|
| | BReP-SNAP-I-1 : 4D-AirNet: A 4D CBCT Image Reconstruction Method Synergizing Analytical Method, Iterative Method, and Deep Learning
G.Chen*, Q.Huang, E.Elder, T.Liu, H.Gao |
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| | BReP-SNAP-I-2 : Accurate CT Reconstruction Via Derivative Backprojection Filtration for Circular Scans with Non-Uniform Data Redundancy
X.Jiang*, L.Zhu |
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| | BReP-SNAP-I-3 : Algorithm for Optimization of the X-Ray Beam and Filter Parameters in Dual-Energy Imaging Systems
I.Romadanov*, M.Sattarivand |
|
| | BReP-SNAP-I-4 : An Auto-Accessing Method for Reducing the Reading Time of Digital Breast Tomosynthesis with a Synthetic Mammogram
H.Kim*, S.Cho |
|
| | BReP-SNAP-I-5 : An Empirical Comparison of Weka Classifiers for Outcome Prediction Using An Imaging Habitats Definition and Feature Extraction Method On MRI
Q.Han*, R.Palm, K.Latifi, E.Moros, A.Naghavi, G.Zhang |
|
| | BReP-SNAP-I-6 : Assessment of Flying-Focal Spot in MPR Images of High-Frequency Objects at Various Distances for the Isocenter
R.Al-Senan*, K.Brown, S.King |
|
| | BReP-SNAP-I-7 : Assessment of the Perfusion Variation After Radiotherapy for Brain Metastasis Using MR 3D ASL Perfusion Imaging
C.Hou*, G.Gong, Y.Yin |
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| | BReP-SNAP-I-8 : B0 Variation From Ferromagnetic Gantry Rotation
H.Gach*, A.Curcuru, D.Yang, O.Green, S.Mutic, T.Kim |
|
| | BReP-SNAP-I-9 : Building a Patient-Specific Model Using Transfer Learning for 4D-CBCT Augmentation
L.Sun*, Y.Chang, Z.Jiang, L.Ren |
|
| | BReP-SNAP-I-10 : Classification of Optical Coherence Tomography Images Using Deep Neural Networks
J.Kotoku*, T.Tsuji, Y.Hirose, K.Fujimori, T.Hirose, A.Oyama,
Y.Saikawa, T.Mimura, K.Shiraishi, T.Kobayashi, A.Mizota |
|
| | BReP-SNAP-I-11 : Comparison of a Deep Learning-Based CT Reconstruction Algorithm (AiCE) to Other Reconstruction Techniques in a Pediatric Population
S.Brady*, E.Somasundaram, J.Dillman, A.Trout |
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| | BReP-SNAP-I-12 : Cone-Beam CT Image Reconstruction with Spherical Harmonics
T.Shimomura*, A.Haga |
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| | BReP-SNAP-I-13 : Dedicated Breast CT: Comparative Evaluation of Multi-Scale Residual Dense Network and Residual Encoder-Decoder Network for Deep Learning-Driven Sparse-View Reconstruction
Z.Fu, H.Tseng, S.Vedantham*, A.Karellas, A.Bilgin |
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| | BReP-SNAP-I-14 : Deep Learning with Adaptive Hyper-Parameters for Image Reconstruction in Low-Dose CT
Q.Ding*, H.Gao, H.Ji |
|
| | BReP-SNAP-I-15 : Design and Manufacture of Anatomically Realistic, Actuated, Elastic Lung Inserts for PET/CT Phantom Studies with Respiratory Motion
D.Black*, Y.Oloumi, J.Wong, R.Fedrigo, C.Uribe-munoz, D.Kadrmas,
A.Rahmim, I.Klyuzhin |
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| | BReP-SNAP-I-16 : Dosimetric Uncertainties Associated with High Z-Materials in Dose Calculation Algorithms for Therapeutic High Energy Proton Beams
I.Ali*, N.Alsbou, S.Ahmad |
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| | BReP-SNAP-I-17 : Dynamic Range Reducer for C-Arm Cone-Beam CT Acquisitions: Initial Prototype and Evaluation
H.Zhang*, N.Bennett, S.Hsieh, K.Mueller, R.Fahrig, A.Maier,
M.Levenston, G.Gold, A.Wang |
|
| | BReP-SNAP-I-18 : Electronic X-Ray Field Alignment Detection
E.mckenzie*, I.Rutel |
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| | BReP-SNAP-I-19 : Estimation of Tumor Tracer Kinetics Employing a Novel Cross Voxel Exchange Model
