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DOE OSTI2016

SU-F-R-24: Identifying Prognostic Imaging Biomarkers in Early Stage Lung Cancer Using Radiomics

Purpose: Patients diagnosed with early stage lung cancer have favorable outcomes when treated with surgery or stereotactic radiotherapy. However, a significant proportion (∼20%) of patients will develop metastatic disease and eventually die of the disease. The purpose of this work is to identify quantitative imaging biomarkers from CT for predicting overall survival in early stage lung cancer. Methods: In this institutional review board-approved HIPPA-compliant retrospective study, we retrospectively analyzed the diagnostic CT scans of 110 patients with early stage lung cancer. Data from 70 patients were used for training/discovery purposes, while those of remaining 40 patients were used for independent validation. We extracted 191 radiomic features, including statistical, histogram, morphological, and texture features. Cox proportional hazard regression model, coupled with the least absolute shrinkage and selection operator (LASSO), was used to predict overall survival based on the radiomic features. Results: The optimal prognostic model included three image features from the Law’s feature and wavelet texture. In the discovery cohort, this model achieved a concordance index or CI=0.67, and it separated the low-risk from high-risk groups in predicting overall survival (hazard ratio=2.72, log-rank p=0.007). In the independent validation cohort, this radiomic signature achieved a CI=0.62, and significantly stratified the low-risk and high-risk groups in terms of overall survival (hazard ratio=2.20, log-rank p=0.042). Conclusion: We identified CT imaging characteristics associated with overall survival in early stage lung cancer. If prospectively validated, this could potentially help identify high-risk patients who might benefit from adjuvant systemic therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-32: Evaluation of MRI Acquisition Parameter Variations On Texture Feature Extraction Using ACR Phantom

Purpose: To investigate the sensitivity of classic texture features to variations of MRI acquisition parameters. Methods: This study was performed on American College of Radiology (ACR) MRI Accreditation Program Phantom. MR imaging was acquired on a GE 750 3T scanner with XRM explain gradient, employing a T1-weighted images (TR/TE=500/20ms) with the following parameters as the reference standard: number of signal average (NEX) = 1, matrix size = 256×256, flip angle = 90°, slice thickness = 5mm. The effect of the acquisition parameters on texture features with and without non-uniformity correction were investigated respectively, while all the other parameters were kept as reference standard. Protocol parameters were set as follows: (a). NEX = 0.5, 2 and 4; (b).Phase encoding steps = 128, 160 and 192; (c). Matrix size = 128×128, 192×192 and 512×512. 32 classic texture features were generated using the classic gray level run length matrix (GLRLM) and gray level co-occurrence matrix (GLCOM) from each image data set. Normalized range ((maximum-minimum)/mean) was calculated to determine variation among the scans with different protocol parameters. Results: For different NEX, 31 out of 32 texture features’ range are within 10%. For different phase encoding steps, 31 out of 32 texture features’ range are within 10%. For different acquisition matrix size without non-uniformity correction, 14 out of 32 texture features’ range are within 10%; for different acquisition matrix size with non-uniformity correction, 16 out of 32 texture features’ range are within 10%. Conclusion: Initial results indicated that those texture features that range within 10% are less sensitive to variations in T1-weighted MRI acquisition parameters. This might suggest that certain texture features might be more reliable to be used as potential biomarkers in MR quantitative image analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-33: Can CT and CBCT Be Used Simultaneously for Radiomics Analysis

Purpose: To investigate whether CBCT and CT can be used in radiomics analysis simultaneously. To establish a batch correction method for radiomics in two similar image modalities. Methods: Four sites including rectum, bladder, femoral head and lung were considered as region of interest (ROI) in this study. For each site, 10 treatment planning CT images were collected. And 10 CBCT images which came from same site of same patient were acquired at first radiotherapy fraction. 253 radiomics features, which were selected by our test-retest study at rectum cancer CT (ICC>0.8), were calculated for both CBCT and CT images in MATLAB. Simple scaling (z-score) and nonlinear correction methods were applied to the CBCT radiomics features. The Pearson Correlation Coefficient was calculated to analyze the correlation between radiomics features of CT and CBCT images before and after correction. Cluster analysis of mixed data (for each site, 5 CT and 5 CBCT data are randomly selected) was implemented to validate the feasibility to merge radiomics data from CBCT and CT. The consistency of clustering result and site grouping was verified by a chi-square test for different datasets respectively. Results: For simple scaling, 234 of the 253 features have correlation coefficient ρ>0.8 among which 154 features haveρ>0.9 . For radiomics data after nonlinear correction, 240 of the 253 features have ρ>0.8 among which 220 features have ρ>0.9. Cluster analysis of mixed data shows that data of four sites was almost precisely separated for simple scaling(p=1.29 * 10{sup −7}, χ{sup 2} test) and nonlinear correction (p=5.98 * 10{sup −7}, χ{sup 2} test), which is similar to the cluster result of CT data (p=4.52 * 10{sup −8}, χ{sup 2} test). Conclusion: Radiomics data from CBCT can be merged with those from CT by simple scaling or nonlinear correction for radiomics analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment

