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Early detection of heart function abnormality by native T1: a comparison of two T1 quantification methods

  • Pandji Triadyaksa
  • Dirkjan Kuijpers
  • Tugba Akinci D’Antonoli
  • Jelle Overbosch
  • Mieneke Rook
  • J. Martijn van Swieten
  • Matthijs Oudkerk
  • Paul E. SijensEmail author
Open Access
Cardiac

Abstract

Objective

To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods.

Methods

Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed.

Results

Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p < 0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p < 0.001–0.040) by median quantification compared with six (p < 0.001–0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053).

Conclusion

Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment.

Key Points

Median pixel-wise quantification of native T1 maps is robust and can be applied regardless of the statistical distribution of data points.

Median quantification is more sensitive to early heart function abnormality compared with mean quantification.

The new method yields significant native T1 value differentiation between the three coronary artery territories.

Keywords

Magnetic resonance imaging Myocardium Cardiomyopathies Statistical distribution 

Abbreviations

AHA

American Heart Association

CMD

Coronary microvascular dysfunction

CoV

Coefficient of variance

FA

Flip angle

FOV

Field of view

GRAPPA

Generalized autocalibrating partial parallel acquisition

LAD

Left anterior descending

LCx

Left circumflex artery

LGE

Late gadolinium enhancement

LoA

Limit of agreement

LV

Left ventricle

MAD

Median absolute deviation

MOLLI

Modified Look-Locker inversion recovery

MR

Magnetic resonance

MRI

Magnetic resonance imaging

SD

Standard deviation

TE

Echo time

TR

Repetition time

Introduction

Pre-contrast T1 relaxation time, the parameter at stake in native T1 mapping, has shown its potential for identifying myocardial tissue abnormality [1], with the limitation that the values measured are sequence-specific [2, 3, 4, 5, 6, 7]. Native T1 increases may indicate disease and have been associated with diffuse myocardial fibrosis in different types of cardiomyopathy [7, 8, 9, 10, 11, 12, 13, 14, 15]. Moreover, in patient groups with myocardial impairment, an increase of native T1 was observed in the absence of late gadolinium enhancement (LGE) [7, 9, 10, 14, 15] suggesting that native T1 mapping can be an early indicator of myocardial tissue abnormality. Therefore, a robust native T1 quantification method is needed to ensure early identification of heart function abnormality.

In measuring cardiac T1 value, numerous studies showed normal native T1 variation on different myocardial regions [4, 5, 8, 10, 11, 13, 16, 17, 18, 19]. Intersegmental variations complicate the standardisation of normal values and disease identification. Pixel-wise T1 value quantification also faces variability due to protocol parameters, sequence design, scanner adjustment, T1 fit model, tissue characteristics, and patient’s condition [6, 20]. In view of the heterogeneity of pixel-wise T1 values as illustrated in Fig. 1, variability may be reduced by the assessment of median values of pixel-wise T1 per segment rather than the evaluation of the means [14].
Fig. 1

Native T1 mapping of the left ventricular myocardium, three short-axis slices segmented by the AHA model in a case of normal heart function scaled (a) from 0 to 1800 ms and (b) from 900 to 1000 ms to show T1 heterogeneity

In liver and heart iron deposition assessment by T2* mapping, pixel-wise median quantification produced lower observer variability compared with mean quantification [21] and lower T2* variability in different myocardial regions [22, 23]. These studies showed that partial volume effect, heart motion artefact, the fitting model used, and observer’s myocardial contour determination influence the pixel-wise assessment and quantification in the region of interest. However, pixel-wise native T1 assessment studies published to date used mean quantification with a few ones checking the normality of the statistical distribution of datasets as a whole [11, 13, 14, 15, 24] rather than performing statistical normality testing of pixel-wise T1 distribution per segment. This study aims to investigate the normality of pixel-wise T1 values per left ventricular heart segment and subsequently compare the mean and median values. Application of both methods on patients with normal and abnormal heart function is used to assess their potential for early detection of heart function abnormality.

