|
| | BReP-SNAP-M-1 : 3D Dose Predictions and Plan Quality Assessment in MRI Guided Online Plan Adaptation Using Artificial Neural Network (ANN) Models
D.Yang*, A.Thomas, Y.Fu, H.Gach, H.Li |
|
| | BReP-SNAP-M-2 : 3D Intrafraction Assessment of Pelvis and Abdominal Gastrointestinal Peristalsis in MR-Guided Online Adaptive Radiotherapy (MRgoART)
K.Mittauer*, R.Herrera, M.Chuong, T.Romaguera, D.Alvarez, D.Doty,
J.Bryant, A.Bone, R.Kotecha, M.Hall, N.Kalman,
M.Mehta, J.Contreras, A.Gutierrez |
|
| | BReP-SNAP-M-3 : A Comparison of Convolutional Neural Networks and Logistic Regression for the Detection of Vertebral Body Misalignments During Radiation Therapy
R.Petragallo*, J.Lamb |
|
| | BReP-SNAP-M-4 : A Comprehensive Framework for Radiotherapy Treatment Plan Quality Evaluation in Large Multiple Institution Dataset
L.Yuan*, R.Kapoor, W.Sleeman, M.Hagan, J.Palta, M.Rosu-Bubulac |
|
| | BReP-SNAP-M-6 : A Deep Learning Model to Predict a Diagnosis of MCI by Using Static and Dynamic Brain Connectomics
D.Cui, J.Jin, Z.Liu, T.Yin* |
|
| | BReP-SNAP-M-7 : A Depthwise Separable Convolution Neural Network for Survival Prediction of Head & Neck Cancer
R.Li*, A.Das, N.Bice, P.Rad, A.Roy, N.Kirby,
N.Papanikolaou |
|
| | BReP-SNAP-M-8 : A Method to Estimate Accumulated Dose Uncertainties Induced by Deformable Image Registration Discrepancies for Adaptive Proton Therapy
F.Amstutz*, L.Nenoff, C.Ribeiro, F.Albertini, A.Knopf, D.Weber,
A.Lomax, Y.Zhang |
|
| | BReP-SNAP-M-9 : A Method to Predict the Patient-Specific Dose-Volume Histogram Curves for Radiotherapy Planning with Deep Learning
X.Chen*, K.Men, J.Yi, J.Dai |
|
| | BReP-SNAP-M-10 : A Multi-Phase Cross-Modality Deformable Bio-Tissue Phantom for Deformable Image Registration Validation
A.Qin*, M.Snyder, M.Liu, X.Ding, W.Zheng, Q.Liu,
S.Chen, J.Liang, D.Yan |
|
| | BReP-SNAP-M-11 : A Novel Kernel-Weighted Back-Projection Reconstruction Algorithm for Compton Camera Imaging
R.Panthi*, D.Mackin, S.Peterson, P.Maggi, J.Polf, S.Beddar |
|
| | BReP-SNAP-M-12 : A Novel Multi-Path 3D Dense-UNet for Improving Automatic Segmentation of the Glioblastoma GTV On Multi-Modal MR Images
J.Lewis*, K.Singhrao, Y.Yang, X.Qi, D.Ruan, J.Fu |
|
| | BReP-SNAP-M-13 : A Novel Real-Time Markerless Target Tracking Pipeline Based On Faster R-CNN for Lung Cancer Radiotherapy
L.Deng*, Z.Dai, X.Liang, H.Zhao, H.Quan, Y.Xie |
|
| | BReP-SNAP-M-14 : A Patient Risk Model to Determine the Optimal Frequency of Quality Control for Radiotherapy Machines
M.Ma*, J.Dai |
|
| | BReP-SNAP-M-15 : An Adversarial Machine Learning Framework and Biomechanical Model Guided Approach for Generating 3D Lung Tissue Elasticity From Low Dose End-Exhalation CT
A.Santhanam, B.Stiehl*, M.Lauria, I.Barjaktarevic, S.Hsieh, D.Low |
|
| | BReP-SNAP-M-16 : Assessing Inter and Intrafraction Target Motion in Lung SBRT Using Deformable Image Registration
J.Liang*, D.Lack, Q.Liu, R.Sandhu, L.Benedetti, I.Grills,
C.Stevens, D.Yan |
|
| | BReP-SNAP-M-17 : Assessing Longitudinal CT Perfusion Changes in Pancreas and Pelvic Node Tumors Treated with SBRT On Prospective Phase I Dose Escalation Trials
T.Patton*, A.Santoso, T.Reinicke, Y.Vinogradskiy, Q.Diot, C.Fisher,
K.Goodman, B.Jones |
|
| | BReP-SNAP-M-18 : Assessment of Texture Feature Robustness Using a Novel 3D-Printed Phantom
K.Spuhler*, J.Teruel, P.Galavis |
|
| | BReP-SNAP-M-19 : Association of Lung CT Voxel-Based Radiomics Feature Map with Galligas PET Lung Ventilation Imaging
Z.Yang*, K.Lafata, X.Chen, Y.Chang, F.Yin |
|
| | BReP-SNAP-M-20 : Attention-Based Mask Regional Convolutional Neural Network for Delineation of Multiple Organs in Head-And-Neck MRI
X.Dai*, Y.Lei, Y.Fu, T.Wang, J.Zhou, M.McDonald,
W.Curran, T.Liu, X.Yang |
|
| | BReP-SNAP-M-21 : Automatic CT Segmentation for Radiotherapy Treatment Planning: How Good Is Good Enough?
