Blood flow determination using recursive processing: a digital radiographic method.

Abstract

Temporal filtration of fluoroscopic video sequences is being used as an alternative to pulsed digital subtraction angiography. Using the same image processing architecture and a slight modification in processing logic a parametric image can be synthesized from such a temporally filtered image sequence in virtual real time, i.e., an image sequence that spans T seconds takes exactly T seconds to process. Off-line computer processing is not required. Initial phantom studies imply that the time to maximum opacification (tmax) can be used to determine absolute and relative blood flow with a high confidence level (r greater than .989). Phantom and animal examples are presented.

Monitoring Peri-Therapeutic Cerebral Circulation Time: A Feasibility Study Using Color-Coded Quantitative DSA in Patients with Steno-Occlusive Arterial Disease

“…Therefore, he concluded that x-ray angiography was more sensitive in detecting the decreased flow that occurred in patients with SOAD compared with the diffusible isotope technique. 8,20,21 Recent correlation of CT perfusion and DSA also demonstrate similar findings. 22 Both studies proved that CirT provides reliable information on cerebral blood flow.…”

“…Several other methods are available for analyzing flow based on time-attenuation curves, such as mathematical crosssectional computation, shifted distance-attenuation curves, and gamma-variate fitting. 8,15,16 These methods are computationally intensive and are still constrained by the 2D imaging nature of DSA. One of the major limitations and uncontrollable variations in studying flow in vivo has been the individualized cardiac output.…”

“…[3][4][5][6] Nevertheless, quantitative measurements of blood flow using DSA rarely have been explored. [7][8][9] Recently, Strother et al suggested the color-coded parametric method for illustrating the Tmax of intracranial arteriovenous malformations. 10 The composite DSA imaging increases the conspicuity of subtle changes in blood flow over what could be seen previously by looking at sequential timeframes.…”

Impact of Cerebral Microcirculatory Changes on Cerebral Blood Flow During Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage

“…The timedensity curve of the contrast media in each ROI was obtained from the series of DSA images ( Figure 1B). The time-density curve was fitted to a gamma variate function by the least-squares method, 20,21 and mean transit time (MTT) 22,23 in each ROI was determined as (0Ϫϱ)Ct/(0Ϫϱ)C, where C is the quantity of contrast medium remaining at the site and t is the time after the contrast media is injected. Overall CCT was defined as the the difference between MTT in C 5 and MTT in V R , and it was divided into proximal CCT, which was the circulation time in the extraparenchymal large arteries and was defined as the difference between MTT in C 5 and MTT in M 4 , and peripheral CCT, which was the circulation time in the intraparenchymal small vessels and was defined as the difference between MTT in M 4 and MTT in V R .…”

Model-based blood flow quantification from rotational angiography

“…All existing approaches are based on analysing changes in the X-ray image intensity due to changes of the contrast agent concentration. Shpilfoygel et al (2000) have given a comprehensive review of established techniques, which include: techniques based on bolus tracking using time intensity curves (TICs) at different sites along a vessel (Rutishauser et al, 1970;Yerushalmi and Itzchak, 1976;Silverman and Rosen, 1977;Hoehne et al, 1978;Kruger et al, 1983;Fencil et al, 1989;Schmitt et al, 2002;Schmitt et al, 2005;Bogunovic and Loncaric, 2006); techniques based on distance intensity curves (DICs) at different points in time (Colchester et al, 1986;Seifalian et al, 1989;Hoffmann et al, 1991;Brunt et al, 1992;Shpilfoygel et al, 1998;Bladin et al, 1996;Rhode et al, 2005); techniques based on optical flow (Fitzpatrick, 1985;Efron et al, 1978;Amini et al, 1993;Imbert et al, 1995;Huang et al, 1997;Rhode et al, 2001); and techniques based on first pass distribution analysis (Molloi et al, 1998(Molloi et al, , 2004.…”