Materials and methods

This retrospective analysis was conducted on magnetic resonance imaging (MRI) data acquired from May until October 2015 with approval by the hospital review board waiving the requirement of informed consent. MRI including (native) T1 mapping sequences was used to evaluate 145 consecutive patients examined in clinical routine for suspicion of cardiomyopathy. Patients with overt signs of heart failure, i.e. LGE pattern (observed 10–15 min after 0.2 mmol/kg of gadoterate meglumine: Dotarem, Guerbet), irregular heartbeat or myocardial wall, and cavum thickening, were excluded. The remaining 57 patients were divided into two groups with either normal or abnormal functional heart magnetic resonance (MR) parameters. Normal heart function was defined as three of four MR parameters (i.e. left ventricle (LV) end-diastolic volume, LV end-systolic volume, stroke volume, and ejection fraction) being within the normal MR parameter ranges and the fourth still within the border line of normality as defined by Kawel-Boehm et al [25].

Cardiac magnetic resonance imaging

All MR scans were performed on a 1.5-T whole-body scanner (Aera, Siemens Medical Solutions). Functional heart MR parameters were acquired by performing cine imaging steady-state free precession images with echo time (TE) 1.1 ms, repetition time (TR) 42.1 ms, flip angle (FA) 56°, reconstructed voxel size 1.82 × 1.82 × 8 mm, field of view (FOV) 349 × 349, matrix 192 × 192, pixel bandwidth 930 Hz, phase resolution sampling 70%, phase FOV 100%, and GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) acceleration factor 2.

Modified Look-Locker inversion recovery (MOLLI) was implemented in a single breath hold at late diastole, using vendor-provided motion correction for T1 mapping based on image registration with synthetic image estimation [26]. The 5(3)3 MOLLI protocol acquired 5 images after the first inversion pulse, followed by a pause of 3 heartbeats prior to the acquisition of the next 3 images after the second inversion pulse. The protocol’s initial inversion time (TI) was 100 ms, TE 1.12 ms, TR 280.56 ms, and FA 35°. Reconstructed voxel size was 1.41 × 1.41 × 8 mm, FOV 306 × 360, matrix 218 × 256, phase resolution sampling 66%, phase FOV 85%, and GRAPPA acceleration factor 2.

Image analysis

T1 maps were generated by custom-written software (developed in MATLAB version 7.14, The MathWorks) at three short-axis locations (apical, mid-ventricular, and basal) using pixel-wise fitting of a three-parameter model [20]:
$$ SI=A-B{e}^{-\raisebox{1ex}{$ TI$}\!\left/ \!\raisebox{-1ex}{$T{1}^{\ast }$}\right.} $$
(1)
to acquire T1 as:
$$ T1=T{1}^{\ast}\left(\raisebox{1ex}{$B$}\!\left/ \!\raisebox{-1ex}{$A$}\right.-1\right) $$
(2)
where SI and TI are signal intensity and inversion time, respectively, while A and B are constant values. Two cardiac radiologists (with 5 and 7 years of experience, respectively) and two non-cardiac experts (a radiology technician with 15 years of experience and a non-cardiac radiologist with less than 1 year of experience in cardiac imaging) manually drew LV endocardial and epicardial contours once on the T1 map while carefully avoiding LV blood pool and epicardial fat (Fig. 1). Segmental T1 analysis was conducted on all pixels (without applying endocardial/epicardial inset correction) according to the American Heart Association (AHA) 16-segment model [19] on global myocardium by averaging the 16 segments, different slice locations, and different coronary artery territories [27].
The volumetric cardiac MR parameters were evaluated by a cardiac imaging post-processing radiology technician using QMASS software (Medis Medical Imaging Systems) and checked by a cardiac radiologist (Table 1).
Table 1

Characteristics of patients with normal and abnormal heart function according to the criteria of Kawel-Boehm et al [24]

 

Normal heart function (n = 26)

