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Comprehensive Validation of Cardiovascular Magnetic Resonance
Techniques for the Assessment of Myocardial Extracellular Volume
Miller et al: Validation of CMR Assessment of ECV
Christopher A. Miller, BSc, MBChB1,2,3; Josephine Naish, PhD2; Paul Bishop, MBChB4;
Glyn Coutts, PhD5; David Clark, BSc;6 Sha Zhao, PhD2; Simon G. Ray, MD1,3; Nizar Yonan, MD1;
Simon G. Williams, MD1; Andrew S. Flett, MD7,8; James C. Moon, MD7,8; Andreas Greiser, PhD;9
Geoffrey J.M. Parker, PhD2; Matthias Schmitt, MD, PhD1,2
1North West Heart Centre and The Transplant Centre, University Hospital of South Manchester,
Wythenshawe Hospital, Manchester, UK. 2Centre for Imaging Sciences & Biomedical Imaging
Institute, University of Manchester, UK. 3Cardiovascular Research Group, University of Manchester,
UK. 4Department of Pathology, University Hospital of South Manchester, Wythenshawe, Manchester,
UK. 5Christie Medical Physics and Engineering, The Christie Hospital, Manchester, UK. 6Alliance
Medical Cardiac MRI Unit, Wythenshawe Hospital, Manchester, UK. 7The Heart Hospital, London,
UK. 8Institute of Cardiovascular Science, University College London, London, UK. 9Healthcare
Sector, Siemens AG, Erlangen, Germany.
Correspondence to Dr Christopher Miller North West Regional Heart Centre University Hospital of South Manchester Wythenshawe, Manchester, UK Tel: +44 161 291 4940 Fax: +44 161 291 4940 Email: [email protected] DOI: 10.1161/CIRCIMAGING.112.000192
Journal Subject Codes: [30] CT and MRI; [124] Cardiovascular imaging agents/Techniques
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Abstract
Background—Extracellular matrix expansion is a key element of ventricular remodeling and a
potential therapeutic target. Cardiovascular magnetic resonance (CMR) T1-mapping techniques are
increasingly used to evaluate myocardial extracellular volume (ECV), however the most widely
applied methods are without histological validation. Our aim was to perform comprehensive validation
of; A, dynamic-equilibrium CMR (DynEq-CMR), where ECV is quantified using hematocrit-adjusted
myocardial and blood T1 values measured before and after gadolinium bolus; and B, isolated
measurement of myocardial T1, used as an ECV surrogate.
Methods and Results—Whole-heart histological validation was performed using 96 tissue samples,
analyzed for picrosirius red collagen volume fraction (CVF), obtained from each of 16-segments of
the explanted hearts of 6 patients undergoing heart transplantation who had prospectively undergone
CMR prior to transplantation (median interval between CMR and transplantation 29 days). DynEq-
CMR-derived ECV was calculated from T1 measurements made using a modified look locker
inversion recovery sequence before and 10- and 15-minutes post-contrast. In addition, ECV was
measured 2-20 minutes post-contrast in 30 healthy volunteers. There was a strong linear relationship
between DynEq-CMR-derived ECV and histological CVF (p<0.001; within-subject r=0.745, p<0.001;
r2=0.555; between-subject r=0.945, p<0.01, r2=0.893; for ECV calculated using 15-minute post-
contrast T1). Correlation was maintained throughout the entire heart. Isolated post-contrast T1
measurement showed significant within-subject correlation with histological CVF (r=-0.741, p<0.001;
r2=0.550 for 15 minute post-contrast T1), but between-subject correlations were not significant.
DynEq-CMR-derived ECV varied significantly according to contrast dose, myocardial region and
gender.
Conclusions—DynEq-CMR-derived ECV shows a good correlation with histological CVF throughout
the whole heart. Isolated post-contrast T1 measurement is insufficient for ECV assessment.
Key Words: collagen, histopathology, magnetic resonance imaging, myocardial fibrosis
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Expansion of the myocardial interstitial space is a feature common to a range of cardiac pathologies,
and appears fundamental to the process of adverse left ventricular (LV) remodeling.1-4 Interstitial
expansion, in part due to accumulation of collagen, is associated with changes in mechanical and
electrical properties of the myocardium and as such may represent a sentinel phenotype, transitional
between ‘healthy’ myocardium and ‘diseased’ myocardium associated with increased mortality risk.5-9
Furthermore, interstitial expansion is reversible and a potential therapeutic target.10-12 Quantification of
the myocardial interstitial space, or extracellular volume (ECV), may therefore represent an important
diagnostic and prognostic biomarker.
Endomyocardial biopsy represents the current gold standard for assessment of the myocardial
interstitium, however its invasive nature, and lack of whole heart coverage, restrict its clinical use for
this purpose. Cardiovascular magnetic resonance (CMR) techniques that potentially allow non-
invasive evaluation of the interstitial space have generated considerable recent interest.
Gadolinium chelates are standard extracellular contrast agents that potently shorten T1 relaxation time.
Gadolinium concentration is directly related to change in the relaxation rate R1 (where R1 is the
reciprocal of T1). Therefore T1 measurement (‘mapping’) before and after contrast administration can
be used to calculate gadolinium concentration in myocardium and blood. At contrast agent
equilibrium, gadolinium concentration is equal in blood and myocardium, and since the volume of
distribution of gadolinium (or ECV) in blood is known from the hematocrit, so myocardial ECV can
be calculated.
This approach requires a steady state to exist between blood and myocardial contrast agent in order for
the effect of contrast agent kinetics to be removed. Using a primed gadolinium infusion to achieve
contrast agent equilibrium (Equilibrium Contrast CMR, EQ-CMR), Flett et al13 demonstrated a strong
correlation between CMR measurement of myocardial ECV and histological collagen volume fraction.
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However, due to the potentially cumbersome nature of this technique, two alternative methods of
assessing ECV have become more widely adopted.