W.Ingram*, L.Dong |
|
| | BReP-SNAP-M-22 : Automatic Detection of Dental Artifact in a Fully-Automated Treatment Planning Workflow
S.Hernandez*, C.Sjogreen, S.Gay, T.Netherton, A.Olanrewaju, C.Nguyen,
D.Rhee, J.Mendez, L.Court, C.Cardenas, L.Zhang |
|
| | BReP-SNAP-M-23 : Automatic Location and Size Selection of Round Applicators for AccuBoost Treatments
F.West*, S.Roles, J.Patrick, R.Munbodh, M.Rivard, J.Hepel,
K.Leonard, D.Wazer, Z.Saleh |
|
| | BReP-SNAP-M-24 : Automatic Target Segmentation and Uncertainty Prediction for Post-Prostatectomy Radiotherapy Planning Using Bayesian U-Net
X.Xu*, C.Lian, A.Wang, T.Royce, R.Chen, J.Lian,
D.Shen |
|
| | BReP-SNAP-M-25 : Automatic Tumor and Multi-Organ Segmentation Technique in CT Based On Deep Learning for Radiation Therapy After Breast-Conserving Surgery
J.Lee*, H.Cho, S.Ye, D.Choi, W.Park, H.Kim,
W.Cho, H.Kim |
|
| | BReP-SNAP-M-26 : Auto-Segmentation of Pelvic OARs On MRI Multi-Sequence Using An Fused-Unet
z.cheng, T.Zeng, Y.Liu, L.Lai, X.Yang*, s.Huang |
|
| | BReP-SNAP-M-27 : Beam-Energy and Depth-Of-Interaction Estimation in MV Imaging Using a Dual-Ended Readout
M.Myronakis*, R.Berbeco |
|
| | BReP-SNAP-M-28 : Beams-Eye-View Tracking of Prostate Fiducial Markers During VMAT Treatments
A.Mylonas*, E.Hewson, P.Keall, J.Booth, D.Nguyen |
|
| | BReP-SNAP-M-29 : Branch-UNet for Intraprostatic Lesion Segmentation in Multi-Parametric MRI Images for Boosting Radiotherapy of Prostate Cancer
Y.Chen*, L.Xing, H.Bagshaw, M.Buyyounouski, B.Han |
|
| | BReP-SNAP-M-30 : Can Daily Online Adaptive Therapy Overcome Prostate Patients' Periodic Non-Adherence to Full Bladder/Empty-Rectum Protocols?
M.Moazzezi*, K.Moore, K.Kisling, C.Bojechko, X.Ray |
|
| | BReP-SNAP-M-31 : Can Machine Learning Optimization (MLO) Auto Planning Clinically Replace Multi-Criteria Optimization (MCO) Manual Planning in Complex Pancreatic VMAT with Dose Painting?
Y.Wang*, H.Prichard, J.Wo, T.Hong |
|
| | BReP-SNAP-M-32 : Cardiac Substructure Tracking During Ablative Radiotherapy
N.Hindley*, C.Shieh, S.Lydiard, T.Reynolds, P.Keall |
|
| | BReP-SNAP-M-33 : Cascading Deep Multi-Label Network for CT Liver and Spleen Structure Segmentation: Learning From Imperfect Clinical Data
R.Haq*, A.Jackson, A.Apte, M.Montovano, A.Wu, J.Deasy,
M.Thor |
|
| | BReP-SNAP-M-34 : CBCT Scatter Correction Using Monte Carlo Simulations and Deep Convolutional Neural Networks for Adaptive Proton Therapy
A.Lalonde*, B.Winey, J.Verburg, H.Paganetti, G.Sharp |
|
| | BReP-SNAP-M-35 : Characterization of Diffusion Weighted MRI and Apparent Diffusion Coefficient Calculation On a Low Field MR-LINAC Radiotherapy System at Multiple Gantry Angles
B.Lewis*, A.Guta, S.Mackey, H.Gach, S.Mutic, O.Green,
T.Kim |
|
| | BReP-SNAP-M-36 : Classification and Segmentation of Tumor Cells and Nuclei On Biopsy Slides Using Deep Learning for Microdosimetry Applications
L.Weishaupt*, J.Torres, S.Camilleri-broët, S.Côté Maldonado, S.Abbasinejad Enger |
|
| | BReP-SNAP-M-37 : Combining Delta-Radiomics and Clinical Biomarkers Based On KNN-PCA Classification to Improve Treatment Outcome Prediction for Pancreatic Cancer
H.Nasief*, W.Hall, C.Zheng, S.Tsai, B.Erickson, X.Li |
|
| | BReP-SNAP-M-38 : Combining Monte Carlo with a Generative Adversarial Network to Predict High-Resolution Low-Noise Dose Distributions
V.Vasudevan*, C.Huang, E.Simiele, L.Yu, L.Xing, E.Schueler |
|
| | BReP-SNAP-M-39 : Combining Radiomics and Convolutional Neural Network to Predict Tumor Growth of Vestibular Schwannoma
K.Wang*, L.Chen, N.George-jones, J.Hunter, J.Wang |
|
