Direct Assessment of Wall Shear Stress by Signal Intensity Gradient from Time-of-Flight Magnetic Resonance Angiography

Abstract

The aim of the study was to calculate the arterial wall signal intensity gradient (SIG) from time-of-flight MR angiography (TOF-MRA) and represent arterial wall shear stress. We developed a new algorithm that uses signal intensity (SI) of a TOF-MRA to directly calculate the signal intensity gradient (SIG). The results from our phantom study showed that the TOF-MRA SIG could be used to distinguish the magnitude of blood flow rate as high (mean SIG ± SD, 2.2 ± 0.4 SI/mm for 12.5 ± 2.3 L/min) and low (0.9 ± 0.3 SI/mm for 8.5 ± 2.6 L/min) in vessels (p < 0.001). Additionally, we found that the TOF-MRA SIG values were highly correlated with various flow rates (β = 0.96, p < 0.001). Remarkably, the correlation coefficient between the WSS obtained from the computational fluid dynamics (CFD) analysis and the TOF-MRA SIG was greater than 0.8 in each section at the carotid artery (p < 0.001 for all β values). This new technique using TOF-MRA could enable the rapid calculation of the TOF-MRA SIG and thereby the WSS. Thus, the TOF-MRA SIG can provide clinicians with an accurate and efficient screening method for making rapid decisions on the risk of vascular disease for a patient in clinical practice.

Figures

Figure 1

Calculation of TOF-MRA signal intensity gradient (SIG). (a) TOF-MRA axial source image. (b) 3-dimensional gradient vector (red arrow) of the maximum intensity change. (c) Positioning of points (A and B): point A is a reference point on the artery wall (contour line) and point B is 0.03 mm distant from point A.

Figure 2

Visualization and TOF-MRA signal intensity gradient (SIG). (a) The reference coordinate settings on TOF-MRA axial source images and the selected (left carotid) arterial color display. (b) 3D reconstruction of arterial geometry using the arterial threshold value. (c) A gradient vector setting: from the reference point on the artery wall (contour line), the position of inner point is identified with both a specific distance (0.03 mm in the present study) and the direction having the maximum gradient of the signal intensity. For the drawing, the gradient vector is lengthened to 0.3 mm. (d) 3D mapping of the carotid arterial TOF-MRA SIG.

Figure 3

A phantom experiment: flow-rate dependency of TOF-MRA SIG. (a) Two tubes (80 mm length) of high flow rate (H, 12.5 ± 2.3 L/min of mean flow rate) and low flow rate (L, 8.5 ± 2.6 L/min). In the middle, a MR phantom was observed. (b) Axial view of TOF-MRA SIG in the H and L tubes. (c) 3D reconstructed tubes depicting TOF-MRA SIG: TOF-MRA SIG values were significantly higher in the H tube (mean ± SD, 2.2 ± 0.4, SI/mm) than in the L tube (0.9 ± 0.3, p < 0.001). TOF-MRA SIG values were measured at three levels at 20 mm intervals.

Figure 4

TOF-MRA axial source image for the right internal carotid (ICA) and external carotid artery (ECA). (a) At the arterial periphery, the signal was darker near the outer wall of ICA than near the inner wall because of the difference in intraluminal saturation. (b) Color displays of the arterial signal intensity showed a clear gradation in the color scale from the arterial center region to the periphery. (c) TOF-MRA SIG showed the regions with high or low SIG values.

Figure 5

3D (a) and cross-sectional (b) views of wall shear stress (WSS) from computational fluid dynamics (CFD) and TOF-MRA SIG in carotid artery. A comparison (c) of TOF-MRA SIG with wall shear stress from CFD. Labels indicate the levels where CFD WSS and TOF-MRA SIG were obtained and compared. In each section, TOF-MRA SIG values were significantly correlated with CFD WSS (all β > 0.8, p < 0.001).