Purpose: The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting. Methods: We investigated 11 CT texture features for radiation-induced lung disease from 3 categories (first-order, grey level co-ocurrence matrix (GLCM), and Law’s filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2-dimensional sub-regions with a grid spacing of 31 mm. The distribution of the evaluated texture features from these sub-regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non-significant dependence on the factor. Results: Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law’s filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First-order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%. Conclusion: Amongst the studied features, GLCM and Law’s filter texture features were more robust than first-order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre-Treatment and Post-Treatment FDG PET Image for NSCLC Patients

Purpose: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. Methods: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculated by using post-treatment features to subtract pre-treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features. Results: The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942. Conclusion: This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients

Purpose: There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lung cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. Methods: FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. Results: All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66). Conclusion: Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-06: Evaluation of Imaging Dose for Modulation Layer Based Dual Energy Cone-Beam CT

Purpose: Dual energy cone beam CT system is finding a variety of promising applications in diagnostic CT, both in imaging of endogenous materials and exogenous materials across a range of body sites. Dual energy cone beam CT system to suggest in this study acquire image by rotating 360 degree with half of the X-ray window covered using copper modulation layer. In the region that covered by modulation layer absorb the low energy X-ray by modulation layer. Relative high energy X-ray passes through the layer and contributes to image reconstruction. Dose evaluation should be carried out in order to utilize such an imaging acquirement technology for clinical use. Methods: For evaluating imaging dose of modulation layer based dual energy cone beam CT system, Prototype cone beam CT that configured X-ray tube (D054SB, Toshiba, Japan) and detector (PaxScan 2520V, Varian Medical Systems, Palo Alto, CA) is used. A range of 0.5–2.0 mm thickness of modulation layer is implemented in Monte Carlo simulation (MCNPX, ver. 2.6.0, Los Alamos National Laboratory, USA) with half of X-ray window covered. In-house phantom using in this study that has 3 cylindrical phantoms configured water, Teflon air with PMMA covered for verifying the comparability the various material in human body and is implemented in Monte Carlo simulation. The actual dose with 2.0 mm copper covered half of X-ray window is measured using Gafchromic EBT3 film with 5.0 mm bolus for compared with simulative dose. Results: Dose in phantom reduced 33% by copper modulation layer of 2.0 mm. Scattering dose occurred in modulation layer by Compton scattering effect is 0.04% of overall dose. Conclusion: Modulation layer of that based dual energy cone beam CT has not influence on unnecessary scatter dose. This study was supported by the Radiation Safety Research Programs (1305033) through the Nuclear Safety and Security Commission.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-08: CT Image Ring Artifact Reduction Based On Prior Image

Purpose: In computed tomography (CT) system, CT images with ring artifacts will be reconstructed when some adjacent bins of detector don’t work. The ring artifacts severely degrade CT image quality. We present a useful CT ring artifacts reduction based on projection data correction, aiming at estimating the missing data of projection data accurately, thus removing the ring artifacts of CT images. Methods: The method consists of ten steps: 1) Identification of abnormal pixel line in projection sinogram; 2) Linear interpolation within the pixel line of projection sinogram; 3) FBP reconstruction using interpolated projection data; 4) Filtering FBP image using mean filter; 5) Forwarding projection of filtered FBP image; 6) Subtraction forwarded projection from original projection; 7) Linear interpolation of abnormal pixel line area in the subtraction projection; 8) Adding the interpolated subtraction projection on the forwarded projection; 9) FBP reconstruction using corrected projection data; 10) Return to step 4 until the pre-set iteration number is reached. The method is validated on simulated and real data to restore missing projection data and reconstruct ring artifact-free CT images. Results: We have studied impact of amount of dead bins of CT detector on the accuracy of missing data estimation in projection sinogram. For the simulated case with a resolution of 256 by 256 Shepp-Logan phantom, three iterations are sufficient to restore projection data and reconstruct ring artifact-free images when the dead bins rating is under 30%. The dead-bin-induced artifacts are substantially reduced. More iteration number is needed to reconstruct satisfactory images while the rating of dead bins increases. Similar results were found for a real head phantom case. Conclusion: A practical CT image ring artifact correction scheme based on projection data is developed. This method can produce ring artifact-free CT images feasibly and effectively.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-11: Software Development for 4D-CBCT Research of Real-Time-Image Gated Spot Scanning Proton Therapy