| | BReP-SNAP-M-40 : Commissioning and Performance Testing of the First Prototype of AlignRT InBore, a Halcyon™-Dedicated Surface Guided Radiation Therapy Platform
D.NGUYEN*, J.Farah, J.Hughes, P.Mathieu, N.Barbet, M.Khodri |
|
| | BReP-SNAP-M-41 : Comparison and Validation of Distortion Correction On a Low-Field MR-Guided Radiotherapy System
Y.Gao*, J.Pham, A.Kalbasi, A.Raldow, P.Hu, Y.Yang |
|
| | BReP-SNAP-M-42 : Comparison of Accuracy and Performance of Monte Carlo Dose Calculations Involving Magnetic Fields: ARCHER VS. TOPAS
L.Mao*, K.Sheng, Q.Cheng, H.Yang, J.Wang, X.Pei,
X.Xu |
|
| | BReP-SNAP-M-43 : Compton Scatter Imaging for Tumor Tracking - Initial Experiment with a Photon-Counting Detector
K.Yang*, Y.Huang, X.Jia, X.Li, B.Liu |
|
| | BReP-SNAP-M-44 : Cone Beam CT-Guided Adaptive Strategy in Pre-Operative Gastric Cancer Radiotherapy
M.Bleeker*, K.Goudschaal, A.Bel, J.Sonke, M.Hulshof, A.van der Horst |
|
| | BReP-SNAP-M-45 : CT-Based Convolutional-Neural-Network Segmentation of HCC Regions with Lung-Cancer-Based Transfer Learning
N.Nagami*, H.Arimura, J.Nojiri, R.Nakano, K.Ninomiya, M.Ogata,
S.Takita, S.Kitamura, H.Irie |
|
| | BReP-SNAP-M-46 : CT-Based Radiomics Analysis: A New Imaging Biomarker in Chronic Obstructive Pulmonary Disease?
R.Au*, V.Liu, M.Koo, W.Tan, J.Bourbeau, J.Hogg,
H.Coxson, M.Kirby |
|
| | BReP-SNAP-M-47 : CycleGAN Based Transfer Learning for Synthesizing CT Image From MR Image
W.Li*, T.Bai, D.Nguyen, A.Owrangi, S.Kazemifar, Y.Li,
J.Xiong, Y.Xie, S.Jiang |
|
| | BReP-SNAP-M-48 : Data Shapely Based Auto-Labeling Algorithm
T.Bai, B.Wang, B.Wang*, D.Nguyen, S.Jiang |
|
| | BReP-SNAP-M-49 : Deep Learning Augmented Proton Portal Imaging: A Phantom Study
S.Charyyev*, Y.Lei, J.Harms, B.Eaton, M.McDonald, W.Curran,
T.Liu, J.Zhou, R.Zhang, X.Yang |
|
| | BReP-SNAP-M-50 : Deep Learning Techniques in Microdosimetry: Using Conditional Generative Adversarial Networks to Predict Energy Deposition On Cellular Length Scales
I.Mansour*, R.Thomson |
|
| | BReP-SNAP-M-51 : Deep Learning-Based Deformable Image Registration Method and Multiple Reference Registration Strategy for Tumor Target Tracking in 2D Cine MRI
y.zhang*, T.Mazur, X.Wu, H.Li, H.Gach, D.Yang |
|
| | BReP-SNAP-M-52 : Deep Neural Network for Survival Analysis Using Intratumoral Radiomics and Dosiomics in the RTOG 0617 Clinical Trial
S.Lee*, H.Geng, H.Zhong, Y.Fan, M.Rosen, Y.Xiao |
|
| | BReP-SNAP-M-53 : Deep Neural Network-Based Prediction of Dual-Energy Subtraction Images From Single-Energy X-Ray Fluoroscopy: A Feasibility Study
J.Wang*, K.Ichiji, N.Homma, X.Zhang, Y.Takai |
|
| | BReP-SNAP-M-54 : Deep-Learning-Based Autosegmentation Outperforms Atlas-Based Autosegmentation in a Clinical Cohort of Breast Cancer Patients
J.Kleijnen*, H.Akhiat, M.Hoogeman, S.Petit |
|
| | BReP-SNAP-M-55 : Detecting Pathological Complete Response in Esophageal Cancer After Neoadjuvant Therapy Based On Survival-Weighted Deep Learning: A Pilot Study
S.Cheng*, W.Yap, E.Tu |
|
| | BReP-SNAP-M-56 : Detection of Vessel Bifurcations in 3D Images for Automatic Objective Assessment of Deformable Image Registration Accuracy
G.Cazoulat*, B.Anderson, M.McCulloch, D.Elganainy, M.Zaid, P.Park,
E.Koay, K.Brock |
|
| | BReP-SNAP-M-57 : Determining the Effect of Nano-Based Therapeutics in Cancer Vs Normal Cells
K.Bromma*, L.Cicon, D.Chithrani |
|
| | BReP-SNAP-M-58 : Development and Implementation of a Knowledge Base for Automated Segment Review
E.Pryser*, M.Schmidt, F.Reynoso, W.Smith |
|
| | BReP-SNAP-M-59 : Development of a Machine Learning Algorithm for Hybrid Interstitial Needle Prediction in High-Dose-Rate Cervical Brachytherapy
