Influence of the cardiac cycle on time-intensity curves using multislice dynamic magnetic resonance perfusion

Impact of the Choice of Native T1 in Pixelwise Myocardial Blood Flow Quantification

BACKGROUND

Quantification of myocardial blood flow (MBF) from dynamic contrast-enhanced (DCE) MRI can be performed using a signal intensity model that incorporates T1 values of blood and myocardium.

PURPOSE

To assess the impact of T1 values on pixelwise MBF quantification, specifically to evaluate the influence of 1) study population-averaged vs. subject-specific, 2) diastolic vs. systolic, and 3) regional vs. global myocardial T1 values.

STUDY TYPE

Prospective.

SUBJECTS

Fifteen patients with chronic coronary heart disease.

FIELD STRENGTH/SEQUENCE

3T; modified Look-Locker inversion recovery for T1 mapping and saturation recovery gradient echo for DCE imaging, both acquired in a mid-ventricular short-axis slice in systole and diastole.

ASSESSMENT

MBF was estimated using Fermi modeling and signal intensity nonlinearity correction with different T1 values: study population-averaged blood and myocardial, subject-specific systolic and diastolic, and segmental T1 values. Myocardial segments with perfusion deficits were identified visually from DCE series.

STATISTICAL TESTS

The relationships between MBF parameters derived by different methods were analyzed by Bland-Altman analysis; corresponding mean values were compared by t-test.

RESULTS

Using subject-specific diastolic T1 values, global diastolic MBF was 0.61 ± 0.13 mL/(min·g). It did not differ from global MBF derived from the study population-averaged T1 (P = 0.88), but the standard deviation of differences was large (0.07 mL/(min·g), 11% of mean MBF). Global diastolic and systolic MBF did not differ (P = 0.12), whereas global diastolic MBF using systolic (0.62 ± 0.13 mL/(min·g)) and diastolic T1 values differed (P < 0.05). If regional instead of global T1 values were used, segmental MBF was lower in segments with perfusion deficits (bias = -0.03 mL/(min·g), -7% of mean MBF, P < 0.05) but higher in segments without perfusion deficits (bias = 0.01 mL/(min·g), 1% of mean MBF, P < 0.05).

DATA CONCLUSION

Whereas cardiac phase-specific T1 values have a minor impact on MBF estimates, subject-specific and myocardial segment-specific T1 values substantially affect MBF quantification.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 3.

Quantification of myocardial blood flow with cardiovascular magnetic resonance throughout the cardiac cycle

BACKGROUND

Myocardial blood flow (MBF) varies throughout the cardiac cycle in response to phasic changes in myocardial tension. The aim of this study was to determine if quantitative myocardial perfusion imaging with cardiovascular magnetic resonance (CMR) can accurately track physiological variations in MBF throughout the cardiac cycle.

METHODS

30 healthy volunteers underwent a single stress/rest perfusion CMR study with data acquisition at 5 different time points in the cardiac cycle (early-systole, mid-systole, end-systole, early-diastole and end-diastole). MBF was estimated on a per-subject basis by Fermi-constrained deconvolution. Interval variations in MBF between successive time points were expressed as percentage change. Maximal cyclic variation (MCV) was calculated as the percentage difference between maximum and minimum MBF values in a cardiac cycle.

RESULTS

At stress, there was significant variation in MBF across the cardiac cycle with successive reductions in MBF from end-diastole to early-, mid- and end-systole, and an increase from early- to end-diastole (end-diastole: 4.50 ± 0.91 vs. early-systole: 4.03 ± 0.76 vs. mid-systole: 3.68 ± 0.67 vs. end-systole 3.31 ± 0.70 vs. early-diastole: 4.11 ± 0.83 ml/g/min; all p values <0.0001). In all cases, the maximum and minimum stress MBF values occurred at end-diastole and end-systole respectively (mean MCV = 26 ± 5%). There was a strong negative correlation between MCV and peak heart rate at stress (r = -0.88, p < 0.001). The largest interval variation in stress MBF occurred between end-systole and early-diastole (24 ± 9% increase). At rest, there was no significant cyclic variation in MBF (end-diastole: 1.24 ± 0.19 vs. early-systole: 1.28 ± 0.17 vs.mid-systole: 1.28 ± 0.17 vs. end-systole: 1.27 ± 0.19 vs. early-diastole: 1.29 ± 0.19 ml/g/min; p = 0.71).

CONCLUSION

Quantitative perfusion CMR can be used to non-invasively assess cyclic variations in MBF throughout the cardiac cycle. In this study, estimates of stress MBF followed the expected physiological trend, peaking at end-diastole and falling steadily through to end-systole. This technique may be useful in future pathophysiological studies of coronary blood flow and microvascular function.