bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐G‐204‐01: BEST IN PHYSICS (IMAGING): Effect of Image Processing Parameters On Nodule Detectability in Chest Radiography
Authors: Little K , Lu Z , MacMahon H , Reiser I 
Published: June 2015

Purpose:To investigate the effect of varying system image processing parameters on lung nodule detectability in digital radiography.Methods:An anthropomorphic chest phantom was imaged in the posterior‐anterior position using a GE Discovery XR656 digital radiography system. To simulate lung nodules, a polystyrene board with 6.35mm diameter PMMA spheres was placed adjacent to the phantom (into the x‐ray path). Due to magnification, the projected simulated nodules had a diameter in the radiographs of approximately 7.5 mm. The images were processed using one of GE's default chest settings (Factory3) and reprocessed by varying the “Edge” and “Tissue Contrast” processing parameters, which were the two user‐configurable parameters for a single edge and contrast enhancement algorithm. For each parameter setting, the nodule signals were calculated by subtracting the chest‐only image from the image with simulated nodules. Twenty nodule signals were averaged, Gaussian filtered, and radially averaged in order to generate an approximately noiseless signal. For each processing parameter setting, this noise‐free signal and 180 background samples from across the lung were used to estimate ideal observer performance in a signal‐known‐exactly detection task. Performance was estimated using a channelized Hotelling observer with 10 Laguerre‐Gauss channel functions.Results:The “Edge” and “Tissue Contrast” parameters each had an effect on the detectability as calculated by the model observer. The CHO‐estimated signal detectability ranged from 2.36 to 2.93 and was highest for “Edge” = 4 and “Tissue Contrast” = −0.15. In general, detectability tended to decrease as “Edge” was increased and as “Tissue Contrast” was increased. A human observer study should be performed to validate the relation to human detection performance.Conclusion:Image processing parameters can affect lung nodule detection performance in radiography. While validation with a human observer study is needed, model observer detectability for common tasks could provide a means for optimizing image processing parameters.
WE‐G‐204‐04: Focal Spot Deblurring For High Resolution Amorphous Selenium (aSe) Complementary Metal Oxide Semiconductor (CMOS) X‐Ray Detector
Authors: Nagesh S Setlur , Rana R , Russ M , Ionita C , Bednarek D , Rudin S 
Published: June 2015

Purpose:CMOS‐based aSe detectors compared to CsI‐TFT‐based flat panels have the advantages of higher spatial sampling due to smaller pixel size and decreased blurring characteristic of direct rather than indirect detection. For systems with such detectors, the limiting factor degrading image resolution then becomes the focal‐spot geometric unsharpness. This effect can seriously limit the use of such detectors in areas such as cone beam computed tomography, clinical fluoroscopy and angiography. In this work a technique to remove the effect of focal‐spot blur is presented for a simulated aSe detector.Method:To simulate images from an aSe detector affected with focal‐spot blur, first a set of high‐resolution images of a stent (FRED from Microvention, Inc.) were acquired using a 75µm pixel size Dexela‐Perkin‐Elmer detector and averaged to reduce quantum noise. Then the averaged image was blurred with a known Gaussian blur at two different magnifications to simulate an idealized focal spot. The blurred images were then deconvolved with a set of different Gaussian blurs to remove the effect of focal‐spot blurring using a threshold‐based, inverse‐filtering method.Results:The blur was removed by deconvolving the images using a set of Gaussian functions for both magnifications. Selecting the correct function resulted in an image close to the original; however, selection of too wide a function would cause severe artifacts.Conclusion:Experimentally, focal‐spot blur at different magnifications can be measured using a pin hole with a high resolution detector. This spread function can be used to deblur the input images that are acquired at corresponding magnifications to correct for the focal spot blur. For CBCT applications, the magnification of specific objects can be obtained using initial reconstructions then corrected for focal‐spot blurring to improve resolution. Similarly, if object magnification can be determined such correction may be applied in fluoroscopy and angiography.
WE‐G‐204‐05: Relative Object Detectability Evaluation of a New High Resolution A‐Se Direct Detection System Compared to Indirect Micro‐Angiographic Fluoroscopic (MAF) Detectors
Authors: Russ M , Nagesh S Setlur , Ionita C , Scott C , Karim K , Bednarek D , Rudin S 
Published: June 2015

