Detectability of small blood vessels with high-frequency power Doppler and selection of wall filter cut-off velocity for microvascular imaging.
Power Doppler imaging of physiologic and pathologic angiogenesis is widely used in preclinical studies to track normal development, disease progression and treatment efficacy but can be challenging given the presence of small blood vessels and slow flow velocities. Power Doppler images can be plagued with false-positive color pixels or undetected vessels, thereby complicating the interpretation of vascularity metrics such as color pixel density (CPD). As an initial step toward improved microvascular quantification, flow-phantom experiments were performed to establish relationships between vessel detection and various combinations of vessel size (160, 200, 250, 300 and 360 microm), flow velocity (4, 3, 2, 1 and 0.5 mm/s) and transducer frequency (30 and 40 MHz) while varying the wall filter cut-off velocity. Receiver operating characteristic (ROC) curves and areas under ROC curves indicate that good vessel detection performance can be achieved with a 40-MHz transducer for flow velocities > or =2 mm/s and with a 30-MHz transducer for flow velocities > or =1 mm/s. In the second part of the analysis, CPD was plotted as a function of wall filter cut-off velocity for each flow-phantom data set. Three distinct regions were observed: overestimation of CPD at low cut-offs, underestimation of CPD at high cut-offs and a plateau at intermediate cut-offs. The CPD at the plateau closely matched the phantom's vascular volume fraction and the length of the plateau corresponded with the flow-detection performance of the Doppler system assessed using ROC analysis. Color pixel density vs. wall filter cut-off curves from analogous in vivo experiments exhibited the same shape, including a distinct CPD plateau. The similar shape of the flow-phantom and in vivo curves suggests that the presence of a plateau in vivo can be used to identify the best-estimate CPD value that can be treated as a quantitative vascularity metric. The ability to identify the best CPD estimate is expected to improve quantification of angiogenesis and anti-vascular treatment responses with power Doppler.
Pinter
Stephen Z
SZ
Biomedical Engineering Graduate Program, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B9, Canada.
Lacefield
James C
JC
D000818:Animals; D001783:Blood Flow Velocity; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D055806:Microvessels / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000502:physiology; D009389:Neovascularization, Pathologic / Q000000981:diagnostic imaging*; D018919:Neovascularization, Physiologic / Q000502:physiology*; D019047:Phantoms, Imaging; D012372:ROC Curve; D013737:Testis / Q000098:blood supply / Q000000981:diagnostic imaging; D018608:Ultrasonography, Doppler / Q000379:methods