www.buffalo.edu

Design and validation of a capillary phantom to test perfusion systems Rachel P. Wood, Parag Khobragade, Leslie Ying, Kenneth V. Snyder,

David Wack, Stephen Rudin and Ciprian N. Ionita

Department of Biomedical Engineering, Toshiba Stroke and Vascular Research Center, State University of New York at Buffalo

Purpose: There is no standard protocol to verify the perfusion imaging systems which diagnose acute stroke. We create a

precise perfusion phantom mimicking capillaries in the brain vasculature which allows validation of various

perfusion protocols and algorithms which generate perfusion maps.

Future Work: Testing of phantoms with CT and MRI imaging modalities. Designing phantom with varying length

and capillary dimensions. Implementation of Single Value Decomposition method to calculate the flow.

References: 1. Fieselmann, A., et al. (2011). "Deconvolution-Based CT and MR Brain Perfusion Measurement: Int J Biomed Imaging. 2011;2011:467563.

doi: 10.1155/2011/467563. Epub 2011 Aug 28.

2. Kenneth, A Miles., at el.(2008). "Multidetector Computed Tomography in Cerebrovascular Disease: CT Perfusion Imaging." American

Journal of Neuroradiology 29(4): e18-e19.

3. Kudo, K.,et al. (2010) “Differences in CT perfusion maps generated by different commercial software: Quantitative analysis by using

identical source data of acute stroke patients.” Neuroradiology 10.1148/radiol.254082000/-/DC1

Motivation: Kudo et al 3 have compared the commercially available perfusion imaging systems by different

vendors with the same source data. They have post processed the patient data using 5 different systems (GE,

Hitachi, Philips, Siemens and Toshiba) each of which used a different algorithm. They derived CBF, CBV and MTT

maps using these systems and compared them to the maps derived from two standardized algorithms with

Perfusion Mismatch Analyzer.. The correlation coefficients between the standard algorithms and the five systems for

CBF and MTT were between 0.36 and 0.95 whereas for the CBV it was 0.95 on average. They concluded that the

derived perfusion maps differed significantly, the areas under CBF and MTT notably varied while the area under

CBV remained constant.

c) a)

Testing of

phantom with

imaging

modalities

Analyzing the

images in

LabVIEW

Deriving

parameters such

as flow, volume,

time to peak and

mean transit time

Designing and

manufacturing

of Phantom

Fig1: a) Typical brain scan showing selection of inlet and outlet b) Arterial inlet, capillaries and Venous Outlet c) Perfusion map of the brain

Water Reservoir

Water Pump

Contrast Injector

Capillary Phantom

Water Reservoir

X-Ray Tube

X-Ray Detector

Connectors Flow Transducer

Ca(t) is measured Cv(t) is measured

Q(t) is measured

a)

b)

c)

Figure 3: a) SolidWorks model and 3D printed phantom showing the front view b) Isometric view of the phantom c)

SolidWorks model and 3D printed holder d) Phantom inserted into the holders to connect to the flow loop

We created a flow loop using a peristaltic pump Masterflex® Model 77201-62 (IL, USA) and contrast agent

Omnipaque™ NDC 0407-1414-94 (NJ, USA) was administered using a programmable injector Medrad®

Mark V ProVis® PPD 104548 (PA, USA) with a Terumo® 5 Fr. catheter. Digital Subtraction Angiographic

images and low contrast images with cone beam CT were acquired by a Toshiba infinix VF-i/BP (CA, USA)

after the contrast was injected. We used a flow transducer Harvard Apparatus GmbH D-79232 (MA, USA)

to select the flow rates. The parameters while acquiring the images were tube voltage 80 kVp, tube current

125mA, SID of 122cm and field of view of 12.5cm x 17.5cm.

Schematic diagram of the experimental setup

Materials and Methods: The existing perfusion systems are not reliable. To overcome the pitfalls of the

present system, we are designing a more controlled and precise phantom. It was designed in Dassault Systems

SolidWorks® and built using the Polyjet printer Objet Eden260 V. The phantom was an overall cylindrical shape

of a diameter 20mm with height 20mm or 30mm containing capillaries of 300µm which were parallel and in

contact making up the inside volume where flow was allowed. A plastic like material called Duruswhite

(Stratasys) is used to print the phantom.

These images were analyzed by our own code in LabVIEW(National Instruments) software. By choosing ROI at

the inlet, outlet and the perfused area, we will be able to measure the contrast concentration which helps derive

the TDC, determine the maximum slope, area under the curve and the cerebral blood flow. The design of the

perfusion phantom is compatible to be tested with all the imaging modalities and capable of producing clinical

relevant Time Density Curves for a wide range of flow rates.

Figure 4: a) DSA images if the phantom at different time intervals b) Image showing Regions of interest to measure the contrast

concentration c) Time Density Curve over the inlet, outlet and capillaries

Results:

Conclusion: We created a robust calibrating model for evaluation and optimization of existing perfusion systems.

This approach is extremely sensitive to changes in the flow due to the high temporal resolution of the Angiographic

system and could be used as a Gold Standard in testing perfusion systems.

Abbreviations: CBF- Cerebral Blood Flow, CBV- Cerebral Blood Volume, MTT- Mean Transit Time

TDC- Time Density Curve, SID- Source to Image Distance

Background: Perfusion imaging has a significant role in the assessment of acute stroke. The main goal is to identify

the infarct core and ischemic penumbra¹. It guides the clinicians to have a better perspective on the

salvageable brain and treat them accordingly. Brain perfusion is measured when contrast is injected

intravenously. The Fick principle is used to calculate CBF 2 (Units: ml.min-1.(100g)-1)

Q (T) = CBF. [Ca t − Cv t ]. dt𝑇

0

Q (T) = Accumulated mass of contrast, Ca(t) - contrast conc. in arterial inlet, Cv(t) – contrast

concentration in venous outlet CBF- Cerebral Blood Flow

Flow Loop:

c) d)

b) a)

2 sec 4 sec 5 sec 8 sec

12 sec 15 sec 20 sec 26 sec

Perfused

area

Background

Inlet

Outlet

b)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0

1.5

3.5

5.5

7.5

9.5

11.5

13.5

15.5

17.5

Am

pli

tud

e(In

ten

sity

)

Time(msec)

Time Density curves at the

selected ROI

Inletcapillariesoutlet

c)

a)

d)

b)

4mm

20mm

7.5

mm

c)

300µ

Ø20

mm

0

0.005

0.01

0.015

0.02

250 300 350

Max

slo

pe

Flow rates( ml/min)

Maximum slope for the

capillaries at different

flow rates

b)

0.02

0.025

0.03

0.035

0.04

250 300 350

Max

slo

pe

Flow rates (ml/min)

Maximum slope for the

outlet at different flow

rates

c)

0.02

0.025

0.03

0.035

0.04

250 300 350

Max

slo

pe

Flow rates (ml/min)

Maximum slope for the

inlet at different flow rates

a)

100150200250300350400450500550600

250 300 350

Flo

w

Absolute Flow(ml/min)

Calculated flow

a)

Figure 5: a, b and c) Maximum slope for the inlet, capillaries and outlet at different flow rates d) Calculated flow v/s absolute flow

The maximum slope calculations were linearly increasing with increase in flow rate, as the slope will

be steeper for higher flow rates. We calculated the flow using the Fick Principle: the percentage error

between the flows calculated and the flow measured was 25%.