K.Stenhouse*, M.Roumeliotis, P.McGeachy |
|
| | BReP-SNAP-M-60 : Development of a New Prompt Gamma Ray Detection System to Achieve Range Verification in Full 3 Dimensions for Pencil-Beam Scanning Proton Therapy
C.Panaino*, R.Mackay, K.Kirkby, M.Taylor |
|
| | BReP-SNAP-M-61 : Development of An Inexpensive Patient Setup and Monitoring System Using An Open Source, GPU Computed, Deep Learning Algorithm
D.Cho*, D.Mah |
|
| | BReP-SNAP-M-62 : Dosimetric Impact of Interfraction Anatomical Variations On Breath-Hold SBRT for Pancreatic Cancer
J.Niedzielski*, S.Ng, S.Beddar, R.Martin, E.Holliday, G.Smith,
B.Minsky, A.Koong, C.Taniguchi, P.Das, E.Koay,
G.Sawakuchi |
|
| | BReP-SNAP-M-63 : Dosimetric Predictors for Volumetric Liver Deformation and Change in Liver Function After SBRT for Hepatocellular Carcinoma: An In-Vivo Assessment Based On Longitudinal Magnetic Resonance Imaging
P.Brodin*, A.Correa De Sousa, H.Nash, J.Tang, W.Tomé, N.Ohri,
S.Kalnicki, M.Garg, C.Guha, R.Kabarriti |
|
| | BReP-SNAP-M-64 : Douglas-Rachford Fixed-Point Iteration of Sparsity Optimization with Deep Neural Networks Parameterized Inversion and Joint Image Prior (SOFPI-DR-Net) for Cine CT Image Reconstruction
J.Liu*, H.Gao, H.Ji |
|
| | BReP-SNAP-M-65 : Evaluation of a Localized Correlation Based Predictive Metric as a Decision Making Tool in Online Image Guidance and Offline Adaptive Prostate Radiotherapy
A.Gopal*, B.Zhang, G.Lasio, S.Lee, B.Yi |
|
| | BReP-SNAP-M-66 : Evaluation of Lung Cancer SBRT Plans with Large Daily Tumor Motion Variations Using Daily 4D-CBCT
S.Balik*, C.Yurtsever, T.Zhuang, P.Qi, G.Videtic, K.Stephans,
P.Xia |
|
| | BReP-SNAP-M-67 : Evaluation of Proton Computed Tomography Detected by Multiple-Layer Ionization Chamber and Strip Chambers Through Monte Carlo Simulation with Human Head Phantoms
X.Chen*, T.Zhao, R.Liu, B.Sun, F.Reynoso, S.Mutic,
T.Zhang |
|
| | BReP-SNAP-M-68 : Evaluation of Synthetic Single Energy and Dual Energy Radiographs to Predict Markerless Tumor Tracking Accuracy
J.Roeske*, H.Mostafavi, R.Patel, F.Cassetta, L.Zhu |
|
| | BReP-SNAP-M-69 : Evaluation of Two Different Systems for the Deformable Segmentation of a Lung Tumor in the Treatment of NSCLC Using SBRT
Y.Cao*, D.Ghosh, J.Gomez, A.Singh, H.Malhotra |
|
| | BReP-SNAP-M-70 : ExacTrac Imaging Dose for Real Time Tumour Monitoring of Lung SBRT Patients Using Monte Carlo Simulation
A.Spurway*, M.Sattarivand |
|
| | BReP-SNAP-M-71 : Exploration of Potential Parameters That Influence When to Replan Head and Neck Cancer Patients
A.Andrade*, G.Shenouda, J.Kildea |
|
| | BReP-SNAP-M-72 : Factors Contributing to Geometrical Accuracy in Online MRI-Linac Guided Brain Radiotherapy
M.Ruschin*, C.Mccann, J.Stewart, P.Maralani, M.Campbell, A.Kim,
Y.Lee, A.Carty, C.Tseng, A.Sahgal, B.Keller |
|
| | BReP-SNAP-M-73 : Feasibility of Using Simultaneously Acquired MV/kV Images to Monitor Spine Target Position During SBRT Delivery: A Phantom Study
Q.Liu*, J.Liang, S.Chen, D.Drake, D.Yan |
|
| | BReP-SNAP-M-74 : Fiducial-Free Real-Time Image-Guided Robotic Radiosurgery for Tumors of the Spine
W.Zhao*, D.Capaldi, C.Chuang, L.Xing |
|
| | BReP-SNAP-M-75 : Forecasting Individual Patient Response to Radiotherapy with a Dynamic Carrying Capacity Model
M.Zahid*, N.Mohsin, A.Mohamed, J.Caudell, L.Harrison, C.Fuller,
E.Moros, H.Enderling |
|
| | BReP-SNAP-M-76 : From Fluence to Dose: Real-Time Adaptive MLC Tracking Using Dose Optimization
L.Mejnertsen*, E.Hewson, D.Nguyen, J.Booth, P.Keall |
|
