bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐G‐206‐01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials
Authors: Wahi‐Anwar M , Lo P , Kim H , Brown M , McNitt‐Gray M 
Published: July 2016

Purpose:The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully‐automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials.Methods:This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols ‐ totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold‐based classifiers. Each classifier ‐ corresponding to a phantom type ‐ contains a template slice, which is compared to the input scan on a slice‐by‐slice basis, resulting in slice‐wise similarity metric values for each slice compared. Pre‐trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier's matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble's best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures.Results:Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy.Conclusion:We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications.Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.
SU‐G‐206‐04: A Method for Realizing Phantom Calibration and Geometry Calibration Accurately Based On a Geometry Evaluation Index
Authors: Yang S , Wu S , Qi H , Xu Y , Zhou L 
Published: July 2016

Purpose:For traditional geometric calibration, the calibration accuracy relies on both accuracy of geometry phantom manufacture and accuracy of ball bearings (BB) location estimation. In this work, we have developed a method to perform phantom calibration and geometry calibration iteratively and accurately in a whole procedure.Methods:We have designed and manufactured a geometry phantom with BB and an evaluation phantom of a crystal ball contained in a cubic gel box. Our calibration method consists of five steps: 1) Estimate BB locations using spiral CT image, which are then used to initialize the particles in Particle Swarm Optimization (PSO) algorithm; 2) Perform geometric calibration; 3) Reconstruct the images of the evaluation phantom based on the current geometry calibration; 4) Evaluate the reconstructed images using a geometry evaluate index; 5) Update BB locations in PSO algorithm. Repeat step2)‐5) until our stopping criteria is met. The edge of the crystal ball in the calibration phantom on CBCT images is detected by Hough transform to define two circular rings outside and inside the ball. The evaluation index used in step 4) is defined as the difference of the averaged image intensities of these two circular rings.Results:We have demonstrated the feasibility and performance of our method on a benchtop CBCT system. It is observed that inaccurate BB locations lead to severe image distortion and relative small evaluation index. With our method, streak artifacts are reduced and the structure becomes sharper and clearer. The evaluation index increases fast within 10 iterations, and then becomes stable gradually.Conclusion:Our method can perform accurate phantom calibration and geometry calibration together in a whole procedure. It helps to mitigate the impact of the geometry phantom manufacture errors on the calibration, which could hence save the cost of the geometry phantom manufacture.
SU‐G‐206‐08: How Should Focal Spot Be Chosen for Optimized CT Imaging with Dose Modulation?
Authors: Bache S , Liu X , Rong J 
Published: July 2016

Purpose:To choose the preferred focal spot for achieving optimized CT image quality with balanced tube heating considerations.Methods:An anthropomorphic pelvic phantom was scanned using a GE Discovery CT750 HD at 120 and 140kVp, 0.8s rotation time, and pitch of 0.984. “Smart mA” was enabled to simulate a routine abdomen–pelvis CT scan. Permissible mA values at 120 and 140 kVp were obtained from the Serial Load Rating table (for mimicking a busy CT clinical operation) in the scanner Technical Reference Manual. At each kVp station and two Noise Index levels, the mA Upper Limit was set above/below the permissible mA values. Scanned mA values and focal spot (FS) used were obtained from the DICOM header of each image, and the FS‐mA relationship was analyzed. For visual confirmation beyond recorded FS information, a CatPhan with a fat‐ring attached for mimicking patient size/shape was scanned at 120kVp. A group of radiologists/physicists compared a pair of CatPhan images qualitatively. Lastly, a number of patient cases were evaluated to confirm the FS‐mA relationship.Results:When preset Upper Limit values were above the permissible mA values, the Large FS (labeled 1.2) was used in scans, even if the maximum scanned mA values were much lower than the permissible values at both 120 and 140 kVp. Otherwise the Small FS (labeled 0.7) was used. Visual evaluation of the high contrast module of CatPhan and additional analysis of patient cases further confirmed that the preset Upper Limit determines which focal spot is to be used, not the actual maximum mA value to be scanned.Conclusion:Specific FS can be selected by setting up appropriate mA Upper Limit in a protocol. CT protocols could be optimized by selecting appropriate FS for improving patient image quality, especially benefiting the small size and pediatric patients.
SU‐G‐206‐14: Individual Dose Alert Thresholds for Abdominal CT Protocols Due to Statistical Independence of Dose Distributions
Authors: Stewart BK 
Published: July 2016

