Methods: Experimental Set-up :

Novel Computational Algorithm to Quantify Blood Flow and Vascular Resistance from Contrast Angiography with High Accuracy

Introduction: Contrast angiography is routinely used to diagnose and treat arterial occlusive disease, but is limited in its ability to provide hemodynamic data.

• Measurement Error >10% (All Phase of Cardiac Cycle1) •Theoretical Flow Maximum based on technique1,2 • Algorithms difficult to use in the clinical setting

Hypothesis: Blood flow can be calculated from a contrast angiogram with <10% error through the application of a novel computational algorithm.

Doran Mix, B.S.1, Daniel Phillips, Ph.D.2, Steven Day, Ph.D.2, Nicole Varble, M.S.1, Karl Schwartz, M.D.1, David Gillespie M.D.1, Ankur Chandra, M.D.1 1University of Rochester, Rochester, NY, USA, 2Rochester Institute of Technology, Rochester, NY, USA

Conclusion: We conclude that using this approach, blood flow can be angiographically measured with increased accuracy.

Limitations: • Pixel Resolution of ROI •Frame Rate of Angiographic Sequence

Future Applications: •Diagnostics •Therapeutic Interventions

Experimental Design: Utilizing the angiographic simulator (Figure #1) the resistance of a in-flow stenosis was systematically varied at the AVF (Figure#2A). The custom computational algorithm was applied under two experimental conditions:

• Experiment #1: For a fixed ~9000 pixel ROI with the proximal edge of the ROI 11.5cm from pigtail catheter (Figure #3)

• Experiment #2: For a progressively increasing user selected ROI, with the proximal edge of the ROI 11.5cm from the pigtail catheter (Table #1 and Figure #4)

Figure#2: Fistula Model and Matlab Algorithm Showing Vessel Segmentation Algorithm

A. Upper Arm AVF model showing location of stenosis and contrast injection using 6F pigtail catheter. B. Light based simulated angiogram showing DICOM image of contrast injection at injection site (Figure A) C. Matlab algorithm showing user selected region of interest from DICOM image (Red Circle Figure B) D. Automatic vessel detection in ROI (Red Box) using Otsu method and 4x4 search algorithm (Green Box) E. Automatic vessel segmentation, perpendicular to flow using results of vessel detection (Figure D)

A.

Figure #1.A:Radiation-free Angiographic Simulation System Conceptualization: Computer Controlled Pneumatic Compression Chamber Blood Mimicking Fluid Transonic Transit Time Flow Meter Hewlett Packard Multi-Parameter Pressure Monitor 5cc Optically Opaque Dye dissolved in 250cc Optiray 350 1024x1024 Digital CCD X-ray Camera X-ray Generator Frame Timing Simulator DICOM Network USB Network Figure #1.B&C: Resulting Simulator Pressure and Flow Waveforms

PlatinumOne™ Digital Angiography System

In-Vitro Hemodyanmic Flow Circuit Simulator

©

MEDRAD Mark V Power Injector

Pressure/Flow Meters

Data Acquisition System PACS Sever

Adjustable Green Light

Table

X-ray Generator Frame Timing

Simulator

CCD Digital X-ray Camera

Dye Diversion Chamber

Hydraulic Pump

Results:

Angiographic Frame Number at 15fps

Segment Location (0.3mm/Segment)

Degree of Fistula Stenosis Flow Restriction (%) Resistance (mmHg*min/L)

100% Flow Restriction

Resistance: ∞ mmHg*min/L

88% Flow Restriction

Resistance: 485 mmHg*min/L

78% Flow Restriction

Resistance: 245 mmHg*min/L

65% Flow Restriction

Resistance: 138 mmHg*min/L

54% Flow Restriction

Resistance: 94 mmHg*min/L

40% Flow Restriction

Resistance: 57 mmHg*min/L

30% Flow Restriction

Resistance: 39 mmHg*min/L

13% Flow Restriction

Resistance: 19 mmHg*min/L

0% Flow Restriction

Resistance: 8 mmHg*min/L

Axillary Flow 180 cc/min

Distal Flow 180 cc/min

Post- Stenosis MAP 29 mmHg

Pre- Stenosis MAP 87 mmHg

Axillary Flow 280 cc/min

Distal Flow 173 cc/min

Post- Stenosis MAP 31 mmHg

Pre- Stenosis MAP 83 mmHg

Axillary Flow 380 cc/min

Distal Flow 180 cc/min

Post- Stenosis MAP 32 mmHg

Pre- Stenosis MAP 81 mmHg

Axillary Flow 470 cc/min

Distal Flow 152 cc/min

Post- Stenosis MAP 35 mmHg

Pre- Stenosis MAP 79 mmHg

Axillary Flow 570 cc/min

Distal Flow 155 cc/min

Post- Stenosis MAP 37 mmHg

Pre- Stenosis MAP 76 mmHg

Axillary Flow 700 cc/min

Distal Flow 153 cc/min

Post- Stenosis MAP 40 mmHg

Pre- Stenosis MAP 71mmHg

Axillary Flow 780 cc/min

Distal Flow 138 cc/min

Post- Stenosis MAP 43 mmHg

Pre- Stenosis MAP 68 mmHg

Axillary Flow 920 cc/min

Distal Flow 125 cc/min

Post- Stenosis MAP 47 mmHg

Pre- Stenosis MAP 62 mmHg

Axillary Flow 1030 cc/min

Distal Flow 118 cc/min

Post- Stenosis MAP 50 mmHg

Pre- Stenosis MAP 57 mmHg

192 cc/min

294 cc/min

387 cc/min

504 cc/min

525 cc/min

658 cc/min

785cc/min

923 cc/min

1039 cc/min

Histogram for User Specified Axillary Artery Region of Interest

Segmental Tracking of Maximum Gradient Frame Number

Contrast Derived Flow (cc/min)

Schematic of Hemodynamic Circuit Under Test

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Frame# Maximum Gradient for ROI

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Angiographic contrast 5cc light opaque dye mixed in 200cc Optiray 350 injected at 6cc/sec for a total of 10cc of contrast

Table#1: Experiment #2 Results of Angiographic Derived Flow Measurements

AVF Model and Angiographic Algorithm: B.

Stenosis

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Figure #4: Linear regression of r2=.9921 between Transonic flow meter with 4% error (y-axis) and computationally derivate flow from angiographic images (x-axis) from experimental derived flow with increasing ROI Table#1.

References: 1. Shpilfoygel SD, Jahan R, Close RA, Duckwiler GR, Valentino DJ. Comparison of methods for

instantaneous angiographic blood flow measurement. Med Phys. 1999;26(6):862-871. 2. Shpilfoygel SD, Close RA, Valentino DJ, Duckwiler GR. X-ray videodensitometric methods for

blood flow and velocity measurement: A critical review of literature. Med Phys. 2000;27(9):2008-2023.

Figure #3: Absolute Pixel Density in fixed ~9000 pixel ROI, Experiment #1, with contrast injection controlled by simulated x-ray generator at 15th frame. Point of maximum gradient reflects change in distal fistula resistance.

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