| | BReP-SNAP-M-77 : Functionally Weighted Airway Sparing (FWAS) to Preserve Post-SAbR Respiratory Function
E.Vicente*, A.Modiri, J.Kipritidis, A.Hagan, K.Yu, H.Wibowo,
Y.Yan, D.Owen, M.Matuszak, P.Mohindra, R.Timmerman,
A.Sawant |
|
| | BReP-SNAP-M-78 : Gold Fiducial Marker Visualization with Multifrequency Reconstruction for Frequency-Modulated BSSFP
Q.Peng*, L.Goddard, C.Wu, S.Hsu, W.Tomé |
|
| | BReP-SNAP-M-79 : GPU-Based Acceleration of MV-CBCT Simulation
M.Shi*, M.Myronakis, M.Jacobson, M.Lehmann, D.Ferguson, P.Baturin,
P.Huber, R.Fueglistaller, T.Harris, I.Valencia Lozano, C.Williams,
D.Morf, R.Berbeco |
|
| | BReP-SNAP-M-80 : Hard Constraint Approximation for Deep Learning in Radiation Therapy
R.McBeth*, M.Lin, A.Godley, S.Jiang, D.Nguyen |
|
| | BReP-SNAP-M-81 : High-Precision Dosimetry in Yttrium-90 Radioembolization Through Post-Procedural CT Imaging of Radiopaque Microspheres in a Porcine Model
C.Henry*, M.Strugari, G.Mawko, K.Brewer, R.Abraham, A.Syme |
|
| | BReP-SNAP-M-82 : Human-Level Comparable Control Volumes Mapping with An Unsupervised-Learning Model for CT-Guided Radiotherapy
X.Liang*, M.Bassenne, D.Hristov, T.Islam, W.Zhao, M.jia,
Z.Zhang, C.Huang, L.Xing |
|
| | BReP-SNAP-M-83 : Image Guidance for Eye Tumors Treated with Proton Therapy: Noninvasive Eye Tracking Versus X-Ray Imaging
R.Via*, G.Fattori, A.Pica, G.Baroni, A.Lomax, D.Weber,
J.Hrbacek |
|
| | BReP-SNAP-M-84 : Image Processing System by Super-Resolution Using Deep Learning Leading to Exposure Dose Reduction
H.Miyauchi*, Y.Tanaka, K.Takahashi, M.Nakano, T.Hasegawa, M.Hashimoto |
|
| | BReP-SNAP-M-85 : Image Quality of Tomographic Thermal Imaging Reconstruction
J.McCorkindale*, Y.Liao, K.Jones, J.Sun, A.Templeton, J.Chu,
J.Turian |
|
| | BReP-SNAP-M-86 : Image Synthesis and Automation of Synthetic CT From MRI Data for MRI-Driven RT
S.Mousavi*, A.Hosni-Abdalaty, D.Shultz, A.Berlin, C.Coolens, T.Stanescu |
|
| | BReP-SNAP-M-87 : Imaging Dose in Breast Radiotherapy by X-Ray CT Calibration of Cherenkov Light
R.Hachadorian*, P.Bruza, M.Jermyn, D.Gladstone, L.Jarvis, B.Pogue |
|
| | BReP-SNAP-M-88 : Improving the Accuracy of Bone Marrow Dosimetry Using the MIRD Phantom
K.Ferrone*, C.Willis, J.Ma, L.Peterson, F.Guan, S.Kry |
|
| | BReP-SNAP-M-89 : Improving the Treatment Accuracy of Standard Linear Accelerator-Based Pancreatic SBRT: Initial Clinical Experience with An In-House Position Monitoring System
S.Arumugam*, J.Begg, N.Collier, M.Lee |
|
| | BReP-SNAP-M-90 : Incorporating Explicit Dose-Volume Constraints in Deep Learning Improves Prediction of Deliverable Dose Distributions for Prostate VMAT Planning
S.Willems*, L.Vandewinckele, E.Sterpin, K.Haustermans, W.Crijns, F.Maes |
|
| | BReP-SNAP-M-91 : Incorporating GTV Information in a Multi-Stage Process to Improve Automatically Generated Field Apertures for Rectal Cancer Radiotherapy
K.Huang*, P.Das, L.Zhang, M.Amirmazaheri, C.Nguyen, D.Rhee,
T.Netherton, S.Beddar, T.Briere, D.Fuentes, E.Holliday,
L.Court, C.Cardenas |
|
| | BReP-SNAP-M-92 : Interfraction Dose Variation of Supine Whole-Breast Radiotherapy Measured From Daily Cone Beam Computed Tomography Images
A.Van Slyke*, S.Yoon, N.Taunk, G.Freedman, B.Teo, J.Zou,
C.Kennedy, L.Dong, T.Li |
|
| | BReP-SNAP-M-93 : Inter-Fractional Changes in Dose and the Effect On Post-Treatment Quality of Life for Patients with Head and Neck Cancer
S.Weppler*, H.Quon, A.Yarschenko, N.Harjai, V.Tran, P.Chen,
C.Schinkel, W.Smith |
|
| | BReP-SNAP-M-94 : Interfractional Motion Is Not a Predictor of Delivery Accuracy in Liver Stereotactic Body Radiation Therapy
