Distinguishing high-flow from low-flow vascular malformations using maximum intensity projection images in dynamic magnetic resonance angiography

Abstract

Background

In addition to ultrasound, magnetic resonance imaging (MRI) is considered a suitable, non-invasive technique to assess the type and extent of vascular malformations. The distinction between low- and high-flow lesions is crucial because it determines appropriate patient treatment.

Purpose

To distinguish high-flow from low-flow lesions on the basis of the enhancement pattern on MIP images acquired from dynamic time-resolved MR angiography (MRA) and compare it with previously described MR-based methods.

Material and Methods

We examined 25 consecutive patients with previously diagnosed vascular malformations. Next, each malformation was classified as “high-flow” or “low-flow” using the following criteria: (i) findings on T1-weighted (T1W) and T2-weighted (T2W) imaging (signal voids, signal intensity); (ii) the time interval between the start of arterial enhancement and the onset of lesion enhancement (artery–lesion time); (iii) the time of maximum lesion enhancement; and (iv) analysis of the slope of the enhancement curve.

Results

Of the 25 patients, seven had high-flow and 18 had low-flow malformations. Signal voids on spin-echo T1W images were observed only in four of seven high-flow malformations and in two of 18 low-flow malformations. Analysis of signal intensity on T2W images showed increased signal intensity in 17 of 18 low-flow malformations, and in two of seven high-flow lesions. Calculation of the artery–lesion time, maximum enhancement time, and slope revealed significant differences between the high- and low-flow groups.

Conclusion

In conclusion, the slope of the enhancement curve appears to be useful in distinguishing between high- and low-flow vascular malformations. Standardization of MR image evaluation criteria is essential.

Keywords

Magnetic resonance imaging (MRI)magnetic resonance angiography (MRA)vascular malformations maximum intensity projection imaging classification

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