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DOE OSTI2016

Improved frame-based estimation of head motion in PET brain imaging

Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Design and assessment of a novel SPECT system for desktop open-gantry imaging of small animals: A simulation study

Purpose: Given increasing efforts in biomedical research utilizing molecular imaging methods, development of dedicated high-performance small-animal SPECT systems has been growing rapidly in the last decade. In the present work, we propose and assess an alternative concept for SPECT imaging enabling desktop open-gantry imaging of small animals. Methods: The system, PERSPECT, consists of an imaging desk, with a set of tilted detector and pinhole collimator placed beneath it. The object to be imaged is simply placed on the desk. Monte Carlo (MC) and analytical simulations were utilized to accurately model and evaluate the proposed concept and design. Furthermore, a dedicated image reconstruction algorithm, finite-aperture-based circular projections (FABCP), was developed and validated for the system, enabling more accurate modeling of the system and higher quality reconstructed images. Image quality was quantified as a function of different tilt angles in the acquisition and number of iterations in the reconstruction algorithm. Furthermore, more complex phantoms including Derenzo, Defrise, and mouse whole body were simulated and studied. Results: The sensitivity of the PERSPECT was 207 cps/MBq. It was quantitatively demonstrated that for a tilt angle of 30°, comparable image qualities were obtained in terms of normalized squared error, contrast, uniformity, noise, and spatial resolution measurements, the latter at ∼0.6 mm. Furthermore, quantitative analyses demonstrated that 3 iterations of FABCP image reconstruction (16 subsets/iteration) led to optimally reconstructed images. Conclusions: The PERSPECT, using a novel imaging protocol, can achieve comparable image quality performance in comparison with a conventional pinhole SPECT with the same configuration. The dedicated FABCP algorithm, which was developed for reconstruction of data from the PERSPECT system, can produce high quality images for small-animal imaging via accurate modeling of the system as incorporated in the forward- and back-projection steps. Meanwhile, the developed MC model and the analytical simulator of the system can be applied for further studies on development and evaluation of the system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

Purpose: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). Methods: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high- and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. Results: On the line-pair slice of the Catphan{sup ©}600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise standard deviation (STD). Similar performance on spatial resolution is observed on an anthropomorphic head phantom. In addition, results of PWLS-SBR show substantially improved image quality due to preservation of image NPS. On the Catphan{sup ©}600 phantom, NPS using PWLS-SBR has a correlation of 93% with that via direct matrix inversion, while the correlation drops to −52% for PWLS-EPR. Electron density measurement studies indicate high accuracy of PWLS-SBR. On seven different materials, the measured electron densities calculated from the decomposed material images using PWLS-SBR have a root-mean-square error (RMSE) of 1.20%, while the results of PWLS-EPR have a RMSE of 2.21%. In the study on a head-and-neck patient, PWLS-SBR is shown to reduce noise STD by a factor of 3 on material images with image qualities comparable to CT images, whereas fine structures are lost in the PWLS-EPR result. Additionally, PWLS-SBR better preserves low contrast on the tissue image. Conclusions: The authors propose improvements to the regularization term of an optimization framework which performs iterative image-domain decomposition for DECT with noise suppression. The regularization term avoids calculation of image gradient and is based on pixel similarity. The proposed method not only achieves a high decomposition accuracy, but also improves over the previous algorithm on NPS as well as spatial resolution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-BRB-05: Determining the Adequacy of Auto-Contouring Via Probabilistic Assessment of Ensuing Treatment Plan Metrics in Comparison with Manual Contours