Purpose: To acquire correct information for inside the body in patient positioning of Real-time-image Gated spot scanning Proton Therapy (RGPT), utilization of tomographic image at exhale phase of patient respiration obtained from 4-dimensional Cone beam CT (4D-CBCT) has been desired. We developed software named “Image Analysis Platform” for 4D-CBCT researches which has technique to segment projection-images based on 3D marker position in the body. The 3D marker position can be obtained by using two axes CBCT system at Hokkaido University Hospital Proton Therapy Center. Performance verification of the software was implemented. Methods: The software calculates 3D marker position retrospectively by using matching positions on pair projection-images obtained by two axes fluoroscopy mode of CBCT system. Log data of 3D marker tracking are outputted after the tracking. By linking the Log data and gantry-angle file of projection-image, all projection-images are equally segmented to spatial five-phases according to marker 3D position of SI direction and saved to specified phase folder. Segmented projection-images are used for CBCT reconstruction of each phase. As performance verification of the software, test of segmented projection-images was implemented for sample CT phantom (Catphan) image acquired by two axes fluoroscopy mode of CBCT. Dummy marker was added on the images. Motion of the marker was modeled to move in 3D space. Motion type of marker is sin4 wave function has amplitude 10.0 mm/5.0 mm/0 mm, cycle 4 s/4 s/0 s for SI/AP/RL direction. Results: The marker was tracked within 0.58 mm accuracy in 3D for all images, and it was confirmed that all projection-images were segmented and saved to each phase folder correctly. Conclusion: We developed software for 4D-CBCT research which can segment projection-image based on 3D marker position. It will be helpful to create high quality of 4D-CBCT reconstruction image for RGPT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-14: 3D Breast Digital Phantom for XACT Imaging

Purpose: The X-ray induced acoustic computed tomography (XACT) is a new imaging modality which combines X-ray contrast and high ultrasonic resolution in a single modality. Using XACT in breast imaging, a 3D breast volume can be imaged by only one pulsed X-ray radiation, which could dramatically reduce the imaging dose for patients undergoing breast cancer screening and diagnosis. A 3D digital phantom that contains both X-ray properties and acoustic properties of different tissue types is indeed needed for developing and optimizing the XACT system. The purpose of this study is to offer a realistic breast digital phantom as a valuable tool for improving breast XACT imaging techniques and potentially leading to better diagnostic outcomes. Methods: A series of breast CT images along the coronal plane from a patient who has breast calcifications are used as the source images. A HU value based segmentation algorithm is employed to identify breast tissues in five categories, namely the skin tissue, fat tissue, glandular tissue, chest bone and calcifications. For each pixel, the dose related parameters, such as material components and density, and acoustic related parameters, such as frequency-dependent acoustic attenuation coefficient and bandwidth, are assigned based on tissue types. Meanwhile, other parameters which are used in sound propagation, including the sound speed, thermal expansion coefficient, and heat capacity are also assigned to each tissue. Results: A series of 2D tissue type image is acquired first and the 3D digital breast phantom is obtained by using commercial 3D reconstruction software. When giving specific settings including dose depositions and ultrasound center frequency, the X-ray induced initial pressure rise can be calculated accordingly. Conclusion: The proposed 3D breast digital phantom represents a realistic breast anatomic structure and provides a valuable tool for developing and evaluating the system performance for XACT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-31: Reproducibility of An Automatic Exposure Control Technique in the Low-Dose CT Scan of Cardiac PET/CT Exams