Purpose:To evaluate the task specific imaging performance of a new 25µm pixel pitch, 1000µm thick amorphous selenium direct detection system with CMOS readout for typical angiographic exposure parameters using the relative object detectability (ROD) metric.Methods:The ROD metric uses a simulated object function weighted at each spatial frequency by the detectors’ detective quantum efficiency (DQE), which is an intrinsic performance metric. For this study, the simulated objects were aluminum spheres of varying diameter (0.05–0.6mm). The weighted object function is then integrated over the full range of detectable frequencies inherent to each detector, and a ratio is taken of the resulting value for two detectors. The DQE for the 25µm detector was obtained from a simulation of a proposed a‐Se detector using an exposure of 200µR for a 50keV x‐ray beam. This a‐Se detector was compared to two microangiographic fluoroscope (MAF) detectors [the MAF‐CCD with pixel size of 35µm and Nyquist frequency of 14.2 cycles/mm and the MAF‐CMOS with pixel size of 75µm and Nyquist frequency of 6.6 cycles/mm] and a standard flat‐panel detector (FPD with pixel size of 194µm and Nyquist frequency of 2.5cycles/mm).Results:ROD calculations indicated vastly superior performance by the a‐Se detector in imaging small aluminum spheres. For the 50µm diameter sphere, the ROD values for the a‐Se detector compared to the MAF‐CCD, the MAF‐CMOS, and the FPD were 7.3, 9.3 and 58, respectively. Detector performance in the low frequency regime was dictated by each detector's DQE(0) value.Conclusion:The a‐Se with CMOS readout is unique and appears to have distinctive advantages of incomparable high resolution, low noise, no readout lag, and expandable design. The a‐Se direct detection system will be a powerful imaging tool in angiography, with potential break‐through applications in diagnosis and treatment of neuro‐vascular disease.Supported by NIH Grant: 2R01EB002873 and an equipment grant from Toshiba Medical Systems Corporation
WE‐G‐204‐06: Grid‐Line Artifact Minimization for High Resolution Detectors Using Iterative Residual Scatter Correction
Authors: Rana R , Bednarek D , Rudin S 
Published: June 2015

Purpose:Anti‐scatter grid‐line artifacts are more prominent for high‐resolution x‐ray detectors since the fraction of a pixel blocked by the grid septa is large. Direct logarithmic subtraction of the artifact pattern is limited by residual scattered radiation and we investigate an iterative method for scatter correction.Methods:A stationary Smit‐Rοntgen anti‐scatter grid was used with a high resolution Dexela 1207 CMOS X‐ray detector (75 µm pixel size) to image an artery block (Nuclear Associates, Model 76‐705) placed within a uniform head equivalent phantom as the scattering source. The image of the phantom was divided by a flat‐field image obtained without scatter but with the grid to eliminate grid‐line artifacts. Constant scatter values were subtracted from the phantom image before dividing by the averaged flat‐field‐with‐grid image. The standard deviation of pixel values for a fixed region of the resultant images with different subtracted scatter values provided a measure of the remaining grid‐line artifacts.Results:A plot of the standard deviation of image pixel values versus the subtracted scatter value shows that the image structure noise reaches a minimum before going up again as the scatter value is increased. This minimum corresponds to a minimization of the grid‐line artifacts as demonstrated in line profile plots obtained through each of the images perpendicular to the grid lines. Artifact‐free images of the artery block were obtained with the optimal scatter value obtained by this iterative approach.Conclusion:Residual scatter subtraction can provide improved grid‐line artifact elimination when using the flat‐field with grid “subtraction” technique. The standard deviation of image pixel values can be used to determine the optimal scatter value to subtract to obtain a minimization of grid line artifacts with high resolution x‐ray imaging detectors.This study was supported by NIH Grant R01EB002873 and an equipment grant from Toshiba Medical Systems Corp.
WE‐G‐204‐07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection
Authors: Wells J , Zhang L , Samei E 
Published: June 2015