Purpose: To determine if auto-contour and manual-contour—based plans differ when evaluated with respect to probabilistic coverage metrics and biological model endpoints for prostate IMRT. Methods: Manual and auto-contours were created for 149 CT image sets acquired from 16 unique prostate patients. A single physician manually contoured all images. Auto-contouring was completed utilizing Pinnacle’s Smart Probabilistic Image Contouring Engine (SPICE). For each CT, three different 78 Gy/39 fraction 7-beam IMRT plans are created; PD with drawn ROIs, PAS with auto-contoured ROIs, and PM with auto-contoured OARs with the manually drawn target. For each plan, 1000 virtual treatment simulations with different sampled systematic errors for each simulation and a different sampled random error for each fraction were performed using our in-house GPU-accelerated robustness analyzer tool which reports the statistical probability of achieving dose-volume metrics, NTCP, TCP, and the probability of achieving the optimization criteria for both auto-contoured (AS) and manually drawn (D) ROIs. Metrics are reported for all possible cross-evaluation pairs of ROI types (AS,D) and planning scenarios (PD,PAS,PM). Bhattacharyya coefficient (BC) is calculated to measure the PDF similarities for the dose-volume metric, NTCP, TCP, and objectives with respect to the manually drawn contour evaluated on base plan (D-PD). Results: We observe high BC values (BC≥0.94) for all OAR objectives. BC values of max dose objective on CTV also signify high resemblance (BC≥0.93) between the distributions. On the other hand, BC values for CTV’s D95 and Dmin objectives are small for AS-PM, AS-PD. NTCP distributions are similar across all evaluation pairs, while TCP distributions of AS-PM, AS-PD sustain variations up to %6 compared to other evaluated pairs. Conclusion: No significant probabilistic differences are observed in the metrics when auto-contoured OARs are used. The prostate auto-contour needs improvement to achieve clinically equivalent plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature B is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-202-05: Pilot Study of Online Treatment Evaluation and Adaptive Re-Planning for Laryngeal SBRT

Purpose: We have instigated a phase I trial of 5-fraction stereotactic body radiotherapy (SBRT) for advanced-stage laryngeal cancer. We conducted this pilot dosimetric study to confirm the potential utility of online adaptive re-planning to preserve treatment quality. Methods: Ten cases of larynx cancer were evaluated. Baseline and daily SBRT treatment plans were generated per trial protocol. Daily volumetric images were acquired prior to every fraction of treatment. Reference simulation CT images were deformably registered to daily volumetric images using Eclipse. Planning contours were then deformably propagated to daily images. Reference SBRT plans were directly copied to calculate delivered dose distributions on deformed reference CT images. In-house software platform has been developed to calculate cumulative dose over a course of treatment in four steps: 1) deforming delivered dose grid to reference CT images using deformation information exported from Eclipse; 2) generating tetrahedrons using deformed dose grid as vertices; 3) resampling dose to a high resolution within every tetrahedron; 4) calculating dose-volume histograms. Our inhouse software was benchmarked with a commercial software, Mirada. Results: In all ten cases including 49 fractions of treatments, delivered daily doses were completely evaluated and treatment could be re-planned within 10 minutes. Prescription dose coverage of PTV was less than intended in 53% of fractions of treatment (mean: 94%, range: 84%–98%) while minimum coverage of CTV and GTV was 94% and 97%, respectively. Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases. Conclusion: Although GTV and CTV coverage is preserved by in-room 3D image guidance of larynx SBRT, PTV coverage can vary significantly from intended plans. Online adaptive treatment evaluation and re-planning is potentially necessary and our procedure is clinically applicable to fully preserve treatment quality. This project is supported by CPRIT Individual Investigator Research Award RP150386.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-206-03: Metal Artifact Reduction in X-Ray Computed Tomography Based On Local Anatomical Similarity

Purpose: Metal implants such as orthopedic hardware and dental fillings cause severe bright and dark streaking in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. Additionally, such artifacts negatively impact patient set-up in image guided radiation therapy (IGRT). In this work, we propose a novel method for metal artifact reduction which utilizes the anatomical similarity between neighboring CT slices. Methods: Neighboring CT slices show similar anatomy. Based on this anatomical similarity, the proposed method replaces corrupted CT pixels with pixels from adjacent, artifact-free slices. A gamma map, which is the weighted summation of relative HU error and distance error, is calculated for each pixel in the artifact-corrupted CT image. The minimum value in each pixel’s gamma map is used to identify a pixel from the adjacent CT slice to replace the corresponding artifact-corrupted pixel. This replacement only occurs if the minimum value in a particular pixel’s gamma map is larger than a threshold. The proposed method was evaluated with clinical images. Results: Highly attenuating dental fillings and hip implants cause severe streaking artifacts on CT images. The proposed method eliminates the dark and bright streaking and improves the implant delineation and visibility. In particular, the image non-uniformity in the central region of interest was reduced from 1.88 and 1.01 to 0.28 and 0.35, respectively. Further, the mean CT HU error was reduced from 328 HU and 460 HU to 60 HU and 36 HU, respectively. Conclusions: The proposed metal artifact reduction method replaces corrupted image pixels with pixels from neighboring slices that are free of metal artifacts. This method proved capable of suppressing streaking artifacts, improving HU accuracy and image detectability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-209-06: Improving X-Ray Imaging with Computer Vision and Augmented Reality