Purpose: Two separate low-dose CT scans are usually performed for attenuation correction of rest and stress N-13 ammonia PET/CT myocardial perfusion imaging (PET/CT). We utilize an automatic exposure control (AEC) technique to reduce CT radiation dose while maintaining perfusion image quality. Our goal is to assess the reproducibility of displayed CT dose index (CTDI) on same-day repeat CT scans (CT1 and CT2). Methods: Retrospectively, we reviewed CT images of PET/CT studies performed on the same day. Low-dose CT utilized AEC technique based on tube current modulation called Smart-mA. The scan parameters were 64 × 0.625mm collimation, 5mm slice thickness, 0.984 pitch, 1-sec rotation time, 120 kVp, and noise index 50 with a range of 10–200 mA. The scan length matched with PET field of view (FOV) with the heart near the middle of axial FOV. We identified the reference slice number (RS) for an anatomical landmark (carina) and used it to estimate axial shift between two CTs. For patient size, we measured an effective diameter on the reference slice. The effect of patient positioning to CTDI was evaluated using the table height. We calculated the absolute percent difference of the CTDI (%diff) for estimation of the reproducibility. Results: The study included 168 adults with an average body-mass index of 31.72 ± 9.10 (kg/m{sup 2}) and effective diameter was 32.72 ± 4.60 cm. The average CTDI was 1.95 ± 1.40 mGy for CT1 and 1.97 ± 1.42mGy for CT2. The mean %diff was 7.8 ± 6.8%. Linear regression analysis showed a significant correlation between the table height and %diff CTDI. (r=0.82, p<0.001) Conclusion: We have shown for the first time in human subjects, using two same-day CT images, that the AEC technique in low-dose CT is reproducible within 10% and significantly depends on the patient centering.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-37: How Fat Distribution and Table Height Affect Estimates of Patient Size in CT Scanning: A Phantom Study

Purpose: Localizer projection radiographs acquired prior to CT scans are used to estimate patient size, affecting the function of Automatic Tube Current Modulation (ATCM) and hence CTDIvol and SSDE. Due to geometric effects, the projected patient size varies with scanner table height and with the orientation of the localizer (AP versus PA). This study sought to determine if patient size estimates made from localizer scans is affected by variations in fat distribution, specifically when the widest part of the patient is not at the geometric center of the patient. Methods: Lipid gel bolus material was wrapped around an anthropomorphic phantom to simulate two different body mass distributions. The first represented a patient with fairly rigid fat and had a generally oval shape. The second was bell-shaped, representing corpulent patients more susceptible to gravity’s lustful tug. Each phantom configuration was imaged using an AP localizer and then a PA localizer. This was repeated at various scanner table heights. The width of the phantom was measured from the localizer and diagnostic images using in-house software. Results: 1) The projected phantom width varied up to 39% as table height changed.2) At some table heights, the width of the phantom, designed to represent larger patients, exceeded the localizer field of view, resulting in an underestimation of the phantom width.3) The oval-shaped phantom approached a normalized phantom width of 1 at a table height several centimeters lower (AP localizer) or higher (PA localizer) than did the bell-shaped phantom. Conclusion: Accurate estimation of patient size from localizer scans is dependent on patient positioning with respect to scanner isocenter and is limited in large patients. Further, patient size is more accurately measured on projection images if the widest part of the patient, rather than the geometric center of the patient, is positioned at scanner isocenter.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-41: Calibration-Free Material Decomposition for Dual-Energy CT

Purpose: To eliminate tedious phantom calibration or manually region of interest (ROI) selection as required in dual-energy CT material decomposition, we establish a new projection-domain material decomposition framework with incorporation of energy spectrum. Methods: Similar to the case of dual-energy CT, the integral of the basis material image in our model is expressed as a linear combination of basis functions, which are the polynomials of high- and low-energy raw projection data. To yield the unknown coefficients of the linear combination, the proposed algorithm minimizes the quadratic error between the high- and low-energy raw projection data and the projection calculated using material images. We evaluate the algorithm with an iodine concentration numerical phantom at different dose and iodine concentration levels. The x-ray energy spectra of the high and low energy are estimated using an indirect transmission method. The derived monochromatic images are compared with the high- and low-energy CT images to demonstrate beam hardening artifacts reduction. Quantitative results were measured and compared to the true values. Results: The differences between the true density value used for simulation and that were obtained from the monochromatic images, are 1.8%, 1.3%, 2.3%, and 2.9% for the dose levels from standard dose to 1/8 dose, and are 0.4%, 0.7%, 1.5%, and 1.8% for the four iodine concentration levels from 6 mg/mL to 24 mg/mL. For all of the cases, beam hardening artifacts, especially streaks shown between dense inserts, are almost completely removed in the monochromatic images. Conclusion: The proposed algorithm provides an effective way to yield material images and artifacts-free monochromatic images at different dose levels without the need for phantom calibration or ROI selection. Furthermore, the approach also yields accurate results when the concentration of the iodine concentrate insert is very low, suggesting the algorithm is robust with respect to the low-contrast scenario.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-43: A Software-Based Statistical Method to Compute Low Contrast Detectability in Computed Tomography Images