Purpose:To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)]Methods:Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c‐means (FCM) clustering, and a clinically‐derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale‐ and neighborhood‐based pixel classification probabilities which are weighted by the clinically‐derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left‐right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity‐based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient‐based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel2 ROIs with and 1920 ROIs without hardware were manually selected.Results:Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively.Conclusion:Updated segmentation and centerline estimation methods in addition to new gradient‐based hardware detection software provide improved data integrity control and error‐checking for automated clinical chest image quality characterization across multiple radiography systems.
WE‐G‐204‐09: Medical Physics 2.0 in Practice: Automated QC Assessment of Clinical Chest Images
Authors: Willis C , Willis C , Nishino T , Wells J , Wilson J , Samei E 
Published: June 2015

Purpose:To determine whether a proposed suite of objective image quality metrics for digital chest radiographs is useful for monitoring image quality in our clinical operation.Methods:Seventeen gridless AP Chest radiographs from a GE Optima portable digital radiography (DR) unit (Group 1), seventeen (routine) PA Chest radiographs from a GE Discovery DR unit (Group 2), and sixteen gridless (non‐routine) PA Chest radiographs from the same Discovery DR unit (Group 3) were chosen for analysis. Groups were selected to represent “sub‐standard” (Group 1), “standard‐of‐care” (Group 2), and images with a gross technical error (Group 3). Group 1 images were acquired with lower kVp (90 vs. 125), shorter source‐to‐image distance (127cm vs 183cm) and were expected to have lower quality than images in Group 2. Group 3 was expected to have degraded contrast versus Group 2.This evaluation was approved by the institutional Quality Improvement Assurance Board (QIAB). Images were anonymized and securely transferred to the Duke University Clinical Imaging Physics Group for analysis using software previously described1 and validated2. Image quality for individual images was reported in terms of lung grey level(Lgl); lung noise(Ln); rib‐lung contrast(RLc); rib sharpness(Rs); mediastinum detail(Md), noise(Mn), and alignment(Ma); subdiaphragm‐lung contrast(SLc); and subdiaphragm area(Sa). Metrics were compared across groups.Results:Metrics agreed with published Quality Consistency Ranges with three exceptions: higher Lgl, lower RLc, and SDc. Higher bit depth (16 vs 12) accounted for higher Lgl values in our images. Values were most internally consistent for Group 2. The most sensitive metric for distinguishing between groups was Mn followed closely by Ln. The least sensitive metrics were Md and RLc.Conclusion:The software appears promising for objectively and automatically identifying substandard images in our operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.
WE‐G‐210‐01: Current Status of Supplementary Screening with Breast Ultrasound
Authors: Feig S. 
Published: June 2015

This session focuses on recent developments in breast ultrasound imaging, covering clinical practice as well as clinical and pre‐clinical research. Current status of ultrasound screening will be reviewed, followed by discussions of emerging technologies at various stages of regulatory and technical development. The session strives to provide the attendee with a view of a continuum of imaging advances starting with the present and spanning the near and long term future.Learning Objectives:

Understand current state of ultrasound screening for breast cancer
Learn about current availability of whole breast ultrasound
Appreciate the current barriers to stronger adoption of screening ultrasound
Learn about attempts to drive adoption of screening ultrasound (e.g. breast density laws)
Learn about ultrasound tomography as an emerging technique for breast imaging
Learn about the regulatory status of emerging techniques such as ultrasound tomography
Learn about the developmental status of combined 3‐D ultrasound and Mammography systems
Learn about photo‐acoustic imaging and its potential future role in breast imaging
TH‐AB‐304‐02: Fitting Grade>=2(2+) Acute Rectal Complication Rates in Prostate Cancer Patients to Lyman Kutcher Burman (LKB) and Logistic Regression NTCP Models Using Dosimetry and Patient Specific Characteristics
Authors: Liu X , Fatyga M , Li J , Schild M , Schild S , Vora S , Wong W , Wu T 
Published: June 2015