Purpose: To determine the feasibility of using a computer vision algorithm and augmented reality interface to reduce repeat rates and improve consistency of image quality and patient exposure in general radiography. Methods: A prototype device, designed for use with commercially available hardware (Microsoft Kinect 2.0) capable of depth sensing and high resolution/frame rate video, was mounted to the x-ray tube housing as part of a Philips DigitalDiagnost digital radiography room. Depth data and video was streamed to a Windows 10 PC. Proprietary software created an augmented reality interface where overlays displayed selectable information projected over real-time video of the patient. The information displayed prior to and during x-ray acquisition included: recognition and position of ordered body part, position of image receptor, thickness of anatomy, location of AEC cells, collimated x-ray field, degree of patient motion and suggested x-ray technique. Pre-clinical data was collected in a volunteer study to validate patient thickness measurements and x-ray images were not acquired. Results: Proprietary software correctly identified ordered body part, measured patient motion, and calculated thickness of anatomy. Pre-clinical data demonstrated accuracy and precision of body part thickness measurement when compared with other methods (e.g. laser measurement tool). Thickness measurements provided the basis for developing a database of thickness-based technique charts that can be automatically displayed to the technologist. Conclusion: The utilization of computer vision and commercial hardware to create an augmented reality view of the patient and imaging equipment has the potential to drastically improve the quality and safety of x-ray imaging by reducing repeats and optimizing technique based on patient thickness. Society of Pediatric Radiology Pilot Grant; Washington University Bear Cub Fund.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-BRA-03: Analysis of Systematic Errors with 2D/3D Image Registration for Target Localization and Treatment Delivery in Stereotactic Radiosurgery

Purpose: Determine systematic deviations between 2D/3D and 3D/3D image registrations with six degrees of freedom (6DOF) for various imaging modalities and registration algorithms on the Varian Edge Linac. Methods: The 6DOF systematic errors were assessed by comparing automated 2D/3D (kV/MV vs. CT) with 3D/3D (CBCT vs. CT) image registrations from different imaging pairs, CT slice thicknesses, couch angles, similarity measures, etc., using a Rando head and a pelvic phantom. The 2D/3D image registration accuracy was evaluated at different treatment sites (intra-cranial and extra-cranial) by statistically analyzing 2D/3D pre-treatment verification against 3D/3D localization of 192 Stereotactic Radiosurgery/Stereotactic Body Radiation Therapy treatment fractions for 88 patients. Results: The systematic errors of 2D/3D image registration using kV-kV, MV-kV and MV-MV image pairs using 0.8 mm slice thickness CT images were within 0.3 mm and 0.3° for translations and rotations with a 95% confidence interval (CI). No significant difference between 2D/3D and 3D/3D image registrations (P>0.05) was observed for target localization at various CT slice thicknesses ranging from 0.8 to 3 mm. Couch angles (30, 45, 60 degree) did not impact the accuracy of 2D/3D image registration. Using pattern intensity with content image filtering was recommended for 2D/3D image registration to achieve the best accuracy. For the patient study, translational error was within 2 mm and rotational error was within 0.6 degrees in terms of 95% CI for 2D/3D image registration. For intra-cranial sites, means and std. deviations of translational errors were −0.2±0.7, 0.04±0.5, 0.1±0.4 mm for LNG, LAT, VRT directions, respectively. For extra-cranial sites, means and std. deviations of translational errors were - 0.04±1, 0.2±1, 0.1±1 mm for LNG, LAT, VRT directions, respectively. 2D/3D image registration uncertainties for intra-cranial and extra-cranial sites were comparable. Conclusion: The Varian Edge radiosurgery 6DOF-based system, can perform 2D/3D image registration with high accuracy for target localization in image-guided stereotactic radiosurgery. The work was supported by a Research Scholar Grant, RSG-15-137-01-CCE from the American Cancer Society.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-202-04: Validation of Deformable Image Registration Algorithms for Head and Neck Adaptive Radiotherapy in Routine Clinical Setting