Abnormal heart function (n = 31)

p valuea

General parameter

  Number of males

13 (50)b

17 (55)b

0.716c

  Age (years)

47 ± 19

41 ± 18

0.279d

  Heart rate (bpm)

67 ± 8

66 ± 7

0.706

  BMI (kg/m2)

25.15 ± 2.50

24.00 ± 2.60

0.481

  BSA (m2)

1.96 ± 0.22

1.97 ± 0.25

0.940d

MR measured parameter

  LV mass (g)

86.67 ± 20.47

105.04 ± 22.14

0.031

  LV mass index (g/m2)

44.11 ± 10.42

53.34 ± 11.24

0.033

  LV EDV (ml)

155.97 ± 20.52

214.44 ± 25.91

< 0.001

  LV EDV index (ml/m2)

79.39 ± 10.44

108.89 ± 13.16

< 0.001

  LV ESV (ml)

61.23 ± 11.46

101.93 ± 22.48

< 0.001

  LV ESV index (ml/m2)

31.16 ± 5.83

51.76 ± 11.41

< 0.001

  Stroke volume (ml)

96.76 ± 11.58

104.55 ± 16.23

0.305

  LV EF (%)

61.50 ± 3.87

49.50 ± 6.15

< 0.001d

  Cardiac output (L/min)

6.14 ± 1.13

6.50 ± 1.21

0.773

Values are presented as mean ± standard deviation or median ± median absolute deviation or n (%). n, number of patients; bpm, beats per minute; BMI, body mass index; BSA, body surface area; MR, magnetic resonance; LV, left ventricle; EDV, end diastolic volume; ESV, end systolic volume; EF, ejection fraction

ap values calculated by the Mann-Whitney U test

bValue is number of patients, with percentage in parentheses

cp value by the chi-square test

dp values by the independent t test

Statistical analysis

Statistical normality testing of data distribution was assessed using the Shapiro-Wilk test using custom-written software (developed in MATLAB version 7.14) [28]. Cardiac MR parameter of a dichotomous variable was compared using the chi-square test and continuous variables were compared using independent t test or the Mann-Whitney U test as appropriate. On normal and abnormal heart function patient groups, each segment T1 quantification was reported both using mean ± standard deviation (SD) and median ± median absolute deviation (MAD) [29, 30] regardless of segment’s statistical normality status. On segments having normally distributed and non-normally distributed pixel-wise T1, comparison between mean and median T1 quantification was assessed by the Mann-Whitney U test. The agreements between mean and median segmental T1 quantification were assessed using the Bland-Altman plot with a limit of agreement (LoA) set to be 1.96 × SD of the difference.

A coefficient of variance (CoV) of the T1 relaxation time was calculated as the SD of the difference divided by the mean and expressed in percentage. Comparison of T1 values between two patient groups on different LV regions was conducted using the independent sample t test for data evaluated by the mean and the Mann-Whitney U test for data evaluated by the median. Multiple comparisons across myocardial regions were done by the Kruskal-Wallis test with the Dunn-Bonferroni post hoc test adjustment. Statistical analyses were performed using IBM SPSS statistics software version 23 (IBM Corporation) with p < 0.05 considered as statistically significant.

Results

Patient classification

According to the criteria of Kawel-Boehm et al [20], 26 of 57 patients were classified in normal heart function group and the remaining 31 patients were classified in abnormal heart function group with similar general characteristics, such as age, heart rate, body surface area, and body mass index (p > 0.05). Their characteristics are listed in Table 1 (and differentiated by gender, in Supplementary Table S1).

Statistical normality of native T1 data distribution

The assessment of AHA 16 segments of LV myocardium from 26 normal patients and 31 abnormal heart function patients resulted in a total of 416 and 496 segments, respectively. With four observers assessing these segments, we obtained 1664 and 1984 segments, respectively.

