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Simultaneous and Constrained Calibration of Multiple Hyperspectral Images Through a New Generalized Empirical Line Model

The empirical line (EL) calibration method is commonly used for atmospheric correction of remotely sensed spectral images and recovery of surface reflectance. The current EL-based methods are applicable to calibrate only single images. Therefore, the use of the EL calibration is impractical for imaging campaigns, where many (partially overlapped) images are acquired to cover a large area. In addition, the EL results are unconstrained and an undesired reflectance with negative values or larger than 100% can be obtained. In this paper, we use the standard EL model to formulate a new generalized empirical line (GEL) model. Based on the GEL, we present a novel method for simultaneous and constrained calibration of multiple images. This new method allows for calibration through multiple image constrained empirical line (MIcEL) and three additional calibration modes. Given a set of images, we use the available ground targets and automatically extracted tie points between overlapping images to calibrate all the images in the set simultaneously. Quantitative and visual assessments of the proposed method were carried out relatively to the off-the-shelf method quick atmospheric correction (QUAC), using real hyperspectral images and field measurements. The results clearly show the superiority of MIcEL with respect to the minimization of the difference between the reflectance values of the same object in different overlapping images. An assessment of the absolute accuracy, with respect to 11 field measurement points, shows that the accuracy of MIcEL, with an average mean absolute error (MAE) of ~11%, is comparable with respect to the QUAC.

47 OTHER INSTRUMENTATION↗

Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automated and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.

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SU-E-I-66: Radiomics and Image Registration Updates for the Computational Environment for Radiotherapy Research (CERR)

Purpose: To present new tools in CERR for Radiomics, image registration and other software updates and additions. Methods: Radiomics: CERR supports generating 3-D texture metrics based on gray scale co-occurance. Two new ways to calculate texture features were added: (1) Local Texture Averaging: Local texture is calculated around a voxel within the userdefined bounding box. The final texture metrics are the average of local textures for all the voxels. This is useful to detect any local texture patterns within an image. (2) Image Smoothing: A convolution ball of user-defined radius is rolled over an image to smooth out artifacts. The texture metrics are then computed on the smooth image. Image Registration: (1) Support was added to import deformation vector fields as well as non-deformable transformation matrices generated by vendor software and stored in standard DICOM format. (2) Support was added to use image within masks while computing image deformations. CT to MR registration is supported. This registration uses morphological edge information within the images to guide the deformation process. In addition to these features, other noteworthy additions to CERR include (1) Irregularly shaped ROI: This is done by taking intersection between infinitely extended irregular polygons drawn on any of the two views. Such an ROI is more conformal and useful in avoiding any unwanted parts of images that cannot be avoided with the conventional cubic box. The ROI is useful to generate Radiomics metrics. (2) Ability to insert RTDOSE in DICOM format to existing CERR plans. (3) Ability to import multi-frame PET-CT and SPECT-CT while maintaining spatial registration between the two modalities. (4) Ability to compile CERR on Unix-like systems. Results: The new features and updates are available via https://www.github.com/adityaapte/cerr . Conclusion: Features added to CERR increase its utility in Radiomics, Image-Registration and Outcomes modeling.

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SU-E-I-68: Practical Considerations On Implementation of the Image Gently Pediatric CT Protocols

Purpose: One limitation associated with the Image Gently pediatric CT protocols is practical implementation of the recommended manual techniques. Inconsistency as a result of different practice is a possibility among technologist. An additional concern is the added risk of data error that would result in over or underexposure. The Automatic Exposure Control (AEC) features automatically reduce radiation for children. However, they do not work efficiently for the patients of very small size and relative large size. This study aims to implement the Image Gently pediatric CT protocols in the practical setting while maintaining the use of AEC features for pediatric patients of varying size. Methods: Anthropomorphological abdomen phantoms were scanned in a CT scanner using the Image Gently pediatric protocols, the AEC technique with a fixed adult baseline, and automatic protocols with various baselines. The baselines were adjusted corresponding to patient age, weight and posterioranterior thickness to match the Image Gently pediatric CT manual techniques. CTDIvol was recorded for each examination. Image noise was measured and recorded for image quality comparison. Clinical images were evaluated by pediatric radiologists. Results: By adjusting vendor default baselines used in the automatic techniques, radiation dose and image quality can match those of the Image Gently manual techniques. In practice, this can be achieved by dividing pediatric patients into three major groups for technologist reference: infant, small child, and large child. Further division can be done but will increase the number of CT protocols. For each group, AEC can efficiently adjust acquisition techniques for children. This implementation significantly overcomes the limitation of the Image Gently manual techniques. Conclusion: Considering the effectiveness in clinical practice, Image Gently Pediatric CT protocols can be implemented in accordance with AEC techniques, with adjusted baselines, to achieve the goal of providing the most appropriate radiation dose for pediatric patients of varying sizes.