The first, which involves a contrast agent bolus (i.e. without a subsequent infusion), assumes contrast
agent kinetic effects to be negligible due to a dynamic equilibrium between blood and myocardium
(dynamic-equilibrium CMR; DynEq-CMR).9, 14-19 In the second method, an isolated myocardial T1
measurement is made at a fixed time-point following a bolus of contrast agent (i.e. without pre-
contrast, or blood T1 measurements) and is used as a surrogate measure of ECV (“isolated-T1”).20-23
However, despite these techniques being applied to a rapidly expanding number of pathologies, both
have potential shortcomings; the assumed dynamic equilibrium proposed in the first method may not
always hold true in reality, and the second technique may be confounded by factors such as body fat
percentage, renal function and hematocrit. Systematic in vivo histological validation of these
techniques in humans is lacking.24-26
The aims of the current study were to first, provide comprehensive, whole-heart, histological
validation of the DynEq-CMR technique for measurement of myocardial ECV. This, at the same time,
allowed assessment of the validity of isolated-T1 technique. Second, we aimed to assess the effect of
contrast agent dose, post-contrast acquisition time, myocardial regionality, cardiac cycle and gender
on DynEq-CMR ECV measurement.
Methods
Study Design
All research was carried out at University Hospital of South Manchester NHS Trust, UK. An ethics
committee of the UK National Research Ethics Service approved the study and written informed
consent was obtained from all participants. The work was conducted according to the Helsinki
Declaration.
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The study comprises 3 parts: (1) Phantom studies; performed in order to validate the accuracy of the
T1 mapping sequence used and to calculate a T1 heart-rate correction algorithm (see Online Data
Supplement); (2) Histological validation. CMR was performed prospectively in patients awaiting heart
transplantation. When these patients subsequently underwent transplantation, the explanted hearts
were used to provide whole-heart histological validation of the DynEq-CMR and isolated-T1
techniques; and (3) The effect of contrast dose, post-contrast acquisition time, myocardial regionality,
cardiac cycle and gender on DynEq-CMR were assessed in healthy volunteers.
CMR imaging
All CMR imaging was performed with the same 1.5 tesla scanner (Avanto; Siemens Medical Imaging,
Germany) equipped with a 32-element phased-array coil.
T1 measurements were made using an electrocardiogram (ECG)-gated single-shot modified Look
Locker inversion recovery (MOLLI) sequence as described by Messroghli et al.27 Typical parameters
were FOV 340x255mm, matrix 192x138, 8mm slice thickness, flip angle 35°, iPAT factor (GRAPPA)
2 with 24 reference lines, 6/8 partial Fourier k-space sampling, acquisition time 201ms for a single
image, initial effective inversion time (TIeff) 100ms with a TIeff increment of 80ms. To sample T1
recovery, serial single-shot diastolic images were acquired every heart beat after 3 non-selective
adiabatic inversion pulses (i.e., 3, 3, and 5 images after each inversion pulse, totaling 11 images), with
3 dummy heart beats before the second and third inversion pulses to allow recovery (17 heart beat
total acquisition duration).
The gadolinium contrast agent used throughout the study was gadopentetate dimeglumine (Gd-DTPA;
Magnevist; Bayer Healthcare), administered as a single bolus of 2mL/s followed by a 30mL saline
chaser bolus delivered at the same flow rate using a power injector.
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Histological validation
All patients on the heart transplant waiting list at University Hospital of South Manchester NHS Trust,
UK (1 of 6 UK adult heart transplant centers), between January 1st 2011 and July 1st 2012 were
screened for study eligibility. Of the 54 patients on the waiting list during this period, 41 had an
intracardiac device that prohibited CMR, and 2 of the 13 patients without devices were deemed too
unwell for CMR by the supervising medical team. Eleven patients were therefore invited for CMR, of
which 2 refused consent. The 9 remaining patients underwent CMR, of which 6 underwent heart
transplantation. As pre-specified, no patients with acute myocardial inflammation (known or suspected
myocarditis, acute myocardial infarction) or cardiac amyloidosis were included.
The CMR protocol included LV short-axis MOLLI sequence acquisition at basal, mid and apical
ventricular levels before, and 10- and 15-minutes after a bolus of 0.20mmol/kg Gd-DTPA contrast
agent (Figure 1). Standard long- and short-axis steady-state free precession (SSFP) cine imaging was
performed in order to assess LV mass and volumetric parameters. Standard late gadolinium
enhancement (LGE) imaging was performed at least 10 minutes following contrast agent
administration using spoiled gradient echo segmented inversion recovery, and phase sensitive
inversion recovery (PSIR) segmented gradient echo, sequences.28 Blood samples were taken at the
time of CMR in order to measure hematocrit.
At the time of transplantation, the explanted hearts were immediately fixed in 10% buffered formalin.
The hearts were cut at basal, mid and apical LV levels (using the MOLLI slice positions as guidance,
Figure 1) and 16 tissue blocks were taken from the LV of each heart (96 samples in total) according to
the American Heart Association/American College of Cardiology 16-segment model29 before being
embedded in paraffin and stained with picrosirius red. High-power magnification (x200) digital
images, excluding perivascular areas, underwent automated image analysis (macro written in
ImageJ30). As described previously,13 a combination of standard deviation from mean signal and
isodata automatic thresholding derived the collagen area, expressed as a percentage of total
myocardial area, excluding fixation artifact. Twelve high-power fields were assessed per segment
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(1152 high-power fields were assessed in total) and mean histological collagen volume fraction (CVF,
%) and CVF heterogeneity (coefficient of variation between fields) were obtained. In order to
determine whether CVF measurements were affected by the degree of magnification used to acquire
the histological images, CVF measurements were repeated at 2 additional magnifications (x50 and
x100) in 2 randomly selected tissue blocks from each patient (12 samples (13%) in total, i.e. an
additional 288 histological images).
Effect of contrast agent dose, acquisition time, myocardial regionality, cardiac cycle and gender
Thirty completely asymptomatic healthy volunteers, with no known risk factors or history of cardiac
disease, normal physical examination and normal ECG, underwent CMR. (Subjects were not patients
who had been referred for CMR that was subsequently found to be normal). CMR (cine and LGE
imaging) was normal in all cases.
Volunteers were prospectively split into 3 age- and sex-matched groups, and received Gd-DTPA
contrast agent at the following doses: Group A 0.10mmol/kg, Group B 0.15mmol/kg and Group C
0.20mmol/kg.