Purpose: The low contrast detectability (LCD) of a CT scanner is its ability to detect and display faint lesions. The current approach to quantify LCD is achieved using vendor-specific methods and phantoms, typically by subjectively observing the smallest size object at a contrast level above phantom background. However, this approach does not yield clinically applicable values for LCD. The current study proposes a statistical LCD metric using software tools to not only to assess scanner performance, but also to quantify the key factors affecting LCD. This approach was developed using uniform QC phantoms, and its applicability was then extended under simulated clinical conditions. Methods: MATLAB software was developed to compute LCD using a uniform image of a QC phantom. For a given virtual object size, the software randomly samples the image within a selected area, and uses statistical analysis based on Student’s t-distribution to compute the LCD as the minimal Hounsfield Unit’s that can be distinguished from the background at the 95% confidence level. Its validity was assessed by comparison with the behavior of a known QC phantom under various scan protocols and a tissue-mimicking phantom. The contributions of beam quality and scattered radiation upon the computed LCD were quantified by using various external beam-hardening filters and phantom lengths. Results: As expected, the LCD was inversely related to object size under all scan conditions. The type of image reconstruction kernel filter and tissue/organ type strongly influenced the background noise characteristics and therefore, the computed LCD for the associated image. Conclusion: The proposed metric and its associated software tools are vendor-independent and can be used to analyze any LCD scanner performance. Furthermore, the method employed can be used in conjunction with the relationships established in this study between LCD and tissue type to extend these concepts to patients’ clinical CT images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-46: Optimizing Dose Reduction in Adult Head CT Protocols While Maintaining Image Quality in Postmortem Head Scans

Purpose: To optimize adult head CT protocol by reducing dose to an appropriate level while providing CT images of diagnostic quality. Methods: Five cadavers were scanned from the skull base to the vertex using a routine adult head CT protocol (120 kVp, 270 mA, 0.75 s rotation, 0.5 mm × 32 detectors, 70.8 mGy CTDIvol) followed by seven reduced-dose protocols with varying combinations of reduced tube current, reduced rotation time, and increased detectors with CTDIvol ranging from 38.2 to 65.6 mGy. Organ doses were directly measured with 21 OSL dosimeters placed on the surface and implanted in the head by a neurosurgeon. Two neuroradiologists assessed grey-white matter differentiation, fluid space, ventricular size, midline shift, brain mass, edema, ischemia, and skull fractures on a three point scale: (1) Unacceptable, (2) Borderline Acceptable, and (3) Acceptable. Results: For the standard scan, doses to the skin, lens of the eye, salivary glands, thyroid, and brain were 37.55 mGy, 49.65 mGy, 40.67 mGy, 4.63 mGy, and 27.33 mGy, respectively. Two cadavers had cerebral edema due to changing dynamics of postmortem effects, causing the grey-white matter differentiation to appear less distinct. Two cadavers with preserved grey-white matter received acceptable scores for all image quality features for the protocol with a CTDIvol of 57.3 mGy, allowing organ dose savings ranging from 34% to 45%. One cadaver allowed for greater dose reduction for the protocol with a CTDIvol of 42 mGy. Conclusion: Efforts to optimize scan protocol should consider both dose and clinical image quality. This is made possible with postmortem subjects, whose brains are similar to patients, allowing for an investigation of ideal scan parameters. Radiologists at our institution accepted scan protocols acquired with lower scan parameters, with CTDIvol values closer to the American College of Radiology’s (ACR) Achievable Dose level of 57 mGy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-47: Optimizing Protocols for Image Quality and Dose in Abdominal CT of Large Patients