In all segments of normal patients, statistical normality testing of pixel-wise native T1 per segment showed that 964 of 1664 segments (58%) were statistically non-normally distributed, whereas in all segments of abnormal patients, this statistical distribution was found in 1140 of 1984 segments (57%). In segments having statistically normally distributed pixel-wise T1 (subject for mean quantification), segmental T1 quantification by either mean or median showed no significant difference of T1 value in normal heart function group (p = 0.532) and in abnormal heart function group (p = 0.628). This indicates that in statistically normally distributed data, median quantification is equivalent to the use of the mean. For segments with non-normally distributed pixel-wise T1 (subject for median quantification), a significant difference was found between the two T1 quantifications in both normal (p < 0.001) and abnormal heart (p = 0.003) function groups. This finding indicates that mean quantification cannot be used for statistical non-normally distributed data.

The Bland-Altman plot confirms these claims in normal heart function patients by showing smaller differences of pixel-wise T1 assessed by mean and median quantification for segments having statistically normally distributed pixel-wise T1 (mean difference of 0.95 ms, CoV of 0.85%, and LoA of 15.96 ms) (Fig. 2a) compared with segments with non-normally distributed T1 (mean difference of 9.67 ms, CoV of 1.84%, and LoA of 34.72 ms) (Fig. 2b). Likewise, in abnormal heart function patients (Fig. 2c), pixel-wise T1 had similar smaller Bland-Altman mean difference of 1.04 ms, CoV of 0.78%, and LoA of 14.83 ms in statistically normally distributed data as opposed to higher Bland-Altman of (mean differences of 7.11 ms, CoV of 1.74%, and LoA of 33.39 ms) in non-normally distributed data (Fig. 2d).
Fig. 2

Bland-Altman plot assessment of pixel-wise native T1 agreement per segment quantified by means and medians. a Quantification, in normal heart function patients, for segments having statistical normally distributed and statistical non-normally distributed pixel-wise T1 (b). c Quantification, in abnormal heart function patients, for segments having statistical normally distributed and statistical non-normally distributed pixel-wise T1 (d)

Regional T1 analysis and heart function abnormality

In a regional myocardial analysis (Table 2), improvement of interobserver reproducibility of segmental T1 values was found for most regional myocardium areas in normal and abnormal heart function patients when using median compared with the mean for its pixel-wise quantification. This was indicated by CoV reductions, whereas results were similar for observers with different cardiac imaging expertise background (Supplementary Table S2).
Table 2

T1 coefficient of variance between all observers in different left ventricular myocardial regions

  

Patients with normal heart function

 

Patients with abnormal heart function

  

CoV between observers using

 

CoV between observers using

 

nsa

Mean T1

Median T1

nsa

Mean T1

Median T1

Global LV myocardium

2496

5.29

4.88

2976

4.31

3.62

LAD

936

5.33

4.73

1116

4.52

3.59

RCA

780

4.18

3.64

930

3.85

3.12

LCx

780

6.17

6.04

930

4.42

4.05

Basal

936

3.60

2.88

1116

3.29

2.56

1

Anterior

156

4.25

2.73

186

4.71

3.54

2

Anteroseptal

156

2.82

2.40

186

2.32

1.80

3

Inferoseptal

156

2.83

2.50

186

2.45

2.01

4

Inferior

156

2.68

1.78

186

3.34

2.71

5

Inferolateral

156

2.95

2.07

186

2.85

2.21

6

Anterolateral

156

5.12

4.65

186

3.39

2.58

Mid-ventricular

936

5.12

4.52

1116

4.08

3.28

7

Anterior

156

6.37

6.02

186

5.96

4.70

8

Anteroseptal

156

4.04

3.15

186

3.30

2.43

9

Inferoseptal

156

1.91

1.43

186

2.50

1.78

10

Inferior

156

3.44

2.19

186

3.27

2.91

11

Inferolateral

156

5.62

5.00

186

3.39

2.73

12

Anterolateral

156

7.27

6.83

186

4.85

4.09

Apical

624

7.40

7.32

744

5.75

5.14

13

Anterior

156

8.86

8.52

186

6.35

5.33

14

Septal

156

3.34

2.48

186

2.77

2.26

15

Inferior

156

7.83

7.33

186

6.39

5.09

16

Lateral

156

8.53

9.16

186

6.57

6.78

Data are in percentage. LV, left ventricle; LAD, left anterior descending; RCA, right coronary artery; LCx, left circumflex artery; ns, number of segments; CoV, coefficient of variance

aThe number of segments reflects six combinations of segment comparisons between four observers

