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

Corina Kräuter, Ursula Reiter, Clemens Reiter, Volha Nizhnikava, Albrecht Schmidt, Rudolf Stollberger, Michael Fuchsjäger, Gert Reiter, Corina Kräuter, Ursula Reiter, Clemens Reiter, Volha Nizhnikava, Albrecht Schmidt, Rudolf Stollberger, Michael Fuchsjäger, Gert Reiter

Abstract

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.

Keywords: Fermi modeling; T1 mapping; cardiovascular magnetic resonance; dynamic contrast enhancement; myocardial blood flow; nonlinearity correction.

© 2020 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Figures

FIGURE 1
FIGURE 1
Example of a perfusion deficit (a) and a dark rim artifact (b). In both cases, the DCE image during maximum SI in the LV (left panel) shows reduced SI increase in the myocardium (arrow). During maximum SI in the myocardium or equivalently some seconds later (center panel), only the perfusion deficit persists. Only the perfusion deficit demonstrates regional consistent late gadolinium enhancement in the postcontrast T1 map (right panel). DCE = dynamic contrast‐enhanced; SI = signal intensity; LV = left ventricle.
FIGURE 2
FIGURE 2
Overview of the image processing steps for pixelwise and regional MBF quantification. The motion and coil sensitivity corrected perfusion MR data is first used to obtain the AIF from an ROI placed on one image with good contrast between LV blood pool and myocardium. Features of the extracted AIF are employed as temporal landmarks when generating a signal intensity maximum (SIM) map, which is used as a base image for the segmentation of the myocardium. The AIF and every single pixel of the perfusion series are converted from SI to CA concentration using SI model‐based nonlinearity correction and incorporating native T1 values measured from a precontrast T1 map. Pixelwise MBF is determined employing Fermi function constrained deconvolution. DCE‐MRI = dynamic contrast‐enhanced magnetic resonance imaging; AIF = arterial input function; SI = signal intensity; CA = contrast agent; [CA] = contrast agent concentration; SIM = signal intensity maximum; MBF = myocardial blood flow; ROI = region of interest.
FIGURE 3
FIGURE 3
MBF determined using normal ranges of native blood and myocardial T1 values at 3T. The mean of MBF of all patients at varying myocardial T1 values while keeping the blood T1 value fixed (a) and at varying blood T1 values while keeping the myocardial T1 value fixed (b). The mean of MBF of all patients at varying differences between blood and myocardial T1 values while keeping the blood T1 value fixed (c) and while keeping the myocardial T1 value fixed (d). MBF = myocardial blood flow.
FIGURE 4
FIGURE 4
Bland–Altman (a) and linear regression (b) plots for comparison of MBF estimates determined with subject‐specific native T1 values and the study population‐averaged native T1 values. The dark gray bar indicates the 95% confidence limits of the bias. MBF = myocardial blood flow; MBFsubj = MBF with subject‐specific native T1 values; MBFav = MBF with study population‐averaged native T1 values; SD = standard deviation of differences; LoA = limits of agreement; r = Pearson correlation coefficient.
FIGURE 5
FIGURE 5
Bland–Altman (a) and linear regression (b) plots for comparison of systolic and diastolic MBF estimates determined with systolic and diastolic native T1 values, respectively. The dark gray bar indicates the 95% confidence limits of the bias. MBF = myocardial blood flow; MBFdia = diastolic MBF with diastolic native T1 values; MBFsys = systolic MBF with systolic native T1 values; SD = standard deviation of differences; LoA = limits of agreement; r = Pearson correlation coefficient.
FIGURE 6
FIGURE 6
Bland–Altman and linear regression plots for comparison of systolic (a) and diastolic (b) MBF estimates determined with native T1 values of the respective cardiac phase and native T1 values of the other cardiac phase. The dark gray bar indicates the 95% confidence limits of the bias. MBF = myocardial blood flow; MBFsys, match = systolic MBF with systolic native T1 values; MBFsys, mismatch = systolic MBF with diastolic native T1 values; MBFdia, match = diastolic MBF with diastolic native T1 values; MBFdia, mismatch = diastolic MBF with systolic native T1 values; SD = standard deviation of differences; LoA = limits of agreement; r = Pearson correlation coefficient.
FIGURE 7
FIGURE 7
Bland–Altman and linear regression plots comparing 6‐segmental mean MBF values determined with global and 6‐segmental native myocardial T1 values. MBF of all segments of all patients (a), all segments without perfusion deficits (b), and all segments exhibiting perfusion deficits (c). The dark gray bar indicates the 95% confidence limits of the bias. MBF = myocardial blood flow; MBFglobal = MBF with global native T1 values; MBF6‐seg = MBF with 6‐segmental native T1 values; SD = standard deviation of differences; LoA = limits of agreement; r = Pearson correlation coefficient.
FIGURE 8
FIGURE 8
Boxplots of MBF in segments with and without perfusion deficits, calculated without nonlinearity correction (a), with global native T1 values (b), and with 6‐segmental native T1 values (c). Mean MBF is indicated by × and stated with the standard deviation, P refers to the comparison of the means. MBF = myocardial blood flow; MBF SI = MBF without nonlinearity correction; MBF global T1 = MBF with global native T1 values; MBF 6‐seg T1 = MBF with 6‐segmental native T1 values.

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