The CMR protocol included a MOLLI sequence acquisition in short-axis at mid-ventricular level
before and at 2, 4, 6, 8, 10, 12, 14, 15, 16, 18 and 20 minutes post-contrast administration. In addition,
MOLLI imaging was also performed in early systole (150ms after the R wave) pre-contrast and at 10
and 15 minutes post-contrast administration. Standard long- and short-axis SSFP cine imaging was
performed in order to assess LV mass and volumetric parameters. LGE imaging of the entire heart in
short-axis was performed using single-shot PSIR imaging.31 Blood samples were taken at the time of
CMR in order to measure the hematocrit.
CMR image analysis
For T1 relaxation time measurements, endocardial and epicardial contours were drawn on the MOLLI
images using Osirix Imaging Software (Pixmeo; Switzerland; v4.0). An additional region of interest
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(ROI) was drawn in the blood pool, avoiding papillary muscles and trabeculae, and the anterior right
ventricular septal insertion point was marked. ROIs were manually translated on each TIeff time
image to allow motion compensation. The same ROIs were used on corresponding pre- and post-
contrast images. To obtain voxel-wise T1 relaxation times, a 3 parameter fit to the signal intensity, S as
a function of TIeff was performed according to S(TIeff) = A - Be(-TIeff/T1*) and T1 was calculated as
T1=T1*((B/A)-1). Fitting was carried out using MatLab (The MathWorks; USA; vR2009a). Using the
anterior septal insertion point as reference, T1 maps were segmented according to the American Heart
Association/American College of Cardiology model.29 Mean voxel T1 relaxation time in each segment
before and after contrast was then used to calculate segmental myocardial ECV according the
following formula:
Extracellular volume fraction (ECV) = x (1- hematocrit).
Where the partition coefficient, = R1(myocardium) / R1(blood). R1 is proportional to contrast
agent concentration. R1 = R1(post-contrast) – R1(pre-contrast).
Separate ECV calculations were performed using the 10- and 15-minute post-contrast T1 values
acquired in patients awaiting transplantation, and using the 2-20 minute post-contrast T1 values
acquired in healthy subjects.
Pre-contrast and 15-minute post-contrast MOLLI image analysis was independently repeated in 50%
(3 patients, 48 segments) of patients who had CMR prior to undergoing transplantation, and 33% of
the healthy subjects (10 patients, 60 segments) by a second observer in order to assess interobserver
variability of ECV measurement. Patients were randomly selected.
LV mass, end-diastolic volume, end-systolic volume and ejection fraction were quantified from SSFP
images using CMRtools (Cardiovascular Imaging Solutions, UK). LGE images were reported visually
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by 2 experienced operators (C.A.M and M.S.). Segments were recorded as containing no LGE, infarct-
typical LGE or infarct atypical LGE.
Statistical analysis
All data was analyzed in a blinded fashion, with independent analysis of CMR (C.A.M. and M.S.) and
histology (P.B.) data. Statistical analysis was performed using SPSS (IBM, USA; v19). Continuous
variables are expressed as mean ± SD. For the histological validation, regression analysis using
generalized estimating equations (GEE) in order to adjust for repeated measurements within each
subject was used to assess the relationship between DynEq-CMR-derived ECV and histological CVF.
Within subject and between subject correlations were calculated using the methods described by Bland
et al.32, 33 The same analyses were used to assess the relationship between ‘isolated’ post-contrast T1
measurements and histological CVF. As pre-specified, analyses were repeated after excluding
segments containing infarct-typical LGE and after excluding segments containing any LGE (infarct-
typical or -atypical). Characteristics of healthy volunteers were compared across the three groups
using one-way analysis of variance, as were pre-contrast T1 measurements. The effect of contrast
agent dose on repeated post-contrast T1 measurements, and the effect of contrast agent dose,
myocardial region and sex on repeated ECV measurements, were assessed using a repeated measures
analysis of variance model. In order to assess the change in ECV measurement over time, ECV values
calculated at 2- and 20-minutes post-contrast were compared within each contrast agent dose group
using a paired t-test. Diastolic and systolic ECV measurements were compared within each contrast
dose group using a paired t-test. Interobserver agreement was evaluated using the repeatability
coefficient,34 which calculates the range within which measurements by two different observers are
expected to lie for 95% of subjects, and intraclass correlation coefficient (ICC).
Results
Histological validation
Characteristics of each of the six patients who received a heart transplant following CMR are
summarized in Table 1. Median time between CMR and transplantation was 29 days (in 5 patients the
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interval was 40 days or less, but in 1 patient the interval was 276 days). Mean histological CVF was
21.6±12.4%; range 3.3% to 55.2% (Figure 2). Mean DynEq-CMR-derived ECV calculated using the
10-minute post-contrast T1 values was 43.8±7.0%; range 31.1% to 65.1% (median within subject
range 20.2%), and using the 15-minute post-contrast T1 values was 43.9±6.7%; range 30.9% to 68.4%
(median within-subject range 18.2%).
There was a significant linear relationship between ECV measured by DynEq-CMR, using both the
10- or 15-minute post-contrast T1 values, and histological CVF (p<0.001 for both using GEE; Figure
3). Whilst high for both, the correlation was marginally higher when ECV was calculated using the
15-minute post-contrast T1 values (within-subject r=0.745, p<0.001; r2=0.555; between-subject
r=0.945, p<0.01, r2=0.893) compared to when the 10-minute post contrast values were used (within-
subject r=0.728, p<0.001; r2=0.530; between-subject r=0.880, p<0.01, r2=0.774). The linear
regression equation for the relationship between DynEq-CMR-derived ECV using the 15-minute post-
contrast T1 values and histological CVF was: histological CVF = 1.45 x ECV – 42. The correlation
between mean ECV and CVF on a per individual basis was r=0.945, p=0.004; r2=0.893 (Figure 4).
There was a significant linear relationship between isolated post-contrast T1 measurements made at
10- and 15-minutes post-contrast and histological CVF (p<0.001 for both using GEE; Figure 3),
however this was largely driven by the within-subject correlations (isolated 10 minute post-contrast
T1: r=-0.690, p<0.001; r2=0.475; isolated 15 minute post-contrast T1: r=-0.741, p<0.001; r2=0.550).