Purpose: To evaluate the ROI contours and accumulated dose difference using different deformable image registration (DIR) algorithms for head and neck (H&N) adaptive radiotherapy. Methods: Eight H&N cancer patients were randomly selected from the affiliated hospital. During the treatment, patients were rescanned every week with ROIs well delineated by radiation oncologist on each weekly CT. New weekly treatment plans were also re-designed with consistent dose prescription on the rescanned CT and executed for one week on Siemens CT-on-rails accelerator. At the end, we got six weekly CT scans from CT1 to CT6 including six weekly treatment plans for each patient. The primary CT1 was set as the reference CT for DIR proceeding with the left five weekly CTs using ANACONDA and MORFEUS algorithms separately in RayStation and the external skin ROI was set to be the controlling ROI both. The entire calculated weekly dose were deformed and accumulated on corresponding reference CT1 according to the deformation vector field (DVFs) generated by the two different DIR algorithms respectively. Thus we got both the ANACONDA-based and MORFEUS-based accumulated total dose on CT1 for each patient. At the same time, we mapped the ROIs on CT1 to generate the corresponding ROIs on CT6 using ANACONDA and MORFEUS DIR algorithms. DICE coefficients between the DIR deformed and radiation oncologist delineated ROIs on CT6 were calculated. Results: For DIR accumulated dose, PTV D95 and Left-Eyeball Dmax show significant differences with 67.13 cGy and 109.29 cGy respectively (Table1). For DIR mapped ROIs, PTV, Spinal cord and Left-Optic nerve show difference with −0.025, −0.127 and −0.124 (Table2). Conclusion: Even two excellent DIR algorithms can give divergent results for ROI deformation and dose accumulation. As more and more TPS get DIR module integrated, there is an urgent need to realize the potential risk using DIR in clinical.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-207B-01: Radiomics Feature Reproducibility From Repeat CT Scans of Patients with Rectal Cancer

Purpose: To evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer. Methods: 40 rectal cancer patients were enrolled in this study, each of whom underwent two CT scans within average 8.7 days (5 days to 17 days), before any treatment was delivered. The rectal gross tumor volume (GTV) was distinguished and segmented by an experienced oncologist in both CTs. Totally, more than 2000 radiomics features were defined in this study, which were divided into four groups (I: GLCM, II: GLRLM III: Wavelet GLCM and IV: Wavelet GLRLM). For each group, five types of features were extracted (Max slice: features from the largest slice of target images, Max value: features from all slices of target images and choose the maximum value, Min value: minimum value of features for all slices, Average value: average value of features for all slices, Matrix sum: all slices of target images translate into GLCM and GLRLM matrices and superpose all matrices, then extract features from the superposed matrix). Meanwhile a LOG (Laplace of Gauss) filter with different parameters was applied to these images. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) were calculated to assess the reproducibility. Results: 403 radiomics features were extracted from each type of patients’ medical images. Features of average type are the most reproducible. Different filters have little effect for radiomics features. For the average type features, 253 out of 403 features (62.8%) showed high reproducibility (ICC≥0.8), 133 out of 403 features (33.0%) showed medium reproducibility (0.8≥ICC≥0.5) and 17 out of 403 features (4.2%) showed low reproducibility (ICC≥0.5). Conclusion: The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-SPS-03: Direct Measurement of Organ Doses Resulting From Head and Cervical Spine Trauma CT Protocols

