| Novel Detection Technologies |
TU-D-KDBRB1-2: Imaging for Particle Therapy
First Demonstration of a Prompt Gamma Slit-Camera for in Vivo Proton Range Verification Based On Semiconductor Detectors |
Hui Wang |
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TU-D-KDBRB1-3: Imaging for Particle Therapy
Comparison of Acoustic Detection Methods for Proton Range Verification |
Wei Nie |
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TU-D-KDBRB1-6: Imaging for Particle Therapy
Proton Range Verification with Submillimeter Precision Using a Full-Scale Prototype Prompt Gamma-Ray Spectroscopy System |
Joost Verburg |
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| Artifact and Noise Mitigation |
TU-AB-202-2: Breast Imaging
BEST IN PHYSICS (IMAGING): Evaluation of An Automated Grid Artifact Detection System for Quality Control in Digital Mammography |
Christopher MacLellan |
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TU-AB-202-4: Breast Imaging
Back-Projection Filtration Image Reconstruction Approach for Reducing High-Density Object Artifacts in Digital Breast Tomosynthesis |
Hyeongseok Kim |
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TU-AB-202-9: Breast Imaging
Reconstruction Algorithm Design for Mitigating the Orientation Dependent Conspicuity of Fiber-Like Signals in Digital Breast Tomosynthesis |
Sean Rose |
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TU-K-DBRB-3: Quantitative Imaging: CT and MRI
Application and Performance Evaluation of a Modified BM3D Denoising Method to Emphysema Scoring On Ultra-Low-Dose CT Images |
Tingting Zhao |
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| Emerging Modalities and Platforms |
TU-GH-KDBRC-8: Advances in Imaging Technology
An Endoscopic 3-D OCT Imaging System for Detection of Early Stage Pancreatic Cancer |
Lanchun Lu |
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TU-K-207-6: Multimodality, Optical Imaging, and Ultrasound
Photoacoustic CT to Characterize Acute and Chronic Hypoxia |
Devin Miles |
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| Functional Imaging |
TU-D-202-3: Nuclear Medicine
GPET: An Efficient and Accurate Simulation Tool Via GPU-Based Monte Carlo for PET |
Yujie Chi |
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TU-GH-KDBRA1-8: Radiobiology: Experiments and Modeling
Theranostics of Murine Breast Cancer with a 86/90Y-Labeled Antibody |
Emily Ehlerding |
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TU-GH-KDBRA1-9: Radiobiology: Experiments and Modeling
Antibody and Fragment-Based PET Imaging of CTLA-4+ T-Cells in Humanized Mouse Models |
Emily Ehlerding |
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TU-K-207-7: Multimodality, Optical Imaging, and Ultrasound
Removing Noise Bias in Photoacoustic-Based SO2 Estimates: A Simple Empirical SNR-Adaptive Thresholding Approach |
Diego Sampaio |
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| Informing Therapy with Biology |
TU-GH-KDBRA1-1: Radiobiology: Experiments and Modeling
Double Strand Break (DSB) Complexity and Proximity Effects Within the Repair-Misrepair Fixation (RMF) Model for Improved Predictions of Cell Survival From Heavy Ions |
Michael Butkus |
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TU-GH-KDBRA1-2: Radiobiology: Experiments and Modeling
Interstitial Diffuse Optical Probe with Optical Fiber Spectroscopy to Measure Dynamic Tumor Hypoxia |
Leonard Che Fru |
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TU-GH-KDBRA1-5: Radiobiology: Experiments and Modeling
Oxygen Microbubbles Transiently Relieve Tumor Hypoxia and May Improve Radiation Therapy Tumor Control |
Sha Chang |
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TU-GH-KDBRA1-10: Radiobiology: Experiments and Modeling
Role of Double Strand Break DNA Repair Deficiency On the Sensitivity of Cells to Therapeutic Proton Beams |
Scott Bright |
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| Precision in Treatment Delivery |
TU-GH-KDBRA1-11: Radiobiology: Experiments and Modeling