The between-subject correlations were not significant (isolated 10 minute post-contrast T1: r=-0.028,
p=0.96; isolated 15 minute post-contrast T1: r=-0.207, p=0.69). There was no correlation between pre-
contrast T1 values and histological CVF (p=0.437 using GEE; within-subject r=0.138, p=0.192;
between-subject r=0.199, p=0.71).
In light of these findings, all subsequent analyses were performed using ECV calculated with DynEq-
CMR using the 15-minute post-contrast T1 values.
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There was a significant linear relationship between ECV and histological CVF in septal and non-septal
segments (p<0.001 using GEE for both), with minimal difference in within-subject and between
subject correlations (septum: within-subject r=0.750, p<0.001; r2=0.560; between-subject r=0.940,
p<0.01, r2=0.884; non-septum: within-subject r=0.722, p<0.001; r2=0.521; between-subject r=0.941,
p<0.01, r2=0.885). The significant linear relationship between ECV and histological CVF was also
maintained across ventricular levels (p<0.001 using GEE for both basal and mid ventricular segments,
and apical segments), with minimal difference in within-subject and between subject correlations
(basal and mid ventricle: within-subject r=0.727, p<0.001; r2=0.529; between-subject r=0.960,
p<0.01, r2=0.922; apical ventricle: within-subject r=0.748, p<0.001; r2=0.559; between-subject
r=0.892, p<0.01, r2=0.796).
Infarct-typical LGE was present in 32 segments and infarct-atypical LGE was present in a further 10
segments. Only 1 segment displayed LGE throughout its entirety. When segments containing infarct-
typical LGE were excluded (analysis performed on 64 segments), the linear relationship between ECV
and histological CVF was maintained (p<0.001 using GEE; within-subject r=0.682, p<0.001;
r2=0.465; between-subject r=0.912, p<0.01, r2=0.832; Figure 5). Likewise, the linear relationship
between ECV and histological CVF remained when segments containing any LGE (infarct-typical and
infarct-atypical patterns) were excluded (analysis performed on 54 segments) (p<0.001 using GEE;
within-subject r=0.652, p<0.001; r2=0.426; between-subject r=0.843, p<0.02, r2=0.711; Figure 5).
Effect of contrast agent dose, myocardial regionality, cardiac cycle and gender on ECV
There were no significant differences in subject characteristics between contrast agent-dose groups
(Table 2).
Pre-contrast myocardial (group A 1051±49ms; group B 1045±49ms; group C 1040±43ms; p=0.87)
and blood (A 1678±98ms; B 1645±118ms; C 1686±101ms; p=0.66) T1 relaxation times were not
significantly different between groups. Mean myocardial (A 542±65ms; B 465±69ms; C 407±55ms;
p<0.001) and blood (A 407±73ms; B 307±67ms; C 252±48ms; p<0.001) T1 relaxation times averaged
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over all time points post-contrast shortened significantly as contrast dose increased (Figure 6).
Measurements of mean ECV averaged over all time points were significantly higher in group A
compared to group B and group C (A 27.7±3.7%; B 25.8±3.4%; C 25.8±2.8%; p<0.001), but the
difference between groups B and C was not significant (Figure 6). Mean measured ECV increased
linearly over time in each group; between the 2 and the 20 minute post-contrast acquisitions measured
ECV increased from 27.2±2.7% to 28.8±3.4%, p=0.020 in group A; 25.3±2.8% to 26.5±3.2%,
p=0.004 in group B; and 25.2±1.7% to 26.2±2.1%, p=0.068 in group C.
ECV varied significantly between myocardial regions. In each group ECV was highest in the septum
and lowest in the lateral wall, as exemplified by group C (results for other groups were similar):
anterior 24.9±2.5%; septal 27.0±2.5%; inferior 25.2±2.1%; lateral 24.2±2.2%; p<0.001. ECV did not
differ significantly between diastole and systole in any group (A diastolic ECV 28.5±3.2%, systolic
ECV 28.1±3.4%, p=0.26; B diastolic ECV 26.0±3.4%, systolic ECV 25.9±3.3%, p=0.74; C diastolic
ECV 25.7±2.0%, systolic ECV 25.5±2.6%, p=0.56).
Mean ECV was significantly higher in females than in males in each group (A female 29.6±3.0%,
male 25.4±3.0%, p<0.001; B female 27.4±2.7%, male 23.6±2.9%, p<0.001; C female 26.1±2.8%,
male 24.7±2.5%, p=0.027).
Comparison of ECV in health and disease
In healthy subjects receiving 0.20mmol/kg contrast agent (i.e. group C), mean segmental ECV
calculated using 15-minute post-contrast T1 values was 25.5±2.6%. In patients prior to transplantation
(who received the same type and dose of contrast agent), mean ECV, also calculated using 15-minute
post-contrast T1 values, in segments without LGE was 41.4±5.0% (p<0.001 when compared with
healthy subjects), and in segments with LGE was 47.0±7.4% (p<0.001 when compared with healthy
subjects and when compared with segments without LGE; Figure 7). In patients prior to
transplantation, there was a significant difference in mean ECV between segments without LGE,
segments with infarct-atypical LGE (45.8±4.7%) and segments with infarct-typical LGE (47.4±8.1%;
2222222±2±2±2±2±2±2±2.2.2.2.2.22.2%;%;%;%;%;%;%; ppppppp<0<0<0<0<0<0<0.0.0.0.000001010101010101..
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p=0.26; B diastolic ECV 26.0±3.4%, systolic ECV 25.9±3.3%, p=0. 4
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p<0.001). On post-hoc analysis the difference in ECV between segments with infarct-typical LGE and
segments without LGE was significant (p<0.001) but the difference between segments with infarct-
atypical LGE and segments without LGE was not (p=0.125; Figure 7).
Mean pre-contrast myocardial T1 relaxation time was significantly higher in patients prior to
transplantation than in healthy subjects (1187±163ms versus 1045±46ms, p<0.001).