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SU-E-I-88: Mammography Imaging: Does Positioning Matter?

Purpose: In mammography, compression is imperative for quality images and glandular radiation exposure dose. The thickness of the compressed breast directly determines mammography acquisition parameters. The compressed thickness varies due to variation in technologist practice, even for the same patient imaged at different time. This study is to investigate potential effect of the variation in breast positioning on radiation dose and image quality. Methods: Radiation dose at different thicknesses was measured with a BR-12 breast phantom for both conventional craniocaudal view and tomosynthesis in a Hologic Tomosynthesis mammography system. The CIRS stereotactic needle biopsy training phantom embedded dense masses and microcalcification in various sizes were imaged for image quality evaluation. Radiologists evaluated images. Clinical mammograms from the same patient but acquired at different time were retrospectively retrieved to evaluate potential effects of variation in positioning. Results: Acquisition parameters (kVp and mAs) increase with the increased phantom thickness. Radiation exposure increases following an exponential trend. The stereotactic phantom images showed loss of spatial and contrast resolution with inappropriate positioning. The compressed pressure may not be a good indicator for appropriate positioning. The inclusion of different amount of pectoralis muscle may lead to the same compressed pressure but different compressed thickness. The initial retrospective study of 3 patients showed that there were potential large variations in positioning the same patient at different examination time, resulting in large variations in patient radiation dose and image quality. Conclusion: Variations in patient positioning potentially influence patient radiation dose and image quality. The technologist has the critical responsibility to position patient to provide quality images in spite of different breast and body types. To reduce intra and inter practice variations in positioning patient, a training program among each breast imaging center may be a need.

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SU-E-J-112: The Impact of Cine EPID Image Acquisition Frame Rate On Markerless Soft-Tissue Tracking

Purpose: Although reduction of the cine EPID acquisition frame rate through multiple frame averaging may reduce hardware memory burden and decrease image noise, it can hinder the continuity of soft-tissue motion leading to poor auto-tracking results. The impact of motion blurring and image noise on the tracking performance was investigated. Methods: Phantom and patient images were acquired at a frame rate of 12.87Hz on an AS1000 portal imager. Low frame rate images were obtained by continuous frame averaging. A previously validated tracking algorithm was employed for auto-tracking. The difference between the programmed and auto-tracked positions of a Las Vegas phantom moving in the superior-inferior direction defined the tracking error (δ). Motion blurring was assessed by measuring the area change of the circle with the greatest depth. Additionally, lung tumors on 1747 frames acquired at eleven field angles from four radiotherapy patients are manually and automatically tracked with varying frame averaging. δ was defined by the position difference of the two tracking methods. Image noise was defined as the standard deviation of the background intensity. Motion blurring and image noise were correlated with δ using Pearson correlation coefficient (R). Results: For both phantom and patient studies, the auto-tracking errors increased at frame rates lower than 4.29Hz. Above 4.29Hz, changes in errors were negligible with δ<1.60mm. Motion blurring and image noise were observed to increase and decrease with frame averaging, respectively. Motion blurring and tracking errors were significantly correlated for the phantom (R=0.94) and patient studies (R=0.72). Moderate to poor correlation was found between image noise and tracking error with R -0.58 and -0.19 for both studies, respectively. Conclusion: An image acquisition frame rate of at least 4.29Hz is recommended for cine EPID tracking. Motion blurring in images with frame rates below 4.39Hz can substantially reduce the accuracy of auto-tracking. This work is supported in part by the Varian Medical Systems, Inc.