S.Kuznetsova*, R.Sinha, N.Ploquin, K.Thind |
|
| | BReP-SNAP-M-95 : Inter-Vendor Compatibility and Transfer Learning for MR-Based Synthetic CT Deep Learning Models for Domain Adaptation
P.Klages*, N.Tyagi, H.Veeraraghavan |
|
| | BReP-SNAP-M-96 : Intra-Fraction Motion and Dosimetric Effects of Liver Stereotactic Body Radiation Therapy During Free-Breathing, Exhale and Inhale Active Breath-Hold
T.Nano*, M.Feng, D.Capaldi, M.Sharma, E.Hirata, S.Lim,
M.Anwar, M.Hira |
|
| | BReP-SNAP-M-97 : Lifelong Learning for Clinical Target Segmentation of Nasopharyngeal Cancer with Fewer Labeling
K.Men*, X.Chen, Y.Zhang, J.Zhu, J.Yi, J.Dai |
|
| | BReP-SNAP-M-98 : Machine Learning Analysis of Treatment Session Time Components
D.Smith*, R.Kashani, C.Mayo |
|
| | BReP-SNAP-M-99 : Markerless Lung Tumor Tracking Using AI-DE Imaging
L.Zhu*, F.Arrate, P.Baturin, P.Paysan, H.Mostafavi, J.van Heteren,
F.Cassetta, J.Roeske, S.Scheib |
|
| | BReP-SNAP-M-100 : Markerless Lung Tumour Tracking in the Beam-Eye-View for Modern VMAT Treatments
R.Tjang, J.Booth, C.Shieh, V.Caillet, P.Keall, D.Nguyen* |
|
| | BReP-SNAP-M-101 : Monte Carlo Investigation of the Radio-Enhancement Effects Produced by a Gadolinium-Based Nanoparticle (AGuIX)
R.Maschmeyer*, H.Byrne, A.McNamara, G.Le Duc, O.Tillement, F.Lux,
Z.Kuncic |
|
| | BReP-SNAP-M-102 : Moving From Geometric-Based to Risk-Based Adaptive Re-Planning in Head and Neck Radiotherapy
K.Brock*, S.Gryshkevych, B.Rigaud, A.Ohrt, S.Svensson, C.Fuller |
|
| | BReP-SNAP-M-103 : MR Guided Adaptive Radiotherapy Improves Target Coverage and OAR Sparing: Dosimetric Analysis of 1185 Adaptive Fractions and 4 Years Experience
D.Yang*, H.Kim, O.Green, B.Cai, L.Henke, J.Cammin,
B.Gu, H.Li, H.Gach |
|
| | BReP-SNAP-M-104 : Multicenter Characterization of MRIs Used for Radiation Therapy: The Collaborative Quality Assurance Program
L.Conroy*, C.Foottit, B.Zhang, A.Elzibak, R.Hunter, P.Rapley,
J.Kraus Himmelman, E.Gutierrez, T.Tadic, A.McNiven, D.Letourneau |
|
| | BReP-SNAP-M-105 : Multi-Strategy Machine Learning Auto Planning for Liver SBRT: Improving Quality, Consistency and Efficiency for a Complex Treatment
H.Prichard*, J.Wo, T.Hong, Y.Wang |
|
| | BReP-SNAP-M-106 : Novel Angle Descriptor Projection Overlap Volume for Improved Quality Assurance of Lung Cancer Radiotherapy Treatment Plans
J.Zhang*, Y.Yang, Y.Chen, J.Zhang, M.Chen |
|
| | BReP-SNAP-M-107 : Novel Localization Box with 3D Printing for Frameless Linac-Radiosurgery for Uveal Melanoma
Y.Zhang*, Y.Yu, J.Li, W.Shi, H.Liu |
|
| | BReP-SNAP-M-108 : Off-Line Treatment Monitoring of Head and Neck Radiotherapy Using Daily Cone-Beam Computed Tomography: A Preliminary Study
S.Lee*, B.Zhang, G.Lasio, A.Gopal, I.Lee, H.Xu,
S.Chen, B.Yi |
|
| | BReP-SNAP-M-109 : On the Impact of Image Rotation On Quantitative Textural Features in Radiomics Analysis
H.Bagher-Ebadian*, I.Chetty |
|
| | BReP-SNAP-M-110 : One-Shot Uncertainty Estimation for Deep Network Based Image Segmentation
Y.Min*, D.Ruan |
|
| | BReP-SNAP-M-111 : Patient-Specific Deep Learning Model for Deformable Image Registration
A.Amini*, Z.Jiang, Y.Chang, Y.Mowery, L.Ren |
|
| | BReP-SNAP-M-112 : Plan Quality-Driven Evaluation of Automated Segmentation for Radiotherapy
J.Zhu*, X.Chen, T.Zhang, N.Bi, K.Men, J.Dai |
|
| | BReP-SNAP-M-113 : Predicting Glioblastoma Cell Motility with Radiomics
K.Mulford*, M.McMahon, D.Odde, C.Wilke |
|
| | BReP-SNAP-M-114 : Prediction of Dosimetric Variation in Dominant Intraprostatic Lesion with Simultaneous Integrated Boost for Pencil Beam Proton Therapy