Repeatability
Interobserver variability for DynEq-CMR-measurement of ECV in healthy subjects was 2.3% (ICC
0.932), ranging from 1.4% (ICC 0.969) for septal segments, to 2.7% (ICC 0.914) for non-septal
segments. Interobserver variability for DynEq-CMR-measurement of ECV in patients prior to
transplantation was 5.1% (ICC 0.883), ranging from 2.9% (ICC 0.944) for septal segments, to 5.9%
(ICC 0.850) for non-septal segments, and ranging from 4.5% (ICC 0.907) for basal and mid
ventricular segments, to 6.7% (ICC 0.798) for apical segments. For histological CVF assessment,
interobserver variability was 2.8% (ICC 0.994), however, within the same tissue sample, CVF was not
uniform between high-powered fields, with a mean standard deviation of 49% normalized to CVF%.
Mean CVF measurements did not vary significantly according to histological magnification (x200
14.0±11.2%; x50 14.4±12.5%; x100 14.0±11.7%; p=0.507).
Discussion
This is the first human study to provide comprehensive histological validation of the DynEq-CMR
technique for quantification of myocardial ECV, and the first to provide LV histological validation of
the isolated post-contrast T1 measurement technique. Indeed, this is the largest histological validation,
in terms of number of tissue samples, of any CMR ECV-quantification method, and the only to
provide whole heart tissue corroboration.
The DynEq-CMR technique relies on the assumption of a two-compartment model, whereby a steady
state is assumed to exist between the intravascular and interstitial compartments, with equal contrast
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n-septal segments, and ranging from 4.5% (ICC 0.907) for basal and m
nts, to 6.7% (ICC 0.798) for apical segments. For histological CVF ass
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agent concentrations in each, due to rapid exchange of contrast agent between the compartments. The
small increase in ECV over time seen here (Figure 6), which is in keeping with the findings of Kawel
et al19 and Shelbert et al,14 suggests that the two-compartment model may be limited and an
incomplete dynamic equilibrium between blood and myocardium is achieved. The lack of equilibrium
may be due to penetration of gadolinium into other ‘compartments’ such as bone and synovial fluid,
and due to faster renal clearance than exchange rate between compartments. Indeed the latter reason in
particular may explain the significantly higher ECV values, and greatest increase in ECV over time,
seen in the healthy subjects receiving the lowest contrast dose.
As a result of the incomplete dynamic equilibrium, the correlation between DynEq-CMR-derived
ECV and histological CVF changed over time. Nevertheless, the relationship between DynEq-CMR-
derived ECV and histological CVF in the current study is comparable to that found in the study by
Flett et al13 using the EQ-CMR technique (r2=0.80), in which basal septal ECV was compared with
histological CVF of tissue obtained from the basal septum at surgical biopsy in patients undergoing
valve replacement for aortic stenosis (18 patients) or myectomy for hypertrophic cardiomyopathy (8
patients). The DynEq-CMR technique used here however offers advantages over the EQ-CMR
technique in terms of being substantially simpler to perform and less time consuming, and could easily
be incorporated into routine scanning protocols.
There was a strong linear relationship between CMR-derived ECV and histological CVF across the
whole spectrum of ECV and CVF, which is an important finding as it means that ECV is suitable for
stratifying patients based on CVF. Nevertheless, the intercept of the linear regression equation for the
relationship between ECV and CVF was not zero and the slope was different from one (unity),
findings which are in keeping with those of Flett et al13 and Messroghli et al.25 As discussed by Di
Carli et al,35 the y-intercept represents a surrogate for all of the multiple components of the myocardial
interstitium, which as well as collagen include non-collagenous proteins, fibroblasts, endothelial cells
and vessels. In addition, whilst we excluded patients with specific myocardial inflammatory
conditions per se, all subjects had end-stage heart failure and as such myocardial inflammation, and
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of tissue obtained from the basal septum at surgical biopsy in patients u
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hence edematous expansion of the interstitial space without collagen deposition, may have been
present, which may be one explanation for the higher mean pre-contrast T1 seen in pre-transplant
patients compared to healthy volunteers. As such it is logical to assume that CMR-derived ECV does
not only reflect CVF, however it appears that excess ECV beyond the baseline is explained by
increases in CVF, provided other causes of ECV expansion are excluded. The y-intercept and slope of
the equation may also have been influenced by changes in the extracellular compartment relating to
the tissue processing itself, and by the CVF quantification technique.
Flett et al13 excluded patients who demonstrated any LGE in the region of the biopsy. Wong et al,9
who demonstrated an association between DynEq-CMR-derived ECV and short-term all-cause
mortality (but without histological corroboration), excluded regions of myocardium in the vicinity of
infarct-typical LGE but included myocardium displaying infarct-atypical LGE. Others have quantified
ECV in myocardium exhibiting infarct-typical LGE (also without histological corroboration).16, 36
Expansion of the myocardial interstitial space appears to occur as a continuous spectrum. As such we
included all tissue samples in our analysis, regardless of LGE ‘status’, and simply quantified ECV in
each. Nevertheless, our study demonstrated that the correlation between DynEq-CMR-derived ECV
and histological CVF remained strong when segments containing any LGE were excluded and when
segments containing infarct-typical LGE only were excluded, as well as when segments containing
LGE were included.
Indeed, this study serves to highlight the potential shortcomings of the LGE technique relating to its
relative signal intensity nature. Whilst the LGE technique is very well established for infarction
detection and quantification, the ability of LGE to detect and quantify myocardial fibrosis in non-
ischemic cardiomyopathies is much less well validated. Myocardial fibrosis exists as a spectrum from
diffuse to focal. Such homogeneity, or lack of heterogeneity, is problematic for the LGE technique
since it relies on “normal” regions of myocardium to serve as reference for nulling. As a result, LGE
quantification (and hence “quantification of fibrosis” via the LGE technique) in non-ischemic
cardiomyopathies is dependent on the signal intensity threshold used.37 As demonstrated in Figure 5,
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in the current study there was considerable overlap in histological CVF between segments that
displayed LGE and those that did not (although it should be recognized that CVF and ECV values in
the current study represent means for entire segments, with only 1 segment displaying LGE
throughout). Furthermore the difference in mean ECV between segments without LGE and those with
atypical LGE was not significant, although overall mean differences in ECV may not be reflective of
differences in individual patients. Nevertheless, in keeping with other work this study suggests that
myocardial fibrosis in non-ischemic cardiomyopathies may be better assessed using ECV techniques
rather than LGE.38
The correlation between DynEq-CMR-derived ECV and histological CVF was maintained throughout
the LV, although there was greater ECV measurement variability in non-septal compared to septal
regions and in apical compared to basal and mid-ventricular myocardium, likely reflecting the
comparatively thinner ventricular walls in these regions. The degree of variability in the mid-
ventricular slice was comparable to that reported in other studies;14, 16 variability at other ventricular
levels has not previously been reported.