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SU-E-J-175: Proton Dose Calculation On Scatter-Corrected CBCT Image: Feasibility Study for Adaptive Proton Therapy

Purpose: To demonstrate feasibility of proton dose calculation on scattercorrected CBCT images for the purpose of adaptive proton therapy. Methods: Two CBCT image sets were acquired from a prostate cancer patient and a thorax phantom using an on-board imaging system of an Elekta infinity linear accelerator. 2-D scatter maps were estimated using a previously introduced CT-based technique, and were subtracted from each raw projection image. A CBCT image set was then reconstructed with an open source reconstruction toolkit (RTK). Conversion from the CBCT number to HU was performed by soft tissue-based shifting with reference to the plan CT. Passively scattered proton plans were simulated on the plan CT and corrected/uncorrected CBCT images using the XiO treatment planning system. For quantitative evaluation, water equivalent path length (WEPL) was compared in those treatment plans. Results: The scatter correction method significantly improved image quality and HU accuracy in the prostate case where large scatter artifacts were obvious. However, the correction technique showed limited effects on the thorax case that was associated with fewer scatter artifacts. Mean absolute WEPL errors from the plans with the uncorrected and corrected images were 1.3 mm and 5.1 mm in the thorax case and 13.5 mm and 3.1 mm in the prostate case. The prostate plan dose distribution of the corrected image demonstrated better agreement with the reference one than that of the uncorrected image. Conclusion: A priori CT-based CBCT scatter correction can reduce the proton dose calculation error when large scatter artifacts are involved. If scatter artifacts are low, an uncorrected CBCT image is also promising for proton dose calculation when it is calibrated with the soft-tissue based shifting.

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Online updating of context-aware landmark detectors for prostate localization in daily treatment CT images

Purpose: In image guided radiation therapy, it is crucial to fast and accurately localize the prostate in the daily treatment images. To this end, the authors propose an online update scheme for landmark-guided prostate segmentation, which can fully exploit valuable patient-specific information contained in the previous treatment images and can achieve improved performance in landmark detection and prostate segmentation. Methods: To localize the prostate in the daily treatment images, the authors first automatically detect six anatomical landmarks on the prostate boundary by adopting a context-aware landmark detection method. Specifically, in this method, a two-layer regression forest is trained as a detector for each target landmark. Once all the newly detected landmarks from new treatment images are reviewed or adjusted (if necessary) by clinicians, they are further included into the training pool as new patient-specific information to update all the two-layer regression forests for the next treatment day. As more and more treatment images of the current patient are acquired, the two-layer regression forests can be continually updated by incorporating the patient-specific information into the training procedure. After all target landmarks are detected, a multiatlas random sample consensus (multiatlas RANSAC) method is used to segment the entire prostate by fusing multiple previously segmented prostates of the current patient after they are aligned to the current treatment image. Subsequently, the segmented prostate of the current treatment image is again reviewed (or even adjusted if needed) by clinicians before including it as a new shape example into the prostate shape dataset for helping localize the entire prostate in the next treatment image. Results: The experimental results on 330 images of 24 patients show the effectiveness of the authors’ proposed online update scheme in improving the accuracies of both landmark detection and prostate segmentation. Besides, compared to the other state-of-the-art prostate segmentation methods, the authors’ method achieves the best performance. Conclusions: By appropriate use of valuable patient-specific information contained in the previous treatment images, the authors’ proposed online update scheme can obtain satisfactory results for both landmark detection and prostate segmentation.

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Proton dose calculation on scatter-corrected CBCT image: Feasibility study for adaptive proton therapy

Purpose: To demonstrate the feasibility of proton dose calculation on scatter-corrected cone-beam computed tomographic (CBCT) images for the purpose of adaptive proton therapy. Methods: CBCT projection images were acquired from anthropomorphic phantoms and a prostate patient using an on-board imaging system of an Elekta infinity linear accelerator. Two previously introduced techniques were used to correct the scattered x-rays in the raw projection images: uniform scatter correction (CBCT{sub us}) and a priori CT-based scatter correction (CBCT{sub ap}). CBCT images were reconstructed using a standard FDK algorithm and GPU-based reconstruction toolkit. Soft tissue ROI-based HU shifting was used to improve HU accuracy of the uncorrected CBCT images and CBCT{sub us}, while no HU change was applied to the CBCT{sub ap}. The degree of equivalence of the corrected CBCT images with respect to the reference CT image (CT{sub ref}) was evaluated by using angular profiles of water equivalent path length (WEPL) and passively scattered proton treatment plans. The CBCT{sub ap} was further evaluated in more realistic scenarios such as rectal filling and weight loss to assess the effect of mismatched prior information on the corrected images. Results: The uncorrected CBCT and CBCT{sub us} images demonstrated substantial WEPL discrepancies (7.3 ± 5.3 mm and 11.1 ± 6.6 mm, respectively) with respect to the CT{sub ref}, while the CBCT{sub ap} images showed substantially reduced WEPL errors (2.4 ± 2.0 mm). Similarly, the CBCT{sub ap}-based treatment plans demonstrated a high pass rate (96.0% ± 2.5% in 2 mm/2% criteria) in a 3D gamma analysis. Conclusions: A priori CT-based scatter correction technique was shown to be promising for adaptive proton therapy, as it achieved equivalent proton dose distributions and water equivalent path lengths compared to those of a reference CT in a selection of anthropomorphic phantoms.

