bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐DE‐207B‐05: Measuring Spatial Resolution in Digital Breast Tomosynthesis: Update of AAPM Task Group 245
Authors: Scaduto DA , Goodsitt M , Chan H‐P , Olafsdottir H , Das M , Fredenberg E , Geiser W , Goodenough D , Heid P , Hu Y‐H , Liu B , Mainprize J , Reiser I , Van Engen R , Varchena V , Vecchio S , Glick S , Zhao W 
Published: July 2016

Purpose:Spatial resolution in digital breast tomosynthesis (DBT) is affected by inherent/binned detector resolution, oblique entry of x‐rays, and focal spot size/motion; the limited angular range further limits spatial resolution in the depth‐direction. While DBT is being widely adopted clinically, imaging performance metrics and quality control protocols have not been standardized. AAPM Task Group 245 on Tomosynthesis Quality Control has been formed to address this deficiency.Methods:Methods of measuring spatial resolution are evaluated using two prototype quality control phantoms for DBT. Spatial resolution in the detector plane is measured in projection and reconstruction domains using edge‐spread function (ESF), point‐spread function (PSF) and modulation transfer function (MTF). Spatial resolution in the depth‐direction and effective slice thickness are measured in the reconstruction domain using slice sensitivity profile (SSP) and artifact spread function (ASF). An oversampled PSF in the depth‐direction is measured using a 50 µm angulated tungsten wire, from which the MTF is computed. Object‐dependent PSF is derived and compared with ASF. Sensitivity of these measurements to phantom positioning, imaging conditions and reconstruction algorithms is evaluated. Results are compared from systems of varying acquisition geometry (9–25 projections over 15–60°). Dependence of measurements on feature size is investigated.Results:Measurements of spatial resolution using PSF and LSF are shown to depend on feature size; depth‐direction spatial resolution measurements are shown to similarly depend on feature size for ASF, though deconvolution with an object function removes feature size‐dependence. A slanted wire may be used to measure oversampled PSFs, from which MTFs may be computed for both in‐plane and depth‐direction resolution.Conclusion:Spatial resolution measured using PSF is object‐independent with sufficiently small object; MTF is object‐independent. Depth‐direction spatial resolution may be measured directly using MTF or indirectly using ASF or SSP as surrogate measurements. While MTF is object‐independent, it is invalid for nonlinear reconstructions.
WE‐DE‐207B‐11: Implementation of Size‐Specific 3D Beam Modulation Filters On a Dedicated Breast CT Platform Using Breast Immobilization
Authors: Hernandez A , Boone J 
Published: July 2016

Purpose:To implement a 3D beam modulation filter (3D‐BMF) in dedicated breast CT (bCT) and develop a method for conforming the patient's breast to a pre‐defined shape, optimizing the effects of the filter. This work expands on previous work reporting the methodology for designing a 3D‐BMF that can spare unnecessary dose and improve signal equalization at the detector by preferentially filtering the beam in the thinner anterior and peripheral breast regions.Methods:Effective diameter profiles were measured for 219 segmented bCT images, grouped into volume quintiles, and averaged within each group to represent the range of breast sizes found clinically. These profiles were then used to generate five size‐specific computational phantoms and fabricate five size‐specific UHMW phantoms. Each computational phantom was utilized for designing a size‐specific 3D‐BMF using previously reported methods. Glandular dose values and projection images were simulated in MCNP6 with and without the 3DBMF using the system specifications of our prototype bCT scanner “Doheny”. Lastly, thermoplastic was molded around each of the five phantom sizes and used to produce a series of breast immobilizers for use in conforming the patient's breast during bCT acquisition.Results:After incorporating the 3D‐BMF, MC simulations estimated an 80% average reduction in the detector dynamic range requirements across all phantom sizes. The glandular dose was reduced on average 57% after normalizing by the number of quanta reaching the detector under the thickest region of the breast.Conclusion:A series of bCT‐derived breast phantoms were used to design size‐specific 3D‐BMFs and breast immobilizers that can be used on the bCT platform to conform the patient's breast and therefore optimally exploit the benefits of the 3D‐BMF. Current efforts are focused on fabricating several prototype 3D‐BMFs and performing phantom scans on Doheny for MC simulation validation and image quality analysis.Research reported in this paper was supported in part by the National Cancer Institute of the National Institutes of Health under award R01CA181081. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institue of Health.
WE‐FG‐206‐07: Assessing the Lung Function of Patients with Non‐Small Cell Lung Cancer Using Hyperpolarized Xenon‐129 Dissolved‐Phase MRI
Authors: Qing K , Mugler J , Chen Q 
Published: July 2016