C.Chang*, D.Bohannon, K.Stiles, A.Stanforth, S.Tian, Y.Wang,
L.Lin, P.Patel, T.Liu, X.Yang, J.Zhou |
|
| | BReP-SNAP-M-115 : Prediction of Radiation Pneumonitis After Lung Stereotactic Body Radiation Therapy Using Dosiomics Features: A Retrospective Multi-Institutional Study
T.Adachi*, M.Nakamura, T.Shintani, T.Mitsuyoshi, R.Kakino, T.Ogata,
H.Tanabe, T.Ono, H.Hirashima, T.Sakamoto, M.Kokubo,
Y.Matsuo, T.Mizowaki |
|
| | BReP-SNAP-M-116 : Progressive Deep Learning: An Accelerated Training Strategy for Medical Image Segmentation
B.Choi*, J.Chun, S.Olberg, I.Park, H.Li, J.Kim,
S.Mutic, J.Park |
|
| | BReP-SNAP-M-117 : Pulse Sequence Optimization for Non-EPI Diffusion-Weighted Imaging Sequences for Head & Neck On a 1.5T MR-Linac
B.McDonald*, L.Zhu, S.Mulder, A.Dresner, A.Mohamed, S.Ahmed,
R.He, Y.Ding, C.Fuller |
|
| | BReP-SNAP-M-118 : Quality Assurance for the Clinical Implementation of Kilovoltage Intrafraction Monitoring (KIM) in the TROG 17.03 Liver Ablative Radiotherapy with KIM (LARK) Multi-Institutional Clinical Trial
C.Sengupta*, D.Nguyen, T.Moodie, S.Alnaghy, R.O'Brien, P.Keall |
|
| | BReP-SNAP-M-119 : Quantification of Lung Ventilation Using Voxel-Based Delta Radiomics Extracted From Thoracic 4DCT
X.Chen*, K.Lafata, Z.Yang, F.Yin |
|
| | BReP-SNAP-M-120 : Quantum-Inspired Approach to Predicting Geometric Changes in Head and Neck Cancer
J.Pakela*, R.Ten Haken, D.McShan, M.Matuszak, I.El Naqa |
|
| | BReP-SNAP-M-121 : Radiomic Features Combined with Hybrid Machine Learning Robustly Identify Parkinson Disease Subtypes
M.Salmanpour*, M.Shamsaei, A.Saberi, G.Hajianfar, H.Soltanian-zadeh, A.Rahmim |
|
| | BReP-SNAP-M-122 : Radiomics for Early Detection of Recurrence in Patients Treated with Lung Stereotactic Ablative Radiotherapy (SABR)
T.Kunkyab*, D.Hyde, A.Jirasek, B.Mou, C.Haston, J.Andrews,
S.Thomas |
|
| | BReP-SNAP-M-123 : Rapid Commissioning of An MR-Linac with Hydrostatic and Cherenkov Imaging Techniques
R.Zhang*, D.Alexander, B.Williams, L.Gates, F.Rafie, N.Nelson,
P.Bruza, B.Pogue, B.Zaki, D.Gladstone |
|
| | BReP-SNAP-M-124 : Real Time Monitoring of Anatomy During IMRT and VMAT Breast Radiotherapy Using EPID Imaging
B.Zwan*, C.Stanton, H.Byrne, A.Briggs, J.Booth, P.Keall |
|
| | BReP-SNAP-M-126 : Real-Time Adaptive Dose Scaling During MLC-Tracking On the Elekta Unity MR-Linac
P.Borman*, P.Woodhead, P.Uijtewaal, S.Hackett, J.Lagendijk, B.Raaymakers,
M.Fast |
|
| | BReP-SNAP-M-127 : Real-Time Image Guidance with Single Panel X-Ray Imaging System During Radiation Treatment Delivery
H.Yan*, T.Gou, C.Luo, J.Li, C.Deng, X.Li,
C.Ma, J.Li |
|
| | BReP-SNAP-M-129 : Relabeling Non-Standard to Standard Structure Names Using Geometric and Radiomic Information
W.Sleeman*, J.Palta, P.Ghosh, R.Kapoor |
|
| | BReP-SNAP-M-130 : Reproducibility and Correction of the Respiratory Phase Shift for Enhanced Internal-External Motion Correlation
A.Milewski*, D.Olek, W.Martin, A.Rimner, G.Li |
|
| | BReP-SNAP-M-131 : Reproducibility Test of Radiomic Features Using Regularized Partial Correlation Network
J.Oh*, A.Apte, E.Katsoulakis, N.Riaz, V.Hatzoglou, Y.Yu,
U.Mahmood, M.Pouryahya, A.Iyer, A.Dave, N.Lee,
J.Deasy |
|
| | BReP-SNAP-M-132 : Robustness of Doppler Ultrasound Measurements: Impact of Acquisition Parameters On Peak Velocity
N.Lafata*, M.Russ, S.Robertson, E.Samei |
|
| | BReP-SNAP-M-133 : Rotating Linac-MR: B0 Orientation Invariant Quadrature Detection
R.Barta*, V.Volotovskyy, K.Wachowicz, B.Fallone, N.De Zanche |
|
| | BReP-SNAP-M-134 : Segmentation of Invisible Target Volume with Estimated Uncertainties for Post-Operative Prostate Cancer Radiotherapy