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SU-E-I-75: Development of New Biological Fingerprints for Patient Recognition to Identify Misfiled Images in a PACS Server

Purpose: We are trying to develop an image-searching technique to identify misfiled images in a picture archiving and communication system (PACS) server by using five biological fingerprints: the whole lung field, cardiac shadow, superior mediastinum, lung apex, and right lower lung. Each biological fingerprint in a chest radiograph includes distinctive anatomical structures to identify misfiled images. The whole lung field was less effective for evaluating the similarity between two images than the other biological fingerprints. This was mainly due to the variation in the positioning for chest radiographs. The purpose of this study is to develop new biological fingerprints that could reduce influence of differences in the positioning for chest radiography. Methods: Two hundred patients were selected randomly from our database (36,212 patients). These patients had two images each (current and previous images). Current images were used as the misfiled images in this study. A circumscribed rectangular area of the lung and the upper half of the rectangle were selected automatically as new biological fingerprints. These biological fingerprints were matched to all previous images in the database. The degrees of similarity between the two images were calculated for the same and different patients. The usefulness of new the biological fingerprints for automated patient recognition was examined in terms of receiver operating characteristic (ROC) analysis. Results: Area under the ROC curves (AUCs) for the circumscribed rectangle of the lung, upper half of the rectangle, and whole lung field were 0.980, 0.994, and 0.950, respectively. The new biological fingerprints showed better performance in identifying the patients correctly than the whole lung field. Conclusion: We have developed new biological fingerprints: circumscribed rectangle of the lung and upper half of the rectangle. These new biological fingerprints would be useful for automated patient identification system because they are less affected by positioning differences during imaging.

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SU-E-J-17: Intra-Fractional Prostate Movement Correction During Treatment Delivery Period for Prostate Cancer Using the Intra-Fractional Orthogonal KV-MV Image Pairs

Purpose: To evaluate the intra-fractional prostate movement range during the beam delivery and implement new IGRT method to correct the prostate movement during the hypofractionated prostate treatment delivery. Methods: To evaluate the prostate internal motion range during the beam delivery, 11 conventional treatments were utilized. Two-arc RapidArc plans were used for the treatment delivery. Orthogonal KV imaging is performed in the middle of the treatment to correct intra-fractional prostate movement. However, it takes gantry-mounted on-board imaging system relative long time to finish the orthogonal KV imaging because of gantry rotation. To avoid gantry movement and accelerate the IGRT processing time, orthogonal KV-MV image pair is tested using the OBI daily QA Cube phantom. Results: The average prostate movement between two orthogonal KV image pairs was 0.38cm (0.20cm ∼ 0.85cm). And the interval time between them was 6.71 min (4.64min ∼ 9.22 min). 2-arc beam delivery time is within 3 minutes for conventional RapidArc treatment delivery. Hypofractionated treatment or SBRT need 4 partial arc and possible non-coplanar technology, which need much longer beam delivery time. Therefore prostate movement might be larger. New orthogonal KV-MV image pair is a new method to correct the prostate movement in the middle of the beam delivery if real time tracking method is not available. Orthogonal KV-MV image pair doesn’t need gantry rotation. Images were acquired quickly which minimized possible new prostate movement. Therefore orthogonal KV-MV image pair is feasible for IGRT. Conclusion: Hypofractionated prostate treatment with less PTV margin always needs longer beam delivery time. Therefore prostate movement correction during the treatment delivery is critical. Orthogonal KV-MV imaging pair is efficient and accurate to correct the prostate movement during treatment beam delivery. Due to limited fraction number and high dose per fraction, the MV imaging dose is negligible.