Purpose:Hyperpolarized xenon‐129 dissolved‐phase MRI is the first imaging technique that allows 3‐dimensional regional mapping of ventilation and gas uptake by tissue and blood the in human lung. Multiple outcome measures can be produced from this method. Existing studies in subjects with major lung diseases compared to healthy controls demonstrated high sensitivities of this method to pulmonary physiological factors including ventilation, alveolar tissue density, surface‐to‐volume ratio, pulmonary perfusion and gas‐blood barrier thickness. The purpose of this study is to evaluate the utility of this new imaging tool to assess the lung function in patients with non‐small cell lung cancer (NSCLC).Methods:Ten healthy controls (age: 63±10) and five patients (age: 62±13) with NSCLC underwent the xenon‐129 dissolved‐phase MRI, pulmonary function test (PFT) and CT for clinical purpose. Three outcome measures were produced from xenon‐129 dissolved‐phase MRI, including ventilation defect fraction (Vdef%) reflecting the airflow obstruction, tissue‐to‐gas ratio reflecting lung tissue density, and RBC‐to‐tissue ratio reflecting pulmonary perfusion and gas exchange.Results:Compared to healthy controls, patients with NSCLC showed more ventilation defects (NSCLC: 22±6%; control: 40±18%; P=0.01), lower tissue‐to‐gas (NSCLC: 0.82±0.31%; control: 1.07±0.13%; P=0.05) and RBC‐to‐tissue ratios (NSCLC: 0.82±0.31%; control: 1.07±0.13%; P=0.01). Maps for ventilation and gas uptake by tissue and blood were highly heterogeneous in the lungs of patients. Vdef% and RBC‐to‐tissue ratios in all 15 subjects correlated with corresponding global lung functional measures from PFT: FEV1/FVC (R=−0.91, P<0.001) and DLCO % predicted (R=0.54, P=0.03), respectively. The tissue‐to‐gas ratios correlated with tissue density (HU) measured by CT (R=0.88, P<0.001).Conclusion:With the unique ability to provide detailed information about lung function including ventilation, tissue density, perfusion and gas exchange with 3D resolution, hyperpolarized xenon‐129 dissolved‐phase MRI has high potential to be used as an important reference for radiotherapy treatment planning and for evaluating the side effects of the treatment.Receive research support and funding from Siemens.
WE‐FG‐206‐08: Pulmonary Functional Imaging Biomarkers of NSCLC to Guide and Optimize Functional Lung Avoidance Radiotherapy
Authors: Sheikh Khadija , Capaldi Dante PI , Hoover Douglas A , Palma David A , Yaremko Brian P , Parraga Grace 
Published: July 2016

Purpose:Functional lung avoidance radiotherapy promises optimized therapy planning by minimizing dose to well‐functioning lung and maximizing dose to the rest of the lung. Patients with NSCLC commonly present with co‐morbid COPD and heterogeneously distributed ventilation abnormalities stemming from emphysema, airways disease, and tumour burden. We hypothesized that pulmonary functional imaging methods may be used to optimize radiotherapy plans to avoid regions of well‐functioning lung and significantly improve outcomes like quality‐of‐life and survival. To ascertain the utility of functional lung avoidance therapy in clinical practice, we measured COPD phenotypes in NSCLC patients enrolled in a randomized‐controlled‐clinical‐trial prior to curative intent therapy.Methods:Thirty stage IIIA/IIIB NSCLC patients provided written informed consent to a randomized‐controlled‐clinical‐trial (https://clinicaltrials.gov/ct2/show/NCT02002052) comparing outcomes in patients randomized to standard or image‐guided radiotherapy. Hyperpolarized noble gas MRI ventilation‐defect‐percent (VDP) (Kirby et al, Acad Radiol, 2012) as well as CT‐emphysema measurements were determined. Patients were stratified based on quantitative imaging evidence of ventilation‐defects and emphysema into two subgroups: 1) tumour‐specific ventilation defects only (TSD), and, 2) tumour‐specific and other ventilation defects with and without emphysema (TSDVE). Receiver‐operating‐characteristic (ROC) curves were used to characterize the performance of clinical measures as predictors of the presence of non‐tumour specific ventilation defects.Results:Twenty‐one out of thirty subjects (70%) had non‐tumour specific ventilation defects (TSDVE) and nine subjects had ONLY tumour‐specific defects (TSD). Subjects in the TSDVE group had significantly greater smoking‐history (p=.006) and airflow obstruction (FEV1/FVC) (p=.001). ROC analysis demonstrated an 87% classification rate for smoking pack‐years, 90% for FEV1/FVC, and 56% for tumour RECIST measurements for identifying patients with non‐tumour and tumour‐specific ventilation abnormalities.Conclusion:70% of NSCLC patients had ventilation abnormalities stemming from emphysema, airways disease and tumour burden. Smoking‐history and airflow obstruction, but not RECIST, identified NSCLC patients with ventilation abnormalities appropriate for functional lung avoidance therapy.
WE‐FG‐206‐10: Portal Venous Perfusion Quantitation From Liver DCE‐MRI by Voxel Uptake Curve Clustering and Input Function Normalization
Authors: Johansson A , Balter J , Feng M , Cao Y 
Published: July 2016