Purpose:To determine whether a single dose management system alert threshold will suffice for the 34 abdomen protocols on our institution's CT scanners, or if these alert thresholds should be set per protocol. Parametric statistical analysis is ideally valid only for distributions demonstrating: (1) normality (2) homogeneity of variance (3) interval scale and (4) independence. Dose distributions are notoriously non‐normal and thus nonparametric statistical analysis is typically required.Methods:Dose distribution data were collected from a HD750 CT scanner (GE Healthcare) at our institution over the third and fourth quarters of 2015. In total there were 1793 abdomen studies using 34 different protocols. Protocols were selected for statistical analysis (SPSS, IBM Corp.) if the count over the period was ≥ 50 to produce statistically meaningful results. Nine protocols with count ranging from 61‐398 were selected. Protocol dose distributions were statistically analyzed to determine: (1) normality – Shapiro‐Wilk test (2) homogeneity of variance – Levene Statistic (3) similarity of medians – Median test, and (4) similarity of distribution – Kruskal‐Wallis test.Results:For the nine abdomen protocols used with exam count ≥ 50, the dose distributions were statistically significantly different from normal for eight protocols (p≤[0.001‐0.035]), homogeneity of variance invalidated for five protocols (p≤[0.001‐0.037]), median values not the same across all protocols (p<0.001), and all dose distributions different from each other (p<0.001).Conclusion:That all abdomen protocols with exam count ≥ 50, the distributions were predominantly: (1) non‐normal (2) of unequal variance over the distributions (3) of unequal means and (4) non‐identical distributions indicates that the abdomen protocols in general must be treated independently with non‐parametric statistical analysis and thus the alert thresholds should be calculated and set individually, that is, there cannot be a solitary meaningful alert threshold utilized over all abdominal protocols examined in this study.
SU‐G‐IeP1‐05: Diffusion Kurtosis Imaging for Oropharyngeal Cancer Detection
Authors: Ding Y , Mohamed A , Ma J , Frank S , Wang J , Fuller C 
Published: July 2016

Purpose:Diffusion kurtosis imaging (DKI) is an emerging diffusion MRI technique in cancer diagnosis applications. The objective of this study is to compare DKI with conventional diffusion weighted imaging (DWI) for diagnosis of head and neck cancer.Methods:Five male patients with histologically documented phase II/III squamous cell carcinoma of the oropharynx were included in this study. DKI (with 6 b‐values of 0‐2000 s/mm2) and conventional DWI data were acquired at a 3.0 T GE MRI scanner. Monoexponential (calculating apparent diffusion coefficient (ADC) using DWI data) and non‐Gaussian kurtosis (calculating mean diffusion coefficient (MD) and mean kurtosis coefficient (MK) using DKI data) fits were performed on a voxel‐by‐voxel basis in selected regions of interest (primary tumor, metastatic nodes, contralateral region of tumor (tongue muscle), and submandibular glands). The non‐parametric Wilcoxon test of ADC and DKI parameters between primary tumor and tongue muscle in contralateral region of tumor were calculated for comparison.Results:Examples of T1 post‐contrast images and diffusion parametric maps (ADC, MD and MK) were illustrated in Figure 1 for an oropharynx cancer patient. MK parameters were significantly higher in the primary tumor than in the contralateral tongue muscle (0.89 ± 0.17 vs 0.68 ± 0.13, respectively; P <.05). MD and ADC in the primary tumor were significantly lower than those in the contralateral tongue muscle (1.20 ± 0.23 vs 2.57 ± 1.01; P <0.02 and 0.88 ± 0.22 vs 1.41 ± 0.18; P <0.01, respectively) (Table 1). Figure 2 showed statistical distribution of DWI and DKI parameters in selected regions of interest (primary tumor, metastatic nodes, tongue muscle, and submandibular glands).Conclusion:The preliminary results of this study showed DKI could be a new option for increasing diagnostic confidence of oropharyngeal lesions. We continue to accrue study patients to evaluate the potential correlation between DKI parameters and tumor pathologic factors.
SU‐G‐IeP1‐13: Sub‐Nyquist Dynamic MRI Via Prior Rank, Intensity and Sparsity Model (PRISM)
Authors: Jiang B , Gao H 
Published: July 2016

