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SU‐E‐J‐136: Evaluation of a Non‐Invasive Method on Lung Tumor Tracking
T. Zhao, B. White, D. Low
Division of Medical Physics
Research output
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Contribution to journal
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Article
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peer-review
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Dive into the research topics of 'SU‐E‐J‐136: Evaluation of a Non‐Invasive Method on Lung Tumor Tracking'. Together they form a unique fingerprint.
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Keyphrases
Lung
100%
Non-invasive Method
100%
Lung Tumor Tracking
100%
Tidal Volume
80%
Air Flow Rate
60%
Reconstructed Image
40%
Lung Motion Model
40%
95th Percentile
20%
Simulation Data
20%
Abdominal
20%
Residual Error
20%
Volume Flow
20%
4DCT
20%
Total Lung Capacity
20%
Tumor Tracking
20%
CT Data
20%
Lung Motion
20%
Breathing Model
20%
Continuous Signals
20%
B-spline
20%
First Order Derivative
20%
Volume Flow Rate
20%
Biochemistry, Genetics and Molecular Biology
Tidal Volume
100%
Motion
60%
Airflow
60%
Flow Rate
40%
Breathing
20%
Four Dimensional Computed Tomography
20%
Total Lung Capacity
20%
Earth and Planetary Sciences
Air Flow
100%
Flow Velocity
66%
Pneumatics
33%
Data Simulation
33%
Pharmacology, Toxicology and Pharmaceutical Science
Neoplasm
100%
Lung Tumor
100%