Purpose: Newer CT scanners are able to use scout views to adjust mA throughout the scan in order to achieve a given noise level. However, given constraints of radiologist preferences for kVp and rotation time, it may not be possible to achieve an acceptable noise level for large patients. A study was initiated to determine for which patients kVp and/or rotation time should be changed in order to achieve acceptable image quality. Methods: Patient scans were reviewed on two new Emergency Department scanners (Philips iCT) to identify patients over a large range of sizes. These iCTs were set with a limit of 500 mA to safeguard against a failure that might cause a CT scan to be (incorrectly) obtained at too-high mA. Scout views of these scans were assessed for both AP and LAT patient width and AP and LAT standard deviation in an ROI over the liver. Effective diameter and product of the scout standard deviations over the liver were both studied as possible metrics for identifying patients who would need kVp and/or rotation time changed. The mA used for the liver in the CT was compared to these metrics for those patients whose CT scans showed acceptable image quality. Results: Both effective diameter and product of the scout standard deviations over the liver result in similar predictions for which patients will require the kVp and/or rotation time to be changed to achieve an optimal combination of image quality and dose. Conclusion: Two mechanisms for CT technologists to determine based on scout characteristics what kVp, mA limit, and rotation time to use when DoseRight with our physicians’ preferred kVp and rotation time will not yield adequate image quality are described.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-50: Finite Element-Based Deformable Image Registration of Lung and Heart

Purpose: Photodynamic therapy (PDT) is used after surgical resection to treat the microscopic disease for malignant pleural mesothelioma and to increase survival rates. Although accurate light delivery is imperative to PDT efficacy, the deformation of the pleural volume during the surgery impacts the delivered light dose. To facilitate treatment planning, we use a finite-element-based (FEM) deformable image registration to quantify the anatomical variation of lung and heart volumes between CT pre-(or post-) surgery and surface contours obtained during PDT using an infrared camera-based navigation system (NDI). Methods: NDI is used during PDT to obtain the information of the cumulative light fluence on every cavity surface point that is being treated. A wand, comprised of a modified endotrachial tube filled with Intralipid and an optical fiber inside the tube, is used to deliver the light during PDT. The position of the treatment is tracked using an attachment with nine reflective passive markers that are seen by the NDI system. Then, the position points are plotted as three-dimensional volume of the pleural cavity using Matlab and Meshlab. A series of computed tomography (CT) scans of the lungs and heart, in the same patient, are also acquired before and after the surgery. The NDI and CT contours are imported into COMSOL Multiphysics, where the FEM-based deformable image registration is obtained. The NDI and CT contours acquired during and post-PDT are considered as the reference, and the Pre-PDT CT contours are used as the target, which will be deformed. Results: Anatomical variation of the lung and heart volumes, taken at different times from different imaging devices, was determined by using our model. The resulting three-dimensional deformation map along x, y and z-axes was obtained. Conclusion: Our model fuses images acquired by different modalities and provides insights into the variation in anatomical structures over time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-57: Evaluate and Optimize PET Acquisition Overlap in 18F-FDG Oncology Wholebody PET/CT: Can We Scan PET Faster?

Purpose: The longer patient has to remain on the table during PET imaging, the higher the likelihood of motion artifacts due to patient discomfort. This study was to investigate and optimize PET acquisition overlap in 18F-FDG oncology wholebody PET/CT to speed up PET acquisition and improve patient comfort. Methods: Wholebody 18F-FDG PET/CT of phantoms, 8 pre-clinical patients (beagles) and 5 clinical oncology patients were performed in 90s/bed on a time-of-flight Gemini TF 64 system. Imaging of phantoms and beagles was acquired with reduced PET overlaps (40%, 33%, 27%, 20%, 13% and no overlap) in addition to the system default (53%). In human studies, 1 or 2 reduced overlaps from the listed options were used to acquire PET/CT sweeps right after the default standard of care imaging. Image quality was blindly reviewed using visual scoring criteria and quantitative SUV assessment. NEMA PET sensitivity was performed under different overlaps. Results: All PET exams demonstrated no significant impact on the visual grades for overlaps >20%. Blinded reviews assigned the best visual scores to PET using overlaps 53%–27%. Reducing overlap to 27% for oncology patients (12-bed) saved an average of ∼40% acquisition time (11min) compared to using the default overlap (18min). No significant SUV variances were found when reducing overlap to half of default for cerebellum, lung, heart, aorta, liver, fat, muscle, bone marrow, thighs and target lesions (p>0.05), except expected variability in urinary system. Conclusion: This study demonstrated by combined phantom, pre-clinical and clinical PET/CT scans that PET acquisition overlap in axial of today’s systems can be reduced and optimized. It showed that a reduction of PET acquisition overlap to 27% (half of system default) can be implemented to reduce table time by ∼40% to improve patient comfort and minimize potential motion artifacts, without prominently degrading image quality or compromising PET quantification.

60 APPLIED LIFE SCIENCES↗