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Keywords: Angiography
SU-I430-GePD-F9-3Initial Evaluation of the Use of a Convolutional Neural Network to Determine Coronary Artery Disease Severity Using Computed Tomography Angiography
A Podgorsak1, 2*, K Sommer1, 2 , V Iyer3 , M Wilson1 , U Sharma1 , K Kumamaru3 , F Rybicki4 , D Mitsouras4 , E Angel5 , C Ionita1, 2 , (1) SUNY Buffalo, Buffalo, NY, (2) Canon Stroke and Vascular Research Center, Buffalo, NY, (3) Juntendo University, Tokyo, (4) University of Ottawa, Ottawa, ON, (5) Canon Medical Systems, Tustin, CA
TH-BC-304-2A Simple Method to Estimate the System, Patient Size and Projection Specific Dose Conversion Coefficients for Coronary Angiography Procedures
S Wang*, B Peng , S Jambawalikar , Columbia University, New York, NY
TH-D-302-1BEST IN PHYSICS (IMAGING): Addressing Key Technical Challenges of CHO Models Associated with Spatial and Temporal Non-Stationarity in X-Ray Angiography Including Anatomical Background and Moving Test Objects
D Gomez Cardona*, C Favazza , S Leng , B Schueler , K Fetterly , Mayo Clinic, Rochester, MN
TH-D-302-2Assessment of the Influence of X-Ray Angiography Image Processing On CHO Detectability of Moving Test Objects in the Presence of An Anatomically-Relevant Background
D Gomez Cardona*, K Fetterly , Mayo Clinic, Rochester, MN
TH-D-302-6Image Quality Comparison of Statistical Pixel Angiography and Digital Subtraction Angiography for Radioembolization Procedures
E Olguin*, L Rill , Z Zhang , B Geller , University of Florida, Gainesville, FL

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