Mean DynEq-CMR-derived ECV in healthy subjects was in keeping with that found in other studies.9,
14-16, 18, 19, 39 Interestingly, ECV was significantly higher in the septum compared to other myocardial
regions in healthy subjects. This is in keeping with histological data from healthy myocardium, which
demonstrates higher collagen content in the septum compared to other regions,40 and with DynEq-
CMR data reported by Kawel et al.19 This finding, which may be secondary to the septum being
exposed to mechanical strain from both ventricles, has important implications both in terms of CMR
ECV measurement and histological assessment of CVF, as septal sampling appears not necessarily
representative of other myocardial regions.
In addition, ECV was seen to vary significantly between genders. This is in keeping with the findings
of Sado et al39 who, using EQ-CMR in healthy subjects with a similar age range to here, found gender
was an independent predictor of ECV in multivariate analysis (myocardial mass, hematocrit and
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patient height were not); indeed, the absolute difference in ECV between genders reported by Sado et
al is very similar to that found here. The reasons for the sex difference are not clear. However given
that the correlation between ECV and histological CVF was maintained throughout the heart, with low
observer variability particularly in the mid-ventricular slice, this finding appears genuine i.e. it does
not appear to be related to potentially greater partial voluming effects in theoretically thinner female
LV walls. As Sado et al39 discuss, it may be that this gender difference in ECV contributes to known
differences in cardiovascular disease expression between males and females. Further investigation of
this finding is required.
Mean DynEq-CMR-derived ECV in segments containing LGE was similar to that reported by
Ugander et al,16 who assessed ECV in patients with cardiac disease exhibiting infarct-typical or -
atypical LGE undergoing clinical CMR. However, mean ECV in myocardium remote from segments
containing LGE in the pre-transplant patients in the current study was substantially higher than ECV
in myocardium remote from segments containing LGE in the study by Ugander et al.16 This is likely to
reflect the end-stage nature of cardiac disease in the cohort studied here. As a result, whilst there was
considerable overlap between ECV in healthy subjects and ECV in myocardium in patients with
cardiac disease but remote from LGE in the study by Ugander et al (and in other studies),9, 39 there was
very little overlap in the current study.
Iles et al found a significant correlation (r=-0.7, p=0.03) between LV mid-ventricular isolated 15-
minute post-contrast T1 values and histological CVF in 9 heart transplant recipients, although tissue
was of right ventricular origin and of small volume (obtained via transvenous endomyocardial biopsy),
and this technique has been increasingly applied as a surrogate for ECV.24 However isolated post-
contrast T1 measurements are confounded by a number of factors such as renal function, hematocrit,
body fat and myocardial steatosis. In keeping with this, whilst the within-subject correlation between
isolated post-contrast T1 measurements and histological CVF was high in the current study, there was
no significant correlation between subjects.
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Limitations
Whilst the number of tissue samples included in this study is large, the total number of patients is
relatively small. However this reflects the difficulty in obtaining whole-heart histological data from
patients who have recently undergone CMR, and who do not have an acute inflammatory myocardial
condition. A greater number of patients could potentially have been recruited by performing the study
using post-mortem tissue obtained from patients who had undergone CMR for other reasons and
subsequently died. However immediate formalin fixation would not have been possible, which may
have introduced error in histological CVF quantification, as may cause of death. In the current study,
hearts remained in vivo until explantation when they were immediately fixed in formalin, minimizing
‘post-mortem’ changes. The interval between CMR and transplantation could have resulted in changes
in myocardial collagen content. However in 5 of the 6 patients, the interval was minimal (40 days or
less). There was a small improvement in correlation between DynEq-CMR-derived ECV and
histological CVF when ECV was calculated using the 15-minute post-contrast T1 values compared to
when the 10-minute post contrast values were used. As such, the optimal post-contrast time for T1
acquisition was not ascertained and it is possible that the correlation may have improved further with
later post-contrast T1 measurements. However, the clinical status of the patients involved, i.e. end-
stage heart failure, necessitated relatively short scan duration. Finally, an alternative CMR method of
quantifying ECV that has been less commonly applied than the methods assessed here, in which
contrast agent kinetics are deconvoluted using mathematical modeling, was not investigated.41
Conclusions
This study provides comprehensive validation of the DynEq-CMR method for measurement of
myocardial ECV. Isolated post-contrast measurement of myocardial T1 is insufficient for ECV
assessment.
Acknowledgements
The authors would like to thank Siemens for providing access to the MOLLI Sequence (WIP
CV_MOLLI_388).
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Sources of Funding
Christopher Miller is supported by a Fellowship from the National Institute for Health Research, UK
(NIHR-DRF-2010-03-98). Christopher Miller, Simon Williams, Nizar Yonan, and Matthias Schmitt
have received research funding from New Start Transplant Charity.
Disclosures
None.
References
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r Cardiovasculararrrr MM
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Table 1. Characteristics of patients who underwent cardiovascular magnetic resonance and heart transplantation
BMI indicates body mass index; BSA body surface area; eGFR estimated glomerular filtration rate; EDV end diastolic volume; ESV end-systolic volume; EF
ejection fraction. ‘Indexed’ refers to indexed to body surface area.