Purpose:Portal venous perfusion (PV) quantified from liver DCE‐MRI by a dual‐input single‐compartment model is sensitive to errors in the PV input function (PVIF) due to partial volume effect and respiratory motion. To improve repeatability of the local PV perfusion estimate and reduce computation complexity, we developed and evaluated an approach that combines dynamic curve clustering and parameter estimation with normalization of the PVIF.Methods:The PVIF and arterial input function (AIF), after initial transient behaviors, should reach a state where the contrast concentrations in both blood pools are the same. The PVIF was therefore rescaled such that its tail matched that of the AIF. Also, to reduce the motion effect on the PV perfusion images, voxels with similar contrast uptake curves were pooled together by a clustering algorithm, and then the perfusion parameters of the dynamic curves of the cluster centers were estimated. Perfusion maps were created based upon probabilities of a voxel belonging to the clusters and the perfusion parameters of the clusters. The proposed method was evaluated for repeatability of local PV perfusion in normal tissue for 8 patients with repeated examinations, and by the Pearson correlation coefficient of mean PV perfusion of the whole liver to an independent global liver function measure using ICG clearance rate analyzed over 89 examinations.Results:The average PV perfusion RMSE of the repeated measurements was reduced from 52.4 by the conventional voxelwise estimation to 33.7 ml/(100 ml*min) by the proposed method. Similarly, the correlation coefficient of the total perfusion to ICG clearance rate was increased from 0.21 to 0.46 by the new method.Conclusion:This technique shows promise in improving repeatability of PV perfusion from DCE‐MRI of the liver. Future developments have the potential to further reduce systematic and random errors in the PV perfusion quantification, and computational complexity.NIH RO1 CA132834, NIH PO1 CA59827
WE‐FG‐207B‐02: Material Reconstruction for Spectral Computed Tomography with Detector Response Function
Authors: Liu J , Gao H 
Published: July 2016

Purpose:Different from the conventional computed tomography (CT), spectral CT based on energy‐resolved photon‐counting detectors is able to provide the unprecedented material composition. However, an important missing piece for accurate spectral CT is to incorporate the detector response function (DRF), which is distorted by factors such as pulse pileup and charge‐sharing. In this work, we propose material reconstruction methods for spectral CT with DRF.Methods:The polyenergetic X‐ray forward model takes the DRF into account for accurate material reconstruction. Two image reconstruction methods are proposed: a direct method based on the nonlinear data fidelity from DRF‐based forward model; a linear‐data‐fidelity based method that relies on the spectral rebinning so that the corresponding DRF matrix is invertible. Then the image reconstruction problem is regularized with the isotropic TV term and solved by alternating direction method of multipliers.Results:The simulation results suggest that the proposed methods provided more accurate material compositions than the standard method without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity.Conclusion:We have proposed material reconstruction methods for spectral CT with DRF, whichprovided more accurate material compositions than the standard methods without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity.Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).
WE‐FG‐207B‐03: Multi‐Energy CT Reconstruction with Spatial Spectral Nonlocal Means Regularization
Authors: Li B , Shen C , Ouyang L , Yang M , Zhou L , Jiang S , Jia X 
Published: July 2016

Purpose:Multi‐energy computed tomography (MECT) is an emerging application in medical imaging due to its ability of material differentiation and potential for molecular imaging. In MECT, image correlations at different spatial and channels exist. It is desirable to incorporate these correlations in reconstruction to improve image quality. For this purpose, this study proposes a MECT reconstruction technique that employes spatial spectral non‐local means (ssNLM) regularization.Methods:We consider a kVp‐switching scanning method in which source energy is rapidly switched during data acquisition. For each energy channel, this yields projection data acquired at a number of angles, whereas projection angles among channels are different. We formulate the reconstruction task as an optimziation problem. A least square term enfores data fidelity. A ssNLM term is used as regularization to encourage similarities among image patches at different spatial locations and channels. When comparing image patches at different channels, intensity difference were corrected by a transformation estimated via histogram equalization during the reconstruction process.Results:We tested our method in a simulation study with a NCAT phantom and an experimental study with a Gammex phantom. For comparison purpose, we also performed reconstructions using conjugate‐gradient least square (CGLS) method and conventional NLM method that only considers spatial correlation in an image. ssNLM is able to better suppress streak artifacts. The streaks are along different projection directions in images at different channels. ssNLM discourages this dissimilarity and hence removes them. True image structures are preserved in this process. Measurements in regions of interests yield 1.1 to 3.2 and 1.5 to 1.8 times higher contrast to noise ratio than the NLM approach. Improvements over CGLS is even more profound due to lack of regularization in the CGLS method and hence amplified noise.Conclusion:The proposed ssNLM method for kVp‐switching MECT reconstruction can achieve high quality MECT images.
WE‐FG‐207B‐06: Plaque Composition Measurement with Dual Energy Computed Tomography
Authors: Wang C , Ding H , Malkasian S , Molloi S 
Published: July 2016