Purpose: This retrospective study analyzes the exposure history of emergency department (ED) patients undergoing head and cervical spine trauma computed tomography (CT) studies. This study investigated dose levels received by trauma patients and addressed any potential concerns regarding radiation dose issues. Methods: Under proper IRB approval, a cohort of 300 trauma cases of head and cervical spine trauma CT scans received in the ED was studied. The radiological image viewing software of the hospital was used to view patient images and image data. The following parameters were extracted: the imaging history of patients, the reported dose metrics from the scanner including the volumetric CT Dose Index (CTDIvol) and Dose Length Product (DLP). A postmortem subject was scanned using the same scan techniques utilized in a standard clinical head and cervical spine trauma CT protocol with 120 kVp and 280 mAs. The CTDIvol was recorded for the subject and the organ doses were measured using optically stimulated luminescent (OSL) dosimeters. Typical organ doses to the brain, thyroid, lens, salivary glands, and skin, based on the cadaver studies, were then calculated and reported for the cohort. Results: The CTDIvol reported by the CT scanner was 25.5 mGy for the postmortem subject. The average CTDIvol from the patient cohort was 34.1 mGy. From these metrics, typical average organ doses in mGy were found to be: Brain (44.57), Thyroid (33.40), Lens (82.45), Salivary Glands (61.29), Skin (47.50). The imaging history of the cohort showed that on average trauma patients received 26.1 scans over a lifetime. Conclusion: The average number of scans received on average by trauma ED patients shows that radiation doses in trauma patients may be a concern. Available dose tracking software would be helpful to track doses in trauma ED patients, highlighting the importance of minimizing unnecessary scans and keeping doses ALARA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-P-23: Setup Uncertainties for the Lung Stereotactic Body Radiation Therapy

Purpose: The Exactrack X-ray system with six degree-of-freedom (6DoF) adjustment ability can be used for setup of lung stereotactic body radiation therapy. The setup uncertainties from ExacTrack 6D system were analyzed. Methods: The Exactrack X-ray 6D image guided radiotherapy system is used in our clinic. The system is an integration of 2 subsystems: (1): an infrared based optical position system and (2) a radiography kV x-ray imaging system. The infrared system monitors reflective body markers on the patient’s skin to assistant in the initial setup. The radiographic kV devices were used for patient positions verification and adjustment. The position verification was made by fusing the radiographs with the digitally reconstructed radiograph (DRR) images generated by simulation CT images using 6DoF fusion algorithms. Those results were recorded in our system. Gaussian functions were used to fit the data. Results: For 37 lung SBRT patients, the image registration results for the initial setup by using surface markers and for the verifications, were measured. The results were analyzed for 143 treatments. The mean values for the lateral, longitudinal, vertical directions were 0.1, 0.3 and 0.3mm, respectively. The standard deviations for the lateral, longitudinal and vertical directions were 0.62, 0.78 and 0.75mm respectively. The mean values for the rotations around lateral, longitudinal and vertical directions were 0.1, 0.2 and 0.4 degrees respectively, with standard deviations of 0.36, 0.34, and 0.42 degrees. Conclusion: The setup uncertainties for the lung SBRT cases by using Exactrack 6D system were analyzed. The standard deviations of the setup errors were within 1mm for all three directions, and the standard deviations for rotations were within 0.5 degree.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-P-42: “To Navigate, Or Not to Navigate: HDR BT in Recurrent Spine Lesions”

Purpose: We compare the accuracy of HDR catheter placement for paraspinal lesions using O-arm CBCT imaging combined with StealthStation navigation and traditional fluoroscopically guided catheter placement. Methods: CT and MRI scans were acquired pre-treatment to outline the lesions and design treatment plans (pre-plans) to meet dosimetric constrains. The pre-planned catheter trajectories were transferred into the StealthStation Navigation system prior to the surgery. The StealthStation is an infra red (IR) optical navigation system used for guidance of surgical instruments. An intraoperative CBCT scan (O-arm) was acquired with reference IR optical fiducials anchored onto the patient and registered with the preplan image study to guide surgical instruments in relation to the patients’ anatomy and to place the brachytherapy catheters along the pre-planned trajectories. The final treatment plan was generated based on a 2nd intraoperative CBCT scan reflecting achieved implant geometry. The 2nd CBCT was later registered with the initial CT scan to compare the preplanned dwell positions with actual dwell positions (catheter placements). Similar workflow was used in placement of 8 catheters (1 patient) without navigation, but under fluoroscopy guidance in an interventional radiology suite. Results: A total of 18 catheters (3 patients) were placed using navigation assisted surgery. Average displacement of 0.66 cm (STD=0.37cm) was observed between the pre-plan source positions and actual source positions in the 3 dimensional space. This translates into an average 0.38 cm positioning error in one direction including registration errors, digitization errors, and the surgeons ability to follow the planned trajectory. In comparison, average displacement of non-navigated catheters was 0.50 cm (STD=0.22cm). Conclusion: Spinal lesion HDR brachytherapy planning is a difficult task. Catheter placement has a direct impact on target coverage and dose to critical structures. While limited to a handful of patients, our experience shows navigation and fluoroscopy guided placement yield similar results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-P-54: Guidelines to Check Image Registration QA of a Clinical Deformation Registration Software: A Single Institution Preliminary Study