N.Sinno*, E.Taylor, M.Milosevic, D.Jaffray, C.Coolens |
|
| | BReP-SNAP-I-20 : Evaluation of CT-Based Radiomics Features for Predicting Parameters Measured Using a Pulmonary Function Test
Y.Ieko*, N.Kadoya, K.Abe, S.Tanaka, H.Takagi, T.Kanai,
K.Ichiji, T.Yamamoto, H.Ariga, K.Jingu |
|
| | BReP-SNAP-I-22 : Imaging the Metabolic Evolution of Glioblastoma Throughout Tumor Regression Following Radiotherapy to the Point of Relapse with Hyperpolarized Magnetic Resonance Imaging
T.Salzillo*, J.Gumin, J.Lee, N.Zacharias, F.Lang, P.Bhattacharya |
|
| | BReP-SNAP-I-23 : Impact of a High Power Tube On Optimal KVp Selection in Function of Patient Size
G.Van Gompel*, N.Buls, H.Nieboer, J.De Mey |
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| | BReP-SNAP-I-24 : Implementation of CT Protocol Management Software to Detect Deviations From Master Protocols
K.Little*, J.Jacobs, N.Fitousi, M.Robins, J.Carpenter, A.Rupe,
D.Hintenlang |
|
| | BReP-SNAP-I-25 : Implementing a Phase Space File Framework in MCGPU
R.Trevisan Massera*, R.Thomson, A.Tomal |
|
| | BReP-SNAP-I-26 : Improving CBCT Image Quality for Obese Patients Using Unified Scatter Rejection and Correction Method
Y.Park, B.Miller, B.Kavanagh, M.Miften, C.Altunbas* |
|
| | BReP-SNAP-I-27 : In Vivo Hypoxia Monitoring Via Magnetic Resonance Imaging for Mouse Models
N.Virani*, A.Protti, J.Kwon, R.Berbeco |
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| | BReP-SNAP-I-28 : Integrated Intensity-Based Quantification of Small Airway Dimensions Using Computed Tomography
Y.Zhao, S.Molloi* |
|
| | BReP-SNAP-I-29 : Investigating Using Water Inversion Recovery (IR) to Resolve the Olefinic Resonance From Water in Breast In Vivo with Magnetic Resonance Spectroscopy at 3T
C.Fallone*, A.Yahya |
|
| | BReP-SNAP-I-30 : Investigation of the NPS as a Tool for Routine Quality Control of Digital X-Ray Systems
M.Ghorbanzade*, I.Elbakri |
|
| | BReP-SNAP-I-31 : Machine Learning-Based Prediction of Contrast Enhancement in Transcatheter Aortic Valve Replacement CT
E.Macdonald*, Z.Qi, N.Bevins |
|
| | BReP-SNAP-I-32 : Material Decomposition of X-Ray Radiography for Accurate Target Tracking and Patient Setup Using Deep Neural Network
Y.Lin*, D.Lam, S.Zhou, Y.Tan, Q.Chen, B.Sun,
T.Zhang |
|
| | BReP-SNAP-I-33 : Maximum Likelihood-Based Charge Sharing Correction for Spectroscopic X-Ray Detectors
R.Lalonde*, K.Iniewski, J.Tanguay |
|
| | BReP-SNAP-I-34 : Noise Power Spectrum Analysis
E.mckenzie*, D.Gauntt |
|
| | BReP-SNAP-I-35 : Orthogonal Limited Arc Scan Combinations of Cone-Beam CT Reconstructed Iteratively to Reduce Photon Starvation Artifacts Caused by Pedicle Screws
M.Hermansen*, S.Banks, M.Arreola, A.Entezari, F.Bova |
|
| | BReP-SNAP-I-36 : Parameterizing Size-Based Variations in CT Number
S.Rose*, J.Ruyle, T.Szczykutowicz |
|
| | BReP-SNAP-I-37 : Parametric Model Adjustment of Prescribed Mean Radiation Dose to Ensure Complete Coverage at Tumor Margins During Ablative 90Y Radioembolization
S.Kappadath*, B.Lopez, A.Braat, R.DiTusa, A.Mahvash, B.Toskich |
|
| | BReP-SNAP-I-38 : Patient Dose Estimates After Lactated Ringer's Related Breakthrough of a Rubidium-82 Generator
N.Busse*, B.Lofton, J.Stickel |
|
| | BReP-SNAP-I-39 : Phantom-Guided Optimization of BSREM Reconstruction Parameters Using Shell-Less Radioactive Epoxy Lesions for [18F]F-DCFPyL Prostate Cancer Imaging
R.Fedrigo*, D.Kadrmas, P.Edem, F.Bénard, A.Rahmim, C.Uribe-munoz |
|