BEST IN PHYSICS (THERAPY): Enhanced Drug Delivery by Nanoparticle and Radiation-Mediated Tumor Vascular Modulation |
Sijumon Kunjachan |
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TU-K-KDBRC-7: Dose Calculation, Delivery and Measurement Techniques
Using Dual Step Wedge and 2D Scintillator to Achieve Highly Precise and Robust Proton Range Quality Assurance |
Wei Deng |
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| New Approaches to Quantify Dose |
TU-K-KDBRC-2: Dose Calculation, Delivery and Measurement Techniques
An Energy-Adaptive Finite Element Angular Discretization Towards a Fast Deterministic Dose Calculation in Magnetic Fields |
Joel St-Aubin |
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TU-K-KDBRC-4: Dose Calculation, Delivery and Measurement Techniques
BEST IN PHYSICS (THERAPY): Entropic Model for Real-Time Dose Calculation |
Gabriele Birindelli |
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TU-K-KDBRC-8: Dose Calculation, Delivery and Measurement Techniques
Aerrow: A Probe-Format Graphite Calorimeter for Absolute Dosimetry of MR-Guided Radiotherapy Modalities |
James Renaud |
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| Real-Time Treatment Guidance |
TU-AB-205-3: X-Ray Based Imaging for Therapy Guidance
Probabilistic Decomposition of X-Ray Image Sequence to Extract Obscure Target Objects for Monitoring Intrafractional Organ Motion |
Masahiro Shindo |
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TU-D-KDBRB1-1: Imaging for Particle Therapy
Instantaneous Full Field Proton Radiography for Image Guidance |
Matthew Freeman |
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| Response Assessment |
TU-GH-KDBRC-10: Advances in Imaging Technology
Advances in Imaging of Radiation Therapy Response in Soft Tissue Sarcomas Using Magnetic Resonance Elastography (MRE) and Dynamic Contrast-Enhanced (DCE)-MRI |
Kay Pepin |
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TU-D-DBRB-3: Quantitative Imaging: Ultrasound, CT, and PET/CT Applications
Combined Morphologic and Metabolic Pipeline for Treatment Response Evaluation - a Pilot Study On Pancreatic Adenocarcinoma Patients |
Yi Lao |
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| Prediction and Classification |
TU-K-207-1: Multimodality, Optical Imaging, and Ultrasound
Early Prediction of Locoregional Recurrence in Head & Neck Cancer After Radiation Therapy Through Multifaceted Radiomics |
Zhiguo Zhou |
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TU-AB-DBRB-6: Quantitative Imaging: Translation to Practice
Could Radiomic Signature Developed From NSCLC Patients Predict Overall Survival of Patients with ALK+ Mutation? |
Lyu Huang |
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TU-AB-DBRB-7: Quantitative Imaging: Translation to Practice
CT-Based Radiomic Analysis for Prediction of Liver Progression Risk in Hepatocellular Carcinoma Patients Treated with Stereotactic Body Radiation Therapy |
Lise Wei |
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TU-K-202-2: Computed Tomography I
A Radiomics Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors |
Pengfei Yang |
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| Artificial Intelligence and Machine Learning |
TU-AB-DBRB-5: Quantitative Imaging: Translation to Practice
Automatic Segmentation of the Prostate Gland On Planning CT Images Using Deep Neural Networks (DNN) |
Chang Liu |
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TU-D-DBRB-5: Quantitative Imaging: Ultrasound, CT, and PET/CT Applications
Automatic Liver and Tumor Segmentation Using Hierarchical Convolutional-Deconvolutional Neural Networks with Jaccard Distance |
Yading Yuan |
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TU-K-202-4: Computed Tomography I
Automated Segmentation of Malignant Pleural Mesothelioma Tumor On Computed Tomography Scans Using Deep Convolutional Neural Networks |
Eyjolfur Gudmundsson |
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