Impact of native T1 on pixel-wise myocardial blood flow quantification
Corina Kräuter1,2, Ursula Reiter1, Clemens Reiter1, Albrecht Schmidt3, Michael Fuchsjäger1, Rudolf Stollberger2, and Gert Reiter4

1Department of Radiology, Medical University of Graz, Graz, Austria, 2Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 3Department of Internal Medicine, Medical University of Graz, Graz, Austria, 4Research and Development, Siemens Healthineers, Graz, Austria


Native myocardial T1 varies between subjects and between segments, yet its impact on pixel-wise quantification of myocardial blood flow (MBF) has not been studied. 15 patients with coronary heart disease underwent 3T cardiac magnetic resonance native myocardial T1 mapping and perfusion imaging at rest. Nonlinearity correction for MBF calculation was performed employing literature native T1 values and patient-specific global as well as local native T1, respectively. Since reference T1 revealed substantial individual MBF errors and application of patient-specific global T1 overestimated MBF in perfusion deficit regions compared to local T1, patient-specific local native T1 should be employed for MBF quantification.


Quantification of myocardial blood flow (MBF) from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires considering the nonlinear relationship between signal intensity (SI) and contrast agent concentration. While employing an SI model1,2 which incorporates native T1 of blood and myocardium is a popular approach for nonlinearity correction, the impact of native T1 on MBF quantification has not been studied. Since native T1 not only depends on acquisition technique but also varies between subjects and myocardial segments,3,4 the aim was to study its influence on pixel-wise MBF quantification by evaluating MBF estimates determined using native T1 normal values reported in literature and patient-specific global as well as local native T1, respectively.


15 patients with coronary heart disease (age = 62±7 years; male/female = 13/2) underwent 3T native myocardial T1 mapping and DCE-MRI at rest. T1 mapping was performed using an electrocardiogram-(ECG)-gated modified Look-Locker inversion recovery (MOLLI) sequence with automated motion correction and T1 map generation.5,6 DCE-MRI was performed with gadolinium-based contrast agent at a dose of 0.05 mmol/kg employing an ECG-gated single-shot saturation recovery fast low angle shot (SR FLASH) sequence with automated coil sensitivity and motion correction.7 Myocardial segments exhibiting perfusion deficits were identified by visual analysis of DCE-MRI series. Figure 1 shows an overview of the image processing steps for pixel-wise MBF quantification. All image processing was done using in-house software implemented in Matlab (MathWorks Inc., Natick, MA), except native T1 map segmentation, which was performed using dedicated software (cvi42, Circle Cardiovascular Imaging Inc., Calgary, Canada). Mid-ventricular short axis slices were evaluated in several experiments: 1) MBF maps were calculated without T1 mapping, namely without nonlinearity correction and by using the ranges of native blood and myocardial T1 normal values at 3T reported in literature,8-10 respectively. 2) MBF maps were calculated using individual patients’ and the study population’s average blood and myocardial T1 values, respectively. 3) MBF was quantified using local myocardial T1 according to the six American Heart Association (AHA) segments for a mid-ventricular short axis slice. For assessment of local MBF, segmental MBF values were calculated as mean MBF of all pixels of each segment. Comparisons of MBF estimates calculated from different native T1 values were performed using paired t-test and Bland-Altman analysis; segmental native T1 values were compared using Welch’s t-test.


Global MBF determined without nonlinearity correction was significantly higher than global MBF determined using patient-specific global T1 values (MBF SI, 0.71±0.11 ml·min-1·g-1; MBF global T1, 0.61±0.13 ml·min-1·g-1; p=0.0002). Using the range of native T1 normal values reported in literature yielded decreasing global MBF for increasing myocardial T1 values and the opposite behavior for increasing blood T1 values (Figure 2). Comparing MBF determined from patient-specific global T1 with MBF determined from average T1 yielded no significant bias (MBF patient-specific T1, 0.61±0.13 ml·min-1·g-1; MBF average T1, 0.62±0.15 ml·min-1·g-1; p=0.8784) but a large standard deviation of differences of 0.07 ml·min-1·g-1 (Figure 3). Myocardial segments with perfusion deficits were identified in six patients. Mean T1 in myocardial segments with perfusion deficits was higher than mean T1 in segments without perfusion deficits (1425±170 ms vs. 1235±66 ms, p=0.0024). Segmental mean MBF values calculated from global and local native myocardial T1, respectively, were not significantly different (Figure 4A). However, considering segments with and without perfusion deficits individually revealed a decrease in MBF for segments with perfusion deficits if local instead of global native T1 was used; segments without perfusion deficits showed the opposite behavior (Figure 4B and 4C).


The range of average global MBF estimates determined employing different native blood and myocardial T1 normal values reported in literature was large, even exceeding average MBF estimates determined without nonlinearity correction. Figure 2 also shows that myocardial and blood T1 had an equally strong effect on MBF estimates within the literature T1 ranges. Employing specifically the study population’s average native T1 revealed that an individual error up to 30% can be expected if average instead of patient-specific T1 is used. If native myocardial T1 varies locally, contrary T1 effects on MBF estimates may cancel each other out in regional analysis, which was observed when considering segments with and without perfusion deficits individually. The fact that segments with perfusion deficits showed a significantly lower MBF if local instead of global native T1 was used matches the observation of higher native T1 in segments with perfusion deficits.


Native myocardial and blood T1 have substantial impact on MBF estimates determined using SI model based nonlinearity correction. To avoid overestimation of MBF in myocardial regions with perfusion deficits, patient-specific local native T1 should be employed for MBF quantification.


No acknowledgement found.


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Figure 1: Overview of image processing steps. First, the arterial input function (AIF) was obtained from a central region of interest in the left ventricular blood pool. Features of the AIF were employed as temporal landmarks when generating a signal intensity maximum (SIM) map, which was then used as base image for segmentation of the myocardium. The AIF and every pixel of the DCE-MRI series were converted from signal intensity (SI) to contrast agent concentration ([CA]) using SI model based nonlinearity correction and incorporating native T1 measured from a T1 map. Pixel-wise MBF was determined employing Fermi function constrained deconvolution.2

Figure 2: MBF determined using the ranges of native blood and myocardial T1 normal values at 3T reported in literature. Mean of global MBF of all patients (A) at varying myocardial T1 while keeping blood T1 fixed and (B) at varying blood T1 while keeping myocardial T1 fixed.

Figure 3: Bland-Altman plot for comparison of global MBF estimates determined with patient-specific native T1 and the study population’s average native T1, respectively. SD = standard deviation of differences; LoA = limits of agreement.

Figure 4: Bland-Altman plots comparing segmental mean MBF values determined with global and local native myocardial T1, respectively. MBF of (A) all segments of all patients, (B) all segments without perfusion deficits and (C) all segments exhibiting perfusion deficits. SD = standard deviation of differences; LoA = limits of agreement.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)