Patient Sex Diagnosis Age at
transplantation
(years)
Time between
CMR and
transplantation
(days)
Height
(m2)
Weight
(kg)
BMI
(kg/m2)
BSA
(m2)
eGFR
(mL/min/
1.73m2)
Indexed
EDV
(mL/m2)
Indexed
ESV
(mL/m2)
EF
(%)
Indexed
mass
(g/m2)
1 M DCM 44 10 1.83 80.4 24.0 2.0 74 269 199 26 52
2 M DCM 25 31 1.75 82.2 26.8 2.0 76 251 209 17 62
3 M IHD 48 276 1.68 73.5 26.0 1.8 65 142 112 21 58
4 M IHD 55 26 1.53 60.0 25.6 1.6 95 216 187 14 90
5 M IHD 63 40 1.74 68.0 22.5 1.8 82 106 70 33 54
6 M DCM 46 12 1.72 86.8 29.3 2.0 87 188 130 31 58
0000000 2222222.0.0.0.0.0.0.0
31 1.75 82.2 26.8 2.0
276 1.68 73.5 26.0 1.8
26 1.53 60.0 25.6 1.6
3333311111 1.75 82.222 22222 262222 .88888 2.0
222227676767676 1.6.6.66.688888 737777 .5.555 222226.6666 0 0 0 0 1.1111 888
2626 11111.5.553333 6060606060.00.000 222225.55.55 6 666 1.1.66
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Table 2. Characteristics of healthy subjects
Values are mean ± standard deviation. Age range is also given in brackets. BP indicates blood
pressure, eGFR estimated glomerular filtration rate, EDV end diastolic volume; ESV end-systolic
volume; EF ejection fraction. ‘Indexed’ refers to indexed to body surface area.
Overall
(n=30)
Group A
(0.10
mmol/Kg)
(n=10)
Group B
(0.15
mmol/Kg)
(n=10)
Group C
(0.20
mmol/Kg)
(n=10)
p value
Male 15 5 5 5
Age
Male
Female
45±13 (22-65)
45±15 (22-65)
45±12 (23-64)
44±14 (23-65)
44±15 (27-65)
45±15 (23-62)
45±14 (22-64)
46±17 (22-64)
44±12 (28-59)
46±13 (28-64)
44±15 (30-60)
47±12 (28-64)
0.98
Weight (Kg) 73.1±13.8 74.8±12.9 70.3±16.0 73.1±13.8 0.75
Height (m) 1.69±0.10 1.71±0.09 1.67±0.10 1.70±0.11 0.67
BSA (m2) 1.83±0.20 1.87±0.19 1.78±2.4 1.85±0.18 0.65
HR (bpm) 68±9 67±9 70±11 66±8 0.67
Systolic BP (mmHg) 114±11 116±11 114±11 112±12 0.63
Diastolic BP (mmHg) 67±11 71±11 65±13 66±7 0.41
eGFR (mL/min/m2) 95±17 94±11 100±20 91±19 0.50
Indexed EDV (mL/m2) 79±8 78±8 79±7 80±8 0.83
Indexed ESV (mL/m2) 26±5 27±5 27±5 26±5 0.78
EF (%) 67±5 66±4 66±4 68±5 0.45
BSA corrected Mass (g/m2) 45±8 44±9 43±8 47±7 0.46
0000.3.333333±1±1±1±1±1±1±16.6.66666 0000000 73737373737373
67±0000000 1010101010100 111.69 0. 0 .7 0.09 .67 0. 0 .
68±9 67±9 70±11
114±11 116±11 114±11 1
.69 0. 0 .7 0.09 .67 0. 0 .
1.8383838383±0±0±0±0±0.2.2.2220 000 0 1.111 8887±0±00.19 9 9 9 9 1.1.111 7778±2±2±2±2±2 4444.4 1.
666668±8±8±±±9 6767676767±9±9±9±9±9 7770±00 1111111111
111141414 11±1111 1111116±66 111111 11111141414±1±1±11±1111 111
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Figure Legends
Figure 1. Short-axis voxel-wise T1 relaxation time maps at basal (A and B; colors correspond to T1
relaxation time according to color bar legends), mid (D and E) and apical (G and H) ventricular levels
before (A, D, G) and 15-minutes after contrast agent bolus (B, E, H), with corresponding macroscopic
explanted heart sections (C, basal; F, mid and I, apical ventricular levels).
Figure 2. Histological analysis. Tissue blocks taken from each segment29 were embedded in paraffin
and stained with picrosirius red in order to determine histological collagen volume fraction (CVF).
Examples shown demonstrate (A) 8%, (B) 16%, (C) 36% CVF.
Figure 3. Dynamic-equilibrium CMR-measured myocardial extracellular volume, calculated using 10-
minute (A) and 15-minute (B) post-contrast T1 values, plotted against histological collagen volume
fraction. Isolated post-contrast T1 measurements made at 10-minutes (C) and 15-minutes (D) post-
contrast plotted against histological collagen volume fraction. Symbols correspond to different
patients, as set out in the legends.
Figure 4. Mean individual dynamic-equilibrium CMR-measured myocardial extracellular volume,
calculated using 15-minute post-contrast T1 values, plotted against mean individual histological
collagen volume fraction.
Figure 5. Dynamic-equilibrium CMR-measured myocardial extracellular volume fraction plotted
against histological collagen volume fraction, split according to the presence (red symbols) or absence
(black symbols) of infarct-typical late gadolinium enhancement (LGE) (A), and split according to the
presence (red symbols) or absence (black symbols) of any LGE (infarct-typical or infarct-atypical
patterns) (B). Symbols correspond to different patients, as set out in the legends.
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- g
s
ainst histological collagen volume fraction. Symbols correspond to dif
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Figure 6. (A) Myocardial (Myo, dashed lines) and blood (continuous lines) T1 relaxation times plotted
against time after contrast (Gd-DTPA) administration. Group A (red) received 0.10mmol/kg contrast
agent, Group B (blue) received 0.15mmol/kg and Group C (green) received 0.20mmol/kg. Pre-contrast
(Time 0) myocardial and blood T1 relaxation times were not significantly different between groups.