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SU-E-J-42: Evaluation of Fiducial Markers for Ultrasound and X-Ray Images Used for Motion Tracking in Pancreas SBRT

Purpose Ultrasound tracking of target motion relies on visibility of vascular and/or anatomical landmark. However this is challenging when the target is located far from vascular structures or in organs that lack ultrasound landmark structure, such as in the case of pancreas cancer. The purpose of this study is to evaluate visibility, artifacts and distortions of fusion coils and solid gold markers in ultrasound, CT, CBCT and kV images to identify markers suitable for real-time ultrasound tracking of tumor motion in SBRT pancreas treatment. Methods Two fusion coils (1mm × 5mm and 1mm × 10 mm) and a solid gold marker (0.8mm × 10mm) were embedded in a tissue–like ultrasound phantom. The phantom (5cm × 12cm × 20cm) was prepared using water, gelatin and psyllium-hydrophilic-mucilloid fiber. Psylliumhydrophilic mucilloid acts as scattering medium to produce echo texture that simulates sonographic appearance of human tissue in ultrasound images while maintaining electron density close to that of water in CT images. Ultrasound images were acquired using 3D-ultrasound system with markers embedded at 5, 10 and 15mm depth from phantom surface. CT images were acquired using Philips Big Bore CT while CBCT and kV images were acquired with XVI-system (Elexta). Visual analysis was performed to compare visibility of the markers and visibility score (1 to 3) were assigned. Results All markers embedded at various depths are clearly visible (score of 3) in ultrasound images. Good visibility of all markers is observed in CT, CBCT and kV images. The degree of artifact produced by the markers in CT and CBCT images are indistinguishable. No distortion is observed in images from any modalities. Conclusion All markers are visible in images across all modalities in this homogenous tissue-like phantom. Human subject data is necessary to confirm the marker type suitable for real-time ultrasound tracking of tumor motion in SBRT pancreas treatment.

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SU-E-J-48: Imaging Origin-Radiation Isocenter Coincidence for Linac-Based SRS with Novalis Tx

Purpose To implement and evaluate an image-based Winston-Lutz (WL) test to measure the displacement between ExacTrac imaging origin and radiation isocenter on a Novalis Tx system using RIT V6.2 software analysis tools. Displacement between imaging and radiation isocenters was tracked over time. The method was applied for cone-based and MLC-based WL tests. Methods The Brainlab Winston-Lutz phantom was aligned to room lasers. The ExacTrac imaging system was then used to detect the Winston- Lutz phantom and obtain the displacement between the center of the phantom and the imaging origin. EPID images of the phantom were obtained at various gantry and couch angles and analyzed with RIT calculating the phantom center to radiation isocenter displacement. The RIT and Exactrac displacements were combined to calculate the displacement between imaging origin and radiation isocenter. Results were tracked over time. Results Mean displacements between ExacTrac origin and radiation isocenter were: VRT: −0.1mm ± 0.3mm, LNG: 0.5mm ± 0.2mm, LAT: 0.2mm ± 0.2mm (vector magnitude of 0.7 ± 0.2mm). Radiation isocenter was characterized by the mean of the standard deviations of the WL phantom displacements: σVRT: 0.2mm, σLNG: 0.4mm, σLAT: 0.6mm. The linac couch base was serviced to reduce couch walkout. This reduced σLAT to 0.2mm. These measurements established a new baseline of radiation isocenter-imaging origin coincidence. Conclusion The image-based WL test has ensured submillimeter localization accuracy using the ExacTrac imaging system. Standard deviations of ExacTrac-radiation isocenter displacements indicate that average agreement within 0.3mm is possible in each axis. This WL test is a departure from the tradiational WL in that imaging origin/radiation isocenter agreement is the end goal not lasers/radiation isocenter.

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SU-E-J-97: Quality Assurance of Deformable Image Registration Algorithms: How Realistic Should Phantoms Be?