A.Balagopal*, D.Nguyen, M.Lin, H.Morgan, N.Desai, R.Hannan,
A.Garant, Y.Gonzalez, A.Sadeghnejad Barkousaraie, S.Jiang |
|
| | BReP-SNAP-M-135 : Simplification of Transmission Factor Determination for I-131 Gamma Camera Based Internal Dosimetry with Co-57 Solid Flood Source
Q.Liang*, L.Chen, R.Klein, J.Halama, R.Harvey, J.Schwartz,
D.Fisher |
|
| | BReP-SNAP-M-136 : Simultaneous Multi-Slice Accelerated 4D-MRI for the Unity MR-Linac
K.Keijnemans*, P.Borman, B.Raaymakers, M.Fast |
|
| | BReP-SNAP-M-137 : Stability and Reproducibility Study of Radiomics Features Using a 3D-Printed Biological Phantom
Y.Li*, X.Wang, N.Yue, K.Nie |
|
| | BReP-SNAP-M-138 : Supervised Learning-Based Ideal Observer Approximation for Joint Detection and Estimation Tasks
K.Li*, W.Zhou, S.He, H.Li, M.Anastasio |
|
| | BReP-SNAP-M-139 : Surface Guided Imaging and Facial Motion with Open Face Masks
V.Bry*, H.Parenica, J.Rembish, A.Licon, M.Naessig, K.Fischer,
T.Martin, P.Myers, N.Kirby, S.Stathakis, D.Saenz,
N.Papanikolaou, R.Crownover, K.Rasmussen |
|
| | BReP-SNAP-M-140 : Synthetic Digital Reconstructed Radiograph for MR-Only Robotic Radiosurgery with Deep Convolutional Adversarial Networks
G.Szalkowski*, D.Nie, T.Zhu, M.Dance, X.Xu, A.Wang,
T.Royce, R.Chen, D.Shen, J.Lian |
|
| | BReP-SNAP-M-141 : Total Skin Electron Therapy Treatment Planning and Dose Distribution Verification Using Cherenkov Imaging and Computer Animation Techniques
T.Miao*, M.Jermyn, R.Zhang, P.Bruza, T.Zhu, B.Williams,
D.Gladstone, B.Pogue |
|
| | BReP-SNAP-M-142 : Towards Best Practice Guidelines for Reference Dosimetry in a 1.5 T MR-Linac
V.Iakovenko*, B.Keller, H.Nusrat, A.Sahgal, A.Sarfehnia |
|
| | BReP-SNAP-M-143 : Tumor Motion in Locally Advanced Lung Cancer: A Study of 46 Patients with 4DCT and 4DCBCT
L.Su*, N.O'donnell, K.Ding, R.Hales |
|
| | BReP-SNAP-M-144 : Tumor Position Variation in Gating Window Using Cine MRI of 0.35T MRgRT
A.Guta*, B.Lewis, Z.Ji, D.Lam, T.Mazur, S.Mutic,
T.Kim |
|
| | BReP-SNAP-M-145 : Two-Step Subspace Mapping Based Diaphragm Displacement Prediction by Markerless Abdominal Surface Measurement
H.Yu*, E.Zhang, S.Yu, Z.Yang, L.Ma, M.Chen,
X.Gu, W.Lu |
|
| | BReP-SNAP-M-146 : Uncertainty-Aware Reconstructed Image Correction for Proton Computed Tomography Using Bayesian Deep Learning
Y.Nomura*, S.Tanaka, J.Wang, H.Shirato, S.Shimizu, L.Xing |
|
| | BReP-SNAP-M-147 : Update On DVH Analytics: An Open-Source DICOM-RT Database Application
D.Cutright*, M.Gopalakrishnan, A.Roy |
|
| | BReP-SNAP-M-148 : Using An Independent Dose Calculation System to Optimize Clinical Thresholds and Reduce the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance
K.Hasse*, B.Ziemer, Y.Natsuaki, G.Valdes, E.Hirata, T.Solberg,
J.Scholey, A.Witztum |
|
| | BReP-SNAP-M-149 : Validating Radiance's Treatment Planning System for Image-Guided Intra-Operative Radiation Therapy Using Plastic Cones
A.Steinmann*, S.Jain, A.Ayan, N.Gupta, J.Woollard |
|
| | BReP-SNAP-M-150 : Value Proposition of Online Adaptive Radiotherapy: Plan Fidelity and Deliverability
K.Mittauer*, A.Shepard, L.Bayouth, J.Bayouth |
|
| | BReP-SNAP-M-151 : Variable Margin Expansion to Account for MR Image Distortion in Treatment Planning
R.Mumme*, C.Nguyen, A.Olanrewaju, P.Castillo, J.Wang, X.Wang,
C.Cardenas, L.Court, M.Martel, J.Yang |
|
| | BReP-SNAP-M-152 : Real-Time Delineation of Implanted Cardioverter-Defibrillators and Metal Artifact Reduction in Cardiac Cone-Beam CT
I.Park*, J.Chun, S.Olberg, B.Cai, G.Hugo, J.Kim,
S.Mutic, J.Park |