Post-contrast T1 relaxation times shortened significantly as contrast dose increased. (B) Myocardial
and blood R1 (1/T1) values plotted against time after contrast administration. Labeling as per A. (C)
Dynamic-equilibrium CMR-measured myocardial extracellular volume fraction (ECV) plotted against
time following contrast administration. ECV was significantly higher in group A compared to group B
and group C, but there was no significant difference between groups B and C. ECV increased linearly
over time in each group. Error bars represent ±1 SD.
Figure 7. Dynamic-equilibrium CMR-measured segmental myocardial extracellular volume fraction
(ECV) in; healthy subjects (Healthy), segments without late gadolinium enhancement in patients prior
to transplantation (No LGE), segments with infarct-atypical LGE in patients prior to transplantation
and segments with infarct-typical LGE in patients prior to transplantation. ECV was calculated using
15-minute post-contrast (0.20mmol/Kg Gd-DTPA) T1 values throughout. Horizontal lines represent
mean ± 1SD. ECV was significantly higher in segments without LGE in patients awaiting
transplantation than in healthy subjects. ECV was significantly different between segments without
LGE, segments with infarct-atypical LGE and segments with infarct-typical LGE, but on post-hoc
analysis only the difference between segments with infarct-typical LGE and segments without LGE
was significant (p<0.001).
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J.M. Parker and Matthias SchmittRay, Nizar Yonan, Simon G. Williams, Andrew S. Flett, James C. Moon, Andreas Greiser, Geoffrey Christopher A. Miller, Josephine Naish, Paul Bishop, Glyn Coutts, David Clark, Sha Zhao, Simon G.
Assessment of Myocardial Extracellular VolumeComprehensive Validation of Cardiovascular Magnetic Resonance Techniques for the
Print ISSN: 1941-9651. Online ISSN: 1942-0080 Copyright © 2013 American Heart Association, Inc. All rights reserved.
TX 75231is published by the American Heart Association, 7272 Greenville Avenue, Dallas,Circulation: Cardiovascular Imaging
published online April 3, 2013;Circ Cardiovasc Imaging.
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1
SUPPLEMENTAL MATERIAL
Phantom studies
Supplemental methods
Fifteen agarose and CuSO4 phantoms with T1 values of 175 – 1775ms (i.e. a range encompassing
physiological pre-‐ and post-‐contrast myocardial and blood T1 values), and a constant physiological T2
value (55±8ms), were scanned using the MOLLI sequence with simulated heart rates (50 to 110 beats
per minute, 10 beats increments). Reference T1 relaxation times were measured using an inversion
recovery spin echo sequence with 10 inversion times ranging from 25 to 5000ms (TR 10s, TE 8.1ms);
reference T2 relaxation times were measured using a spin echo sequence with 10 echo times ranging
from 10 to 200ms (TR 10s). Phantom imaging was repeated in order to assess inter-‐scan
reproducibility of MOLLI-‐derived T1 measurements.
Supplemental results
As has been demonstrated previously,1 the accuracy of the MOLLI sequence for measurement of T1
relaxation time was found to be dependent on T1 and heart-‐rate, with progressive underestimation
of T1 relaxation time, as compared with the inversion recovery spin echo-‐derived T1 measurements,
as T1 and heart rate increased (Supplemental Figure 1). Inter-‐scan reproducibility of phantom T1
measurements made using the MOLLI sequence was 2.1ms, (intraclass correlation coefficient 1.0).
Heart rate correction algorithm
Using the above phantom data, MOLLI-‐derived T1 relaxation times at different heart rates were fitted
to the inversion recovery spin echo-‐derived T1 relaxation times using second order polynomial fitting,
in order to derive the following correction algorithm,2 which was subsequently applied to all in-‐vivo
MOLLI-‐derived T1 measurements:
2
T1-‐IR = A*(T1-‐MOLLI)2 + B*(T1-‐MOLLI) + C
Where T1-‐IR = inversion recovery spin echo-‐derived T1 relaxation time; T1-‐MOLLI = MOLLI-‐derived T1
relaxation time; and A, B and C are as displayed in Supplemental Table 1. For the in vivo data, linear
interpolation of the coefficients between adjacent simulated heart rates was used.
Impact of heart rate correction algorithm
Application of the heart rate correction algorithm significantly increased mean pre-‐contrast
myocardial T1 relaxation times (for example in patients prior to transplantation, uncorrected T1
1086±129ms; corrected T1 1187±163ms; p<0.001). However the heart rate correction algorithm did
not lead to a significant difference in mean DynEq-‐CMR-‐derived ECV (ECV calculated using
uncorrected T1 values 43.8±6.8% versus ECV calculated using corrected T1 values 43.9±6.7%,
p=0.903). Furthermore, the linear regression equation for the relationship between DynEq-‐CMR-‐
derived ECV and histological CVF using uncorrected T1 values (histological CVF = 1.445 x ECV –
41.588) was almost identical to that when corrected T1 values were used (histological CVF = 1.451 x
ECV – 42.023). As such, for future work using DynEq-‐CMR-‐derived ECV calculated using the MOLLI
sequence applied here, a heart rate correction algorithm (which is cumbersome to apply) may not be
necessary. Nevertheless, for this validatory paper the heart rate correction algorithm was applied
throughout.
3
Supplemental Table 1
Heart rate A B C
50 0.0000671 1.013 -‐13.64
60 0.0000871 0.991 -‐9.38
70 0.0001215 0.951 -‐0.532
80 0.0001565 0.91 8.19
90 0.0001762 0.893 10.38
100 0.0002193 0.85 18.07
110 0.0002501 0.823 21.88
4
Supplemental Figure 1.
Black line represents the identity line.
5
Supplemental references
1. Messroghli DR, Greiser A, Fröhlich M, Dietz R, Schulz-‐Menger J. Optimization and validation of a
fully-‐integrated pulse sequence for modified look-‐locker inversion-‐recovery (MOLLI) T1 mapping of
the heart. J Magn Reson Imaging. 2007;26:1081-‐1086.
2. Lee JJ, Liu S, Nacif MS, Ugander M, Han J, Kawel N, Sibley, CT, Kellman P, Arai AE, Bluemke DA.
Myocardial T1 and Extracellular Volume Fraction Mapping at 3 Tesla. J Cardiovasc Magn Reson.
2011;13:75.