Purpose:Models of rectal toxicity which include dosimetry only are known to have a relatively low predictive power, at least as measured by the area under the ROC curve (AUC). It has been suggested that the predictive power of models can be improved by including non‐dosimetric patient specific characteristics.Methods:We compiled a database of 79 prostate patients who were treated with an IMRT technique to a dose of 77.4 Gy, 1.8Gy/fx, with an integrated boost to 81–83Gy in a sub‐volume of a prostate which was identified on a pre‐treatment MRI study. Acute grade 2+ rectal toxicities were graded according to CTCAE v4 by a physician who retrospectively reviewed patient's medical records. We modified the LKB model to include one patient specific variable at a time, and we also used an NTCP model based on logistic regression to perform multi‐variate analysis. We used patient specific variables available to us in a retrospective study: age, diabetes, hormonal treatment, Gleason Score, PSA, Statin use, prostate volume, boost volume and rectal volume.Results:Grade 2+ acute rectal toxicity occurred in 20% (16/79 patients). The LKB model with dosimetry alone gives AUC=0.65. Four variables, age, diabetes, PSA, Statin use, increase the AUC in LKB model to a maximum of 0.79. The same four variables in the logistic regression model increase AUC to 0.87. The most significant correlations are with PSA and with Statin use.Conclusion:Including patient specific variables in toxicity models can significantly increase apparent predictive power of a model. Somewhat surprising finding of a strong correlation between rectal toxicity and PSA in our dataset suggests that conclusions from each individual study should be treated with caution, until independently confirmed. Larger databases from prospective studies or meta‐analysis of multiple studies may be needed to find patient characteristics that are truly predictive.
TH‐AB‐304‐08: Computational Modeling of Risk of Radiation Pneumonitis
Authors: Logan J.V. , Trifiletti D.M. , Larner J.M. , Siebers J.V. , Watkins W.T. 
Published: June 2015

Purpose:Current normal tissue complication probability models estimate incidence of radiation pneumonitis (RP) in the treatment of lung cancer based on single parameters, e.g. mean lung dose or V20, and therefore do not capture specific variations contained in dose volume histograms (DVHs). We therefore developed an RP‐risk model which utilizes the entire DVH reasoning that such a model would be more robust for predicating radiation‐induced lung disease (RILD).Methods:A logistic regression model was developed to predict RP‐risk on each point of the cumulative DVH utilizing >1200 patient outcomes summarized in the Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) lung study. Stepwise regression including linear, quadratic, and mixed terms in dose and volume was applied to the single dose‐volume parameter‐versus‐outcome data. The model computes a relative importance at each dose‐volume point and these values were linearly combined to estimate an overall RP‐risk. A set of 80 DVHs were used to validate the algorithm prediction by comparison with an independent predictor of RP‐risk using mean lung dose.Results:The logistic regression coefficients were all statistically significant (p<0.005). The proposed model predicted RP‐risk consistently in agreement with the QUANTEC‐RP estimates computed utilizing only mean dose (average difference 0.5%±1.8%) for the 80 DVH set. Most (>70%) of the model variability occurs at doses <30 Gy for DVH levels ranging from 30%‐100%. The model is most sensitive to DVH variations in the V5‐V30 range.Conclusion:The model's predictive power is comparable to estimates from QUANTEC which use mean dose as input. Since the model conveys the relative importance of each dose‐volume level it has the potential to better predict RILD than currently available models. In order to determine if our hypothesis is correct we will compare the model to established methods including complete DVH data and clinical outcomes.This abstract was supported by the George Amorino Pilot Grants in Radiation Oncology from the Department of Radiation Oncology, Univeristy of Virginia.
TH‐AB‐304‐09: A Patient‐Specific Heterogeneous Radiobiological Model for Glioblastoma Multiforme
Authors: McGuinness C , Barani I , Noble R , Ng J 
Published: June 2015