Purpose: Deformable image registration (DIR) has widespread uses in radiotherapy for applications such as dose accumulation studies, multi-modality image fusion, and organ segmentation. The quality assurance (QA) of such algorithms, however, remains largely unimplemented. This work aims to determine how detailed a physical phantom needs to be to accurately perform QA of a DIR algorithm. Methods: Virtual prostate and head-and-neck phantoms, made from patient images, were used for this study. Both sets consist of an undeformed and deformed image pair. The images were processed to create additional image pairs with one through five homogeneous tissue levels using Otsu’s method. Realistic noise was then added to each image. The DIR algorithms from MIM and Velocity (Deformable Multipass) were applied to the original phantom images and the processed ones. The resulting deformations were then compared to the known warping. A higher number of tissue levels creates more contrast in an image and enables DIR algorithms to produce more accurate results. For this reason, error (distance between predicted and known deformation) is utilized as a metric to evaluate how many levels are required for a phantom to be a realistic patient proxy. Results: For the prostate image pairs, the mean error decreased from 1–2 tissue levels and remained constant for 3+ levels. The mean error reduction was 39% and 26% for Velocity and MIM respectively. For head and neck, mean error fell similarly through 2 levels and flattened with total reduction of 16% and 49% for Velocity and MIM. For Velocity, 3+ levels produced comparable accuracy as the actual patient images, whereas MIM showed further accuracy improvement. Conclusion: The number of tissue levels needed to produce an accurate patient proxy depends on the algorithm. For Velocity, three levels were enough, whereas five was still insufficient for MIM.

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SU-E-T-582: On-Line Dosimetric Verification of Respiratory Gated Volumetric Modulated Arc Therapy Using the Electronic Portal Imaging Device

Purpose: To investigate the clinical utility of on-line verification of respiratory gated VMAT dosimetry during treatment. Methods: Portal dose images were acquired during treatment in integrated mode on a Varian TrueBeam (v. 1.6) linear accelerator for gated lung and liver patients that used flattening filtered beams. The source to imager distance (SID) was set to 160 cm to ensure imager clearance in case the isocenter was off midline. Note that acquisition of integrated images resulted in no extra dose to the patient. Fraction 1 was taken as baseline and all portal dose images were compared to that of the baseline, where the gamma comparison and dose difference were used to measure day-to-day exit dose variation. All images were analyzed in the Portal Dosimetry module of Aria (v. 10). The portal imager on the TrueBeam was calibrated by following the instructions for dosimetry calibration in service mode, where we define 1 calibrated unit (CU) equal to 1 Gy for 10×10 cm field size at 100 cm SID. This reference condition was measured frequently to verify imager calibration. Results: The gamma value (3%, 3 mm, 5% threshold) ranged between 92% and 100% for the lung and liver cases studied. The exit dose can vary by as much as 10% of the maximum dose for an individual fraction. The integrated images combined with the information given by the corresponding on-line soft tissue matched cone-beam computed tomography (CBCT) images were useful in explaining dose variation. For gated lung treatment, dose variation was mainly due to the diaphragm position. For gated liver treatment, the dose variation was due to both diaphragm position and weight loss. Conclusion: Integrated images can be useful in verifying dose delivery consistency during respiratory gated VMAT, although the CBCT information is needed to explain dose differences due to anatomical changes.

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SU-E-T-775: Use of Electronic Portal Imaging Device (EPID) for Quality Assurance (QA) of Electron Beams On Varian Truebeam System

Purpose: In a previous study we have demonstrated the feasibility of using EPID to QA electron beam parameters on a single Varian TrueBeam LINAC. This study aims to provide further investigation on (1) reproducibility of using EPID to detect electron beam energy changes on multiple machines and (2) evaluation of appropriate calibration methods to compare results from different EPIDs. Methods: Ad-hoc mode electron beam images were acquired in developer mode with XML code. Electron beam data were collected on a total of six machines from four institutions. A custom-designed double-wedge phantom was placed on the EPID detector. Two calibration methods - Pixel Sensitivity Map (PSM) and Large Source-to-Imager Distance Flood Field (LSID-FF) - were used. To test the sensitivity of EPID in detecting energy drifts, Bending Magnet Current (BMC) was detuned to invoke energy changes corresponding to ∼±1.5 mm change in R50% of PDD on two machines from two institutions. Percent depth ionization (PDI) curves were then analyzed and compared with the respective baseline images using LSID-FF calibration. For reproducibility testing, open field EPID images and images with a standard testing phantom were collected on multiple machines. Images with and without PSM correction for same energies on different machines were overlaid and compared. Results: Two pixel shifts were observed in PDI curve when energy changes exceeded the TG142 tolerance. PSM showed the potential to correct the differences in pixel response of different imagers. With PSM correction, the histogram of images differences obtained from different machines showed narrower distributions than those images without PSM correction. Conclusion: EPID is sensitive for electron energy changes and the results are reproducible on different machines. When overlaying images from different machines, PSM showed the ability to partially eliminate the intrinsic variation of various imagers. Research Funding from Varian Medical Systems Inc.Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.