Purpose:Accelerated dynamic MRI is important for MRI guided radiotherapy. Inspired by compressive sensing (CS), sub‐Nyquist dynamic MRI has been an active research area, i.e., sparse sampling in k‐t space for accelerated dynamic MRI. This work is to investigate sub‐Nyquist dynamic MRI via a previously developed CS model, namely Prior Rank, Intensity and Sparsity Model (PRISM).Methods:The proposed method utilizes PRISM with rank minimization and incoherent sampling patterns for sub‐Nyquist reconstruction. In PRISM, the low‐rank background image, which is automatically calculated by rank minimization, is excluded from the L1 minimization step of the CS reconstruction to further sparsify the residual image, thus allowing for higher acceleration rates. Furthermore, the sampling pattern in k‐t space is made more incoherent by sampling a different set of k‐space points at different temporal frames.Results:Reconstruction results from L1‐sparsity method and PRISM method with 30% undersampled data and 15% undersampled data are compared to demonstrate the power of PRISM for dynamic MRI.Conclusion:A sub‐ Nyquist MRI reconstruction method based on PRISM is developed with improved image quality from the L1‐sparsity method.
SU‐G‐JeP1‐09: Evaluation of Transperineal Ultrasound Imaging as a Potential Solution for Target Tracking During Ablative Body Radiotherapy for Prostate Cancer
Authors: Najafi M , Han B , Cooper D , Hancock S , Hristov D 
Published: July 2016

Purpose:Prostate SABR is emerging as a clinically viable, potentially cost effective alternative to prostate IMRT but its adoption is contingent on providing solutions for accurate tracking during beam delivery. Our goal is to evaluate the performance of the Clarity Autoscan ultrasound monitoring system for inter‐fractional prostate motion tracking in both phantoms and in‐vivo.Methods:In‐vivo evaluation was performed under IRB protocol to allow data collection in prostate patients treated with VMAT whereby prostate was imaged through the acoustic window of the perineum. The probe was placed before KV imaging and real‐time tracking was started and continued until the end of treatment. Initial absolute 3D positions of fiducials were estimated from KV images. Fiducial positions in MV images subsequently acquired during beam delivery were compared with predicted positions based on Clarity estimated motion.Results:Phantom studies with motion amplitudes of ±1.5, ±3, ±6 mm in lateral direction and ±2 mm in longitudinal direction resulted in tracking errors of −0.03 ± 0.3, −0.04 ± 0.6, −0.2 ± 0.9 mm, respectively, in lateral direction and −0.05 ± 0.30 mm in longitudinal direction. In phantom, measured and predicted fiducial positions in MV images were within 0.1 ± 0.6 mm. Four patients consented to participate in the study and data was acquired over a total of 140 fractions. MV imaging tracking was possible in about 75% of the time (due to occlusion of fiducials) compared to 100% with Clarity. Overall range of estimated motion by Clarity was 0 to 4.0 mm. In‐vivo fiducial localization error was 1.2 ± 1.0 mm compared to 1.8 ± 1.9 mm if not taking Clarity estimated motion into account.Conclusion:Real‐time transperineal ultrasound tracking reduces uncertainty in prostate position due to intrafractional motion.Research was supported by Elekta.
SU‐G‐IeP3‐04: Effective Dose Measurements in Fast Kvp Switch Dual Energy Computed Tomography
Authors: Raudabaugh J , Nguyen G , Moore B , Lowry C , Nelson R , Yoshizumi T 
Published: July 2016