| | BReP-SNAP-I-40 : Predicting Tumor Displacement From Intraoperative Magnetic Resonance Imaging Using Viscoelastic Finite Element Biomechanical Modelling
A.Lesage*, M.Chen, A.Sen, G.Cazoulat, J.Weinberg, K.Brock |
|
| | BReP-SNAP-I-41 : Preoperative Non-Invasive Grading of Parotid Gland Cancer Malignancy Using Radiomic MR Features
H.Kamezawa*, H.Arimura, R.Yasumatsu, K.Ninomiya |
|
| | BReP-SNAP-I-42 : Quantitative Comparison of Noise Texture in Gemstone Spectrum Imaging CT Images Reconstructed Using Filtered Back-Projection (FBP), Iterative Reconstruction, and Deep Learning Techniques
J.Tang*, B.Nett, P.Prakash |
|
| | BReP-SNAP-I-43 : Reducing the Number of Projections in CT Imaging Using Domain-Transform Manifold Learning
A.Cramer*, N.Koonjoo, B.Zhu, R.Gupta, M.Rosen |
|
| | BReP-SNAP-I-44 : Scatter Correction of Sparsely Acquired 4D Cone-Beam CT by Bayesian Monte Carlo Extrapolation
S.Blake*, O.Dillon, R.O'Brien |
|
| | BReP-SNAP-I-45 : Segmented Multislice Acquisition for Motion-Insensitive Super Resolution Multislice T2-Weighted Fast-Spin-Echo Imaging
S.Kargar*, E.Borisch, A.Froemming, R.Grimm, A.Kawashima, B.King,
E.Stinson, S.Riederer |
|
| | BReP-SNAP-I-46 : Self-Supervised Metal Artifact Reduction in X-Ray Computed Tomography by Joint Sinogram Completion and Image Refinement
L.Yu*, Z.Zhang, L.Xing |
|
| | BReP-SNAP-I-47 : Spatial Correlation of Radiomics Features with Segmentation Errors of PET-Based Tumor Contours in the Lung
F.Yang* |
|
| | BReP-SNAP-I-48 : Spectral Inconsistency Analysis On a CdTe Photon-Counting Detector
B.Qi*, H.Gao |
|
| | BReP-SNAP-I-49 : Study of Human Placenta Function Using MR Based Blood Oxygen Level Dependent (BOLD) Imaging
Y.Zhou*, Y.Le, R.Kedar, A.Odibo |
|
| | BReP-SNAP-I-50 : Study On Beam Characteristics of Flat Panel X-Ray Source Based On Monte Carlo Method
Z.Ding* |
|
| | BReP-SNAP-I-51 : T2-Relaxation-Diffusion Correlation Analysis for Prediction of Progression Free Survival (PFS) in Glioblastoma
Y.Li*, M.Kim, T.Lawrence, H.Parmar, Y.Cao |
|
| | BReP-SNAP-I-52 : TG116 Exposure Index Calibration: Measured Variation as a Function of Beam Quality
B.Luckett*, V.Garcia, D.Gauntt, I.Rutel |
|
| | BReP-SNAP-I-53 : Two-Stage Generative Adversarial Network (GAN) for Image-Based Metal Artifact Reduction
H.Kim*, C.Kim, B.Cho, J.Kim |
|
| | BReP-SNAP-I-54 : Universal Orbits for Metal Artifact Elimination
G.Gang, T.Russ, Y.Ma, C.Toennes, L.Schad, C.Weiss,
T.Ehtiati, J.Siewerdsen, J.Stayman* |
|
| | BReP-SNAP-I-55 : Using Exposure Index to Develop Thickness-Based Technique Chart for Cross-Table Lateral Hip Radiography
C.Topbas*, G.Fong, V.Singh, K.Hulme, X.Li |
|
| | BReP-SNAP-I-56 : Using Multiple Cylindrical Halbach Rings in Magnetic Particle Imaging
M.Ergor, A.Olamat, N.Dogan, A.Bingolbali* |
|
| | BReP-SNAP-I-57 : Validation of Linear Velocity Independence of Rotating Fan Blade Method of Temporal MTF Estimation
M.Russ*, S.Mann, T.Richards, E.Samei |
|
| | BReP-SNAP-I-58 : Variations in Radiomics Features of a Multi-Texture Phantom Introduced by Deep Learning Iterative Reconstruction Algorithms
N.Baughan*, J.Cruz Bastida, H.Al-Hallaq, I.Reiser |
|
| | BReP-SNAP-I-59 : Reconstructing C-Arm Cone-Beam CT Knee Scans Using An Open-Source GPU-Based Toolbox
H.Zhang*, K.Mueller, R.Fahrig, A.Maier, M.Levenston, G.Gold,
A.Wang |
|
| | BReP-SNAP-I-60 : Low-Dose CT with Deep Learning Regularization Via Proximal Forward Backward Splitting
Q.Ding*, G.Chen, H.Ji, H.Gao |
|
| | BReP-SNAP-I-61 : Range Modulated Proton Radiographic Imaging Using Pencil Beams From the Spot Scanning MEVION Proton Therapy System
C.Pelas*, N.Alsbou, S.Ahmad, I.Ali |