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SU-F-207-02: Use of Postmortem Subjects for Subjective Image Quality Assessment in Abdominal CT Protocols with Iterative Reconstruction

Purpose: New radiation dose reduction technologies are emerging constantly in the medical imaging field. The latest of these technologies, iterative reconstruction (IR) in CT, presents the ability to reduce dose significantly and hence provides great opportunity for CT protocol optimization. However, without effective analysis of image quality, the reduction in radiation exposure becomes irrelevant. This work explores the use of postmortem subjects as an image quality assessment medium for protocol optimizations in abdominal CT. Methods: Three female postmortem subjects were scanned using the Abdomen-Pelvis (AP) protocol at reduced minimum tube current and target noise index (SD) settings of 12.5, 17.5, 20.0, and 25.0. Images were reconstructed using two strengths of iterative reconstruction. Radiologists and radiology residents from several subspecialties were asked to evaluate 8 AP image sets including the current facility default scan protocol and 7 scans with the parameters varied as listed above. Images were viewed in the soft tissue window and scored on a 3-point scale as acceptable, borderline acceptable, and unacceptable for diagnosis. The facility default AP scan was identified to the reviewer while the 7 remaining AP scans were randomized and de-identified of acquisition and reconstruction details. The observers were also asked to comment on the subjective image quality criteria they used for scoring images. This included visibility of specific anatomical structures and tissue textures. Results: Radiologists scored images as acceptable or borderline acceptable for target noise index settings of up to 20. Due to the postmortem subjects’ close representation of living human anatomy, readers were able to evaluate images as they would those of actual patients. Conclusion: Postmortem subjects have already been proven useful for direct CT organ dose measurements. This work illustrates the validity of their use for the crucial evaluation of image quality during CT protocol optimization, especially when investigating the effects of new technologies.

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TU-CD-207-12: Impact of Anatomical Noise On Detection Performance of Microcalcifications in Multi-Contrast Breast Imaging

Purpose: The anatomical noise power spectra (NPS) for differential phase contrast (DPC) and dark field (DF) imaging have recently been characterized using a power-law model with two parameters, alpha and beta, an innovative extension to the methodology used in x-ray attenuation based breast imaging such as mammography, DBT, or cone-beam CT. Beta values of 3.6, 2.6, and 1.3 have been measured for absorption, DPC, and DF respectively for cadaver breasts imaged in the coronal plane; these dramatic differences should be reflected in their detection performance. The purpose of this study was to determine the impact of anatomical noise on breast calcification detection and compare the detection performance of the three contrast mechanisms of a multi-contrast x-ray imaging system. Methods: In our studies, a calcification image object was segmented out of the multi-contrast images of a cadaver breast specimen. 50 measured total NPS were measured from breast cadavers directly. The ideal model observer detectability was calculated for a range of doses (5–100%) and a range of calcification sizes (diameter = 0.25–2.5 mm). Results: Overall we found the highest average detectability corresponded to DPC imaging (7.4 for 1 mm calc.), with DF the next highest (3.8 for 1 mm calc.), and absorption the lowest (3.2 for 1 mm calc.). However, absorption imaging also showed the slowest dependence on dose of the three modalities due to the significant anatomical noise. DPC showed a peak detectability for calcifications ∼1.25 mm in diameter, DF showed a peak for calcifications around 0.75 mm in diameter, and absorption imaging had no such peak in the range explored. Conclusion: Understanding imaging performance for DPC and DF is critical to transition these modalities to the clinic. The results presented here offer new insight into how these modalities complement absorption imaging to maximize the likelihood of detecting early breast cancers. J. Garrett, Y. Ge, K. Li: Nothing to disclose. G.-H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX.

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