Angiographic velocimetry analysis using contrast dilution gradient method with a 1000 frames per second photon-counting detector

Kyle A Williams; Allison Shields; Swetadri Vasan Setlur Nagesh; Mitchell Chudzik; Daniel R Bednarek; Stephen Rudin; Ciprian Ionita

doi:10.1117/1.JMI.10.3.033502

Angiographic velocimetry analysis using contrast dilution gradient method with a 1000 frames per second photon-counting detector

J Med Imaging (Bellingham). 2023 May;10(3):033502. doi: 10.1117/1.JMI.10.3.033502. Epub 2023 Jun 6.

Authors

Kyle A Williams^{1

2

3}, Allison Shields^{2

3}, Swetadri Vasan Setlur Nagesh^{2

4}, Mitchell Chudzik^{1

2}, Daniel R Bednarek^{1

2

3}, Stephen Rudin^{1

2

3

4}, Ciprian Ionita^{1

2

3

4}

Affiliations

¹ University at Buffalo, Department of Biomedical Engineering, Buffalo, New York, United States.
² Canon Stroke and Vascular Research Center, Buffalo, New York, United States.
³ University at Buffalo, Department of Radiology, Buffalo, New York, United States.
⁴ University at Buffalo, Department of Neurosurgery, Buffalo, New York, United States.

PMID: 37287600
PMCID: PMC10242414 (available on 2024-06-06)
DOI: 10.1117/1.JMI.10.3.033502

Abstract

Purpose: Contrast dilution gradient (CDG) analysis is a quantitative method allowing blood velocity estimation using angiographic acquisitions. Currently, CDG is restricted to peripheral vasculature due to the suboptimal temporal resolution of current imaging systems. We investigate extension of CDG methods to the flow conditions of proximal vasculature using 1000 frames per second (fps) high-speed angiographic (HSA) imaging.

Approach: We performed in-vitro HSA acquisitions using the XC-Actaeon detector and 3D-printed patient-specific phantoms. The CDG approach was used for blood velocity estimation expressed as the ratio of temporal and spatial contrast gradients. The gradients were extracted from 2D contrast intensity maps synthesized by plotting intensity profiles along the arterial centerline at each frame. In-vitro results obtained at various frame rates via temporal binning of 1000 fps data were retrospectively compared to computational fluid dynamics (CFD) velocimetry. Full-vessel velocity distributions were estimated at 1000 fps via parallel line expansion of the arterial centerline analysis.

Results: Using HSA, the CDG method displayed agreement with CFD at or above 250 fps [mean-absolute error (MAE): $2.6 \pm 6.3 cm / s$ , $p = 0.05$ ]. Relative velocity distributions correlated well with CFD at 1000 fps with universal underapproximation due to effects of pulsatile contrast injection (MAE: 4.3 cm/s).

Conclusions: Using 1000 fps HSA, CDG-based extraction of velocities across large arteries is possible. The method is sensitive to noise; however, image processing techniques and a contrast injection, which adequately fills the vessel assist algorithm accuracy. The CDG method provides high resolution quantitative information for rapidly transient flow patterns observed in arterial circulation.

Keywords: 1000 fps; angiography; frame rate; patient-specific phantom; velocimetry.