Purpose: The objective of this study is to verify and analyze the accuracy of a clinical deformable image registration (DIR) software. Methods: To test clinical DIR software qualitatively and quantitatively, we focused on lung radiotherapy and analyzed a single (Lung) patient CT scan. Artificial anatomical changes were applied to account for daily variations during the course of treatment including the planning target volume (PTV) and organs at risk (OAR). The primary CT (pCT) and the structure set (pST) was deformed with commercial tool (ImSimQA-Oncology Systems Limited) and after artificial deformation (dCT and dST) sent to another commercial tool (VelocityAI-Varian Medical Systems). In Velocity, the deformed CT and structures (dCT and dST) were inversely deformed back to original primary CT (dbpCT and dbpST). We compared the dbpST and pST structure sets using similarity metrics. Furthermore, a binary deformation field vector (BDF) was created and sent to ImSimQA software for comparison with known “ground truth” deformation vector fields (DVF). Results: An image similarity comparison was made by using “ground truth” DVF and “deformed output” BDF with an output of normalized “cross correlation (CC)” and “mutual information (MI)” in ImSimQA software. Results for the lung case were MI=0.66 and CC=0.99. The artificial structure deformation in both pST and dbpST was analyzed using DICE coefficient, mean distance to conformity (MDC) and deformation field error volume histogram (DFEVH) by comparing them before and after inverse deformation. We have noticed inadequate structure match for CTV, ITV and PTV due to close proximity of heart and overall affected by lung expansion. Conclusion: We have seen similarity between pCT and dbpCT but not so well between pST and dbpST, because of inadequate structure deformation in clinical DIR system. This system based quality assurance test will prepare us for adopting the guidelines of upcoming AAPM task group 132 protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-18: Updates to the Computational Environment for Radiological Research for Image Analysis

Purpose: To present new tools in CERR for Texture Analysis and Visualization. Method: (1) Quantitative Image Analysis: We added the ability to compute Haralick texture features based on local neighbourhood. The Texture features depend on many parameters used in their derivation. For example: (a) directionality, (b) quantization of image, (c) patch-size for the neighborhood, (d) handling of the edge voxels within the region of interest, (e) Averaging co-occurance matrix vs texture features for different directions etc. A graphical user interface was built to set these parameters and then visualize their impact on the resulting texture maps. The entire functionality was written in Matlab. Array indexing was used to speed up the texture calculation. The computation speed is very competitive with the ITK library. Moreover, our implementation works with multiple CPUs and the computation time can be further reduced by using multiple processor threads. In order to reduce the Haralick texture maps into scalar features, we propose the use of Texture Volume Histograms. This lets users make use of the entire distribution of texture values within the region of interest rather than using just the mean and the standard deviations. (2) Qualitative/Visualization tools: The derived texture maps are stored as a new scan (derived) within CERR’s planC data structure. A display that compares various scans was built to show the raw image and the derived texture maps side-by-side. These images are positionally linked and can be navigated together. CERR’s graphics handling was updated and sped-up to be compatible with the newer Matlab versions. As a result, the users can use (a) different window levels and colormaps for different viewports, (b) click-and-drag or use mouse scroll-wheel to navigate slices. 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 and Outcomes modeling.

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