Purpose:Gliomas tumors proliferate and invade healthy brain tissue rapidly, yielding short life expectancies. Radiation therapy is commonly used, but gliomas are known to be radio‐resistant, and almost always recur following treatment. Current radiotherapy protocols are based on the classic linear‐quadratic radiobiological model, assuming a homogeneous (one‐component) tumor. Gliomas are very heterogeneous, consisting of normoxic, hypoxic, and necrotic tissues, each responding differently to radiation. An enhanced linear‐quadratic model which takes into account the different responses of the heterogeneous tumor regions to radiation can guide treatment planning to optimize dose distributions to maximize the therapeutic effect.Methods:We used a set of differential equations to model the growth of glioma tumors. Our model expands on the one‐component model developed by (Rockne,2010) by including normoxic, hypoxic, and necrotic components and radiosensitivity values for each component. Proliferation and diffusion parameters are extracted by contouring the tumor on two sets of pre‐treatment MRIs and modeling it as a volume equivalent sphere.Results:We compared two examples of glioma tumors presented in (Rockne,2010) with our three component model and find better predictive capabilities for post‐RT tumor volumes using the three component model. In both cases, the one‐component model over predicts the effects of radiation on the tumor core, while the three‐component model more accurately fits the data by accounting for radioresistance of the hypoxic core. Additionally, we applied the model to a UCSF patient with a recurring tumor and use the model to predict time to fatal tumor burden.Conclusion:The three‐component model accurately matches tumor growth dynamics derived from MRIs for two example cases presented in literature, and one case from UCSF. Spatial information about hypoxic, necrotic, and normoxic cell densities are derived from the model providing information needed to more intelligently prescribe dose distributions tailored to a specific patient's tumor.[Rockne,et.al.,PhysMedBio,2010]
TH‐AB‐304‐12: Validation of a Morphological Xerostomia Prediction Model
Authors: Gabrys H , Buettner F , Schubert K , Mescher H , Debus J , Sterzing F , Bangert M 
Published: June 2015

Purpose:We present a validation study of a morphological NTCP model predicting dry mouth syndrome (xerostomia) based on the spatial distribution of the dose received by salivary the glands in head‐and‐neck cancer patients during radiation therapy.Methods:We examined a recently proposed model based on Bayesian multivariate logistic regression. It uses scale‐invariant moments of the three‐dimensional dose distribution to explicitly include information about the distribution of the dose within the parotid glands in different spatial directions. We tested the classification performance of this model on our independent patient cohort and compared it to routinely used clinical mean dose models. The predictive power of the models was evaluated using receiver operating characteristics (ROC).Results:The study comprised 105 head‐and‐neck cancer patients from varying tumor sites. Of these patients, 13 reported severe xerostomia between five and 48 months after radiation therapy. The AUC of the morphological model, the mean‐dose model based on the contralateral parotid gland, and the mean‐dose model based on both parotid glands were 0.79, 0.71, and 0.69 respectively. The morphological model performed better than the model based on the mean‐dose in the contralateral gland (p‐value=0.04) as well as the model based on the mean‐dose in both parotid glands (p‐value=0.05). Preliminary results regarding the underlying feature selection reconfirm the high predictive power of the second moment, i.e. the spread of the dose, in cranio‐caudal direction within the contralateral parotid gland.Conclusion:Our study confirms the benefit of spatial dose information over volumetric dose information for the prediction of xerostomia after radiation therapy. Incorporation of such morphological models during treatment planning could improve the evidence for clinical decision making and patients’ quality of life after treatment. In the future, we will explore the possibility to additionally incorporate parotid shrinkage, tumor regression, uncertainties stemming from imperfect delineations, patient positioning and organ motion.