A non-invasive clinical application of wave intensity analysis based on ultrahigh temporal resolution phase-contrast cardiovascular magnetic resonance

Abstract

Background: Wave intensity analysis, traditionally derived from pressure and velocity data, can be formulated using velocity and area. Flow-velocity and area can both be derived from high-resolution phase-contrast cardiovascular magnetic resonance (PC-CMR). In this study, very high temporal resolution PC-CMR data is processed using an integrated and semi-automatic technique to derive wave intensity.

Methods: Wave intensity was derived in terms of area and velocity changes. These data were directly derived from PC-CMR using a breath-hold spiral sequence accelerated with sensitivity encoding (SENSE). Image processing was integrated in a plug-in for the DICOM viewer OsiriX, including calculations of wave speed and wave intensity. Ascending and descending aortic data from 15 healthy volunteers (30 ± 6 years) data were used to test the method for feasibility, and intra- and inter-observer variability. Ascending aortic data were also compared with results from 15 patients with coronary heart disease (61 ± 13 years) to assess the clinical usefulness of the method.

Results: Rapid image acquisition (11 s breath-hold) and image processing was feasible in all volunteers. Wave speed was physiological (5.8 ± 1.3 m/s ascending aorta, 5.0 ± 0.7 m/s descending aorta) and the wave intensity pattern was consistent with traditionally formulated wave intensity. Wave speed, peak forward compression wave in early systole and peak forward expansion wave in late systole at both locations exhibited overall good intra- and inter-observer variability. Patients with coronary heart disease had higher wave speed (p <0.0001), and lower forward compression wave (p <0.0001) and forward expansion wave (p <0.0005) peaks. This difference is likely related to the older age of the patients' cohort, indicating stiffer aortas, as well as compromised ventricular function due to their underlying condition.

Conclusion: A non-invasive, semi-automated and reproducible method for performing wave intensity analysis is presented. Its application is facilitated by the use of a very high temporal resolution spiral sequence. A formulation of wave intensity based on area change has also been proposed, involving no assumptions about the cross-sectional shape of the vessel.

Figure 1

Phase-contrast CMR data. Sample of modulus and phase images from the ascending (A) and descending (B) aorta of a volunteer at peak systole.

Figure 2

Calculation of wave speed. Samples of velocity (A) and area (B) curves calculated with the plug-in. These data are combined in a loop (C) whose linear slope in early systole (highlighted in red) yields wave speed c. Waveforms from both a volunteer (top row) and a patient (bottom row) are shown.

Figure 3

Waveform separation. Measured velocity U and area A can be separated into forward (+) and backward (−) components. Data from one of the volunteers and one of the patients are presented.

Figure 4

Wave intensity pattern and net wave intensity comparison between patients and volunteers. Pattern of separated and net wave intensity (dI) of one volunteer (A), highlighting a typical pattern with: dominant forward compression wave in early systole (FCW), followed by a backward compression wave (BCW) and a forward expansion wave (FEW) in late systole. Comparison with a net dI pattern of a patient with coronary heart disease (B) shows a clear difference in FCW and FEW peaks magnitude.

Figure 5

Relationship between wave speed and age. Physiological relationship between increasing age and increasing wave speed, obtained pooling data from the two cohorts of volunteers (full dots) and patients (empty dots).