Purpose:To investigate the feasibility of characterizing arterial plaque composition in terms of water, lipid and protein or calcium using dual energy computed tomography. Characterization of plaque composition can potentially help distinguish vulnerable from stable plaques.Methods:Simulations studies were performed by the CT simulator based on ASTRA tomography toolbox. The beam energy for dual energy images was selected to be 80 kVp and 135 kVp. The radiation dose and energy spectrum for the CT simulator were carefully calibrated with respect to a 320‐slice CT scanner. A digital chest phantom was constructed using Matlab for calibration and plaque measurement. Pure water, lipid, protein or calcium was used for calibration and a mixture of different volume percentages of these materials were used for validation purposes. Non‐calcified plaque was simulated using water, lipid and protein with volumetric percentage range of 35%∼65%, 5%∼60% and 5%∼40%, respectively. Calcified plaque was simulated using water, lipid and calcium with volumetric percentage range of 50%∼80%, 8%∼45% and 3%∼13%, respectively. We employed iterative sinogram processing (ISP) to reduce the beam hardening effect in the simulation to improve the decomposition results.Results:The simulated known composition and dual energy decomposition results were in good agreement. Water, lipid and protein (calcium) mixtures were decomposed into water, lipid and protein (calcium) contents. The RMS errors of volumetric percentage for the water, lipid and protein (non‐calcified plaque) decomposition, as compared to known values, were estimated to be approximately 5.74%, 2.54%, and 0.95% respectively. The RMS errors of volumetric percentage for the water, lipid and Calcium (calcified plaque) decomposition, as compared to known values, were estimated to be approximately 7.4%, 8.64%, and 0.08% respectively.Conclusion:The results of this study suggest that the dual energy decomposition can potentially be used to quantify the water, lipid, and protein or calcium composition of a plaque with relatively good accuracy.Grant funding from Toshiba Medical Systems and Philips Medical Systems
WE‐H‐BRB‐00: Big Data in Radiation Oncology
Authors: Benedict Stanley 
Published: July 2016

Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future SuccessThe overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives.This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis.

Learning Objectives:
To discuss current and future sources of big data for use in radiation oncology research
To optimize our current data collection by adopting new strategies from outside radiation oncology
To determine what new knowledge big data can provide for clinical decision support for personalized medicineL. Xing, NIH/NCI Google Inc.
WE‐H‐BRB‐01: Overview of the ASTRO‐NIH‐AAPM 2015 Workshop On Exploring Opportunities for Radiation Oncology in the Era of Big Data
Authors: Benedict S. 
Published: July 2016

Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future SuccessThe overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives.This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis.

Learning Objectives:
To discuss current and future sources of big data for use in radiation oncology research
To optimize our current data collection by adopting new strategies from outside radiation oncology
To determine what new knowledge big data can provide for clinical decision support for personalized medicineL. Xing, NIH/NCI Google Inc.
WE‐H‐BRB‐02: Where Do We Stand in the Applications of Big Data in Radiation Oncology?
Authors: Xing L. 
Published: July 2016

Big Data in Radiation Oncology: (1) Overview of the NIH 2015 Big Data Workshop, (2) Where do we stand in the applications of big data in radiation oncology?, and (3) Learning Health Systems for Radiation Oncology: Needs and Challenges for Future SuccessThe overriding goal of this trio panel of presentations is to improve awareness of the wide ranging opportunities for big data impact on patient quality care and enhancing potential for research and collaboration opportunities with NIH and a host of new big data initiatives.This presentation will also summarize the Big Data workshop that was held at the NIH Campus on August 13–14, 2015 and sponsored by AAPM, ASTRO, and NIH. The workshop included discussion of current Big Data cancer registry initiatives, safety and incident reporting systems, and other strategies that will have the greatest impact on radiation oncology research, quality assurance, safety, and outcomes analysis.

Learning Objectives:
To discuss current and future sources of big data for use in radiation oncology research
To optimize our current data collection by adopting new strategies from outside radiation oncology
To determine what new knowledge big data can provide for clinical decision support for personalized medicineL. Xing, NIH/NCI Google Inc.