Purpose:The objective of this study was two‐fold: (a) to test a new approach to approximating organ dose by using the effective energy of the combined 80kV/140kV beam in dual‐energy (DE) computed tomography (CT), and (b) to derive the effective dose (ED) in the abdomen‐pelvis protocol in DECT.Methods:A commercial dual energy CT scanner was employed using a fast‐kV switch abdomen/pelvis protocol alternating between 80 kV and 140 kV. MOSFET detectors were used for organ dose measurements. First, an experimental validation of the dose equivalency between MOSFET and ion chamber (as a gold standard) was performed using a CTDI phantom. Second, the ED of DECT scans was measured using MOSFET detectors and an anthropomorphic phantom. For ED calculations, an abdomen/pelvis scan was used using ICRP 103 tissue weighting factors; ED was also computed using the AAPM Dose Length Product (DLP) method and compared to the MOSFET value.Results:The effective energy was determined as 42.9 kV under the combined beam from half‐value layer (HVL) measurement. ED for the dual‐energy scan was calculated as 16.49 ± 0.04 mSv by the MOSFET method and 14.62 mSv by the DLP method.Conclusion:Tissue dose in the center of the CTDI body phantom was 1.71 ± 0.01 cGy (ion chamber) and 1.71 ± 0.06 (MOSFET) respectively; this validated the use of effective energy method for organ dose estimation. ED from the abdomen‐pelvis scan was calculated as 16.49 ± 0.04 mSv by MOSFET and 14.62 mSv by the DLP method; this suggests that the DLP method provides a reasonable approximation to the ED.
SU‐G‐IeP3‐10: Molecular Imaging with Clinical X‐Ray Sources and Compton Cameras
Authors: Vernekohl D , Ahmad M , Chinn G , Xing L 
Published: July 2016

Purpose:The application of Compton cameras (CC) is a novel approach translating XFCT to a practical modality realized with clinical CT systems without the restriction of pencil beams. The dual modality design offers additional information without extra patient dose. The purpose of this work is to investigate the feasibility and efficacy of using CCs for volumetric x‐ray fluorescence (XF) imaging by Monte Carlo (MC) simulations and statistical image reconstruction.Methods:The feasibility of a CC for imaging x‐ray fluorescence emitted from targeted lesions is examined by MC simulations. 3 mm diameter water spheres with various gold concentrations and detector distances are placed inside the lung of an adult human phantom (MIRD) and are irradiated with both fan and cone‐beam geometries. A sandwich design CC composed of Silicon and CdTe is used to image the gold nanoparticle distribution. The detection system comprises four 16×26 cm2 detector panels placed on the chest of a MIRD phantom. Constraints of energy‐, spatial‐resolution, clinical geometries and Doppler broadening are taken into account. Image reconstruction is performed with a list‐mode MLEM algorithm with cone‐projector on a GPU.Results:The comparison of reconstruction of cone‐ and fan‐beam excitation shows that the spatial resolution is improved by 23% for fan‐beams with significantly decreased processing time. Cone‐beam excitation increases scatter content disturbing quantification of lesions near the body surface. Spatial resolution and detectability limit in the center of the lung is 8.7 mm and 20 fM for 50 nm diameter gold nanoparticles at 20 mGy.Conclusion:The implementation of XFCT with a CC is a feasible method for molecular imaging with high atomic number probes. Given constrains of detector resolutions, Doppler broadening, and limited exposure dose, spatial resolutions comparable with PET and molecular sensitivities in the fM range are realizable with current detector technology.
SU‐G‐IeP3‐11: On the Utility of Pixel Variance to Characterize Noise for Image Receptors of Digital Radiography Systems
Authors: Finley C , Dave J 
Published: July 2016

