant JH , Rottmann J , Lewis JH , Keall PJ , Berbeco RI
Published: July 2013

Purpose: To develop the 2D/3D registration of cine mode electronic portal imaging device (EPID) images acquired during radiotherapy treatment to the planning computed tomography (CT) images and combine it with relative, markerless EPID tumor tracking. Together the methods will provide an automatic absolute tracking between physician defined volumes such as the gross tumor volume (GTV) the treatment field. Methods: During treatment of lung SBRT cases, EPID images were continuously acquired. The relative motion was tracked using a markerless multitemplate algorithm whose accuracy was previously confirmed and assessed with manual tracking. Each image then underwent an intensity based 2D/3D registration to the planning CT. In order to minimize the effect of motion blur, the end‐of‐exhale phase of the four dimensional computed tomography (4DCT) was used. The volume was converted from Hounsfield units into electron density by a calibration curve and DRRs were generated for the beam geometry. Using normalized cross correlation (NCC) between the DRR and EPID image, the best in plane rigid transformation was found. It was then applied to contours in the planning CT, mapping them into the EPID image domain. The best breathing phase for the registration was found with a large set of patient data. Results: The success of 2D/3D registration proved accurate only over certain phases of the breathing cycle. By registering at this time and using relative tracking, we successfully track target volumes in the EPID images throughout the entire treatment delivery. Conclusions: Through the combination of relative tracking and phase dependent EPID/4DCT registration, it is possible to track clinical volumes on EPID images. This knowledge of tumor volumes relative to the treatment field provides powerful information for future applications like motion management, adaptive radiotherapy and delivered dose calculations.
WE‐C‐108‐03: CT‐Based 3D Dose Calculation Method Using Artificial Neural Networks (ANN)
Authors: Moghadam A , Hadad K , Watchman C , Hamilton R
Published: July 2013

Purpose: In this study Artificial Neural Networks (ANN) are trained to develop a fast CT based dose calculation method. The ANN is capable of calculating the 3D dose distribution inside a voxel‐based phantom for prostate I–125 seed implants. The speed allows for accurate CT‐based calculation of the dose distributions during implantation in real time due to plan modifications. Methods: Training the ANN needs a set of accurate sample dose distributions in heterogeneous CT based phantoms. For the purpose of this study, 60000 sample cubicle phantoms were extracted from 3 patient CT studies. The dose distributions of the cubicle phantoms were calculated using MCNP5. The absorption coefficient of each voxel of the cubicle phantom was calculated by analyzing the CT scan. This information was then given to the ANN as the input. The calculated dose by MCNP5 was given to it as the sample output for the purpose of training. The dose distributions calculated by ANNs were then tested for 7000 cubicle phantoms against the MCNP5 results. Finally, the ANN was used to calculate the dose distribution in two real treatment plans. The results were compared against the MCNP5 voxel based results. Results: The regression graphs for individual ANNs show a regression of R=0.99543 for the worst ANN The regression of the best Trained ANN was equal to R=0.9997 for the 7000 cases. The maximum average absolute error of the ANN dose calculation was 3% in comparison with MCNP5. Conclusion: The results of individual ANN calculations and the complete treatment plan calculations verified the accuracy of the ANN method. Capability of the ANN method to calculate the dose distributions for 500 seed positions in less than 10 minutes suggests that this method can be used for model based dose calculation for real‐time plan modifications in the case of LDR brachytherapy.