Purpose:To characterize noise for image receptors of digital radiography systems based on pixel variance.Methods:Nine calibrated digital image receptors associated with nine new portable digital radiography systems (Carestream Health, Inc., Rochester, NY) were used in this study. For each image receptor, thirteen images were acquired with RQA5 beam conditions for input detector air kerma ranging from 0 to 110 µGy, and linearized ‘For Processing’ images were extracted. Mean pixel value (MPV), standard deviation (SD) and relative noise (SD/MPV) were obtained from each image using ROI sizes varying from 2.5×2.5 to 20×20 mm2. Variance (SD2) was plotted as a function of input detector air kerma and the coefficients of the quadratic fit were used to derive structured, quantum and electronic noise coefficients. Relative noise was also fitted as a function of input detector air kerma to identify noise sources. The fitting functions used least‐squares approach.Results:The coefficient of variation values obtained using different ROI sizes was less than 1% for all the images. The structured, quantum and electronic coefficients obtained from the quadratic fit of variance (r>0.97) were 0.43±0.10, 3.95±0.27 and 2.89±0.74 (mean ± standard deviation), respectively, indicating that overall the quantum noise was the dominant noise source. However, for one system electronic noise coefficient (3.91) was greater than quantum noise coefficient (3.56) indicating electronic noise to be dominant. Using relative noise values, the power parameter of the fitting equation (|r|>0.93) showed a mean and standard deviation of 0.46±0.02. A 0.50 value for this power parameter indicates quantum noise to be the dominant noise source whereas values around 0.50 indicate presence of other noise sources.Conclusion:Characterizing noise from pixel variance assists in identifying contributions from various noise sources that, eventually, may affect image quality. This approach may be integrated during periodic quality assessments of digital image receptors.
SU‐G‐JeP3‐01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy
Authors: Lafata K , Ren L , Cai J , Yin F 
Published: July 2016

Purpose:To develop a methodology based on digitally‐reconstructed‐fluoroscopy (DRF) to quantitatively assess target localization accuracy of lung SBRT, and to evaluate using both a dynamic digital phantom and a patient dataset.Methods:For each treatment field, a 10‐phase DRF is generated based on the planning 4DCT. Each frame is pre‐processed with a morphological top‐hat filter, and corresponding beam apertures are projected to each detector plane. A template‐matching algorithm based on cross‐correlation is used to detect the tumor location in each frame. Tumor motion relative beam aperture is extracted in the superior‐inferior direction based on each frame's impulse response to the template, and the mean tumor position (MTP) is calculated as the average tumor displacement. The DRF template coordinates are then transferred to the corresponding MV‐cine dataset, which is retrospectively filtered as above. The treatment MTP is calculated within each field's projection space, relative to the DRF‐defined template. The field's localization error is defined as the difference between the DRF‐derived‐MTP (planning) and the MV‐cine‐derived‐MTP (delivery). A dynamic digital phantom was used to assess the algorithm's ability to detect intra‐fractional changes in patient alignment, by simulating different spatial variations in the MV‐cine and calculating the corresponding change in MTP. Inter‐and‐intra‐fractional variation, IGRT accuracy, and filtering effects were investigated on a patient dataset.Results:Phantom results demonstrated a high accuracy in detecting both translational and rotational variation. The lowest localization error of the patient dataset was achieved at each fraction's first field (mean=0.38mm), with Fx3 demonstrating a particularly strong correlation between intra‐fractional motion‐caused localization error and treatment progress. Filtering significantly improved tracking visibility in both the DRF and MV‐cine images.Conclusion:We have developed and evaluated a methodology to quantify lung SBRT target localization accuracy based on digitally‐reconstructed‐fluoroscopy. Our approach may be useful in potentially reducing treatment margins to optimize lung SBRT outcomes.R01‐184173