Regional T1 analysis of four observers on different LV myocardial regions by using mean and median T1 quantification is presented in Tables 3 and 4, respectively. For each table, the statistical normality testing of its data distribution per LV myocardial region is presented by Supplementary Table S3 for native T1 quantified by its mean and by Supplementary Table S4 for native T1 quantified by its median. Tables S3 and S4 show that most of the T1 data from different myocardial regions are statistically non-normally distributed reflecting inadequate use of mean quantification in Table 3 to differentiate two different patient groups. As a result, the differentiation of T1 values between normal and abnormal heart function groups is undetected in ten of 16 AHA segments of Table 3 (p = 0.059–0.879). When comparing the two patient groups using median quantification (Table 4), significant increase of T1 values is identified in abnormal heart function patients compared with normal heart function in all myocardial regions (p < 0.001–0.024) with exception in the mid-ventricular anteroseptal (p = 0.110) and basal anterior segments (p = 0.080). Heart function differentiation between the two patient groups is thus concluded to be significant in 14 myocardial segments (p < 0.001–0.040) by median quantification compared with only six (p < 0.001–0.042) when using the mean.
Table 3

Mean T1 value in different left ventricular myocardial regions

 

T1 value of patients with normal heart function

T1 value of patients with abnormal heart function

 
 

nsa

Mean ± SD (ms)

nsa

Mean ± SD (ms)

p value b

Global LV myocardium

2496

960.69 ± 60.92

2976

976.75 ± 68.65

< 0.001

LAD

936

958.85 ± 60.73

1116

974.73 ± 71.09

< 0.001

RCA

780

973.35 ± 60.23

930

989.35 ± 64.49

< 0.001

LCx

780

950.24 ± 59.65

930

966.57 ± 67.84

< 0.001

Basal

936

975.23 ± 45.31

1116

982.36 ± 63.21

0.003

1

Anterior

156

961.18 ± 46.10

186

971.17 ± 65.11

0.099

2

Anteroseptal

156

993.88 ± 48.61

186

992.96 ± 62.64

0.879

3

Inferoseptal

156

987.97 ± 42.21

186

993.07 ± 58.92

0.353

4

Inferior

156

987.97 ± 38.09

186

996.81 ± 64.46

0.117

5

Inferolateral

156

969.59 ± 39.13

186

979.66 ± 62.96

0.072

6

Anterolateral

156

950.78 ± 40.28

186

960.47 ± 57.23

0.068

Mid-ventricular

936

961.51 ± 52.86

1116

973.36 ± 64.77

< 0.001

7

Anterior

156

940.11 ± 56.45

186

953.18 ± 70.86

0.064

8

Anteroseptal

156

973.67 ± 45.14

186

977.09 ± 60.32

0.550

9

Inferoseptal

156

980.54 ± 46.28

186

990.38 ± 57.79

0.081

10

Inferior

156

976.31 ± 50.81

186

986.47 ± 58.59

0.091

11

Inferolateral

156

961.45 ± 42.94

186

982.18 ± 62.04

< 0.001

12

Anterolateral

156

936.95 ± 57.11

186

950.85 ± 67.31

0.042

Apical

624

937.66 ± 82.09

744

973.43 ± 80.65

< 0.001

13

Anterior

156

914.57 ± 79.12

186

962.01 ± 80.23

< 0.001

14

Septal

156

969.67 ± 48.66

186

991.98 ± 76.94

0.001

15

Inferior

156

933.98 ± 90.70

186

980.04 ± 79.49

0.004

16

Lateral

156

932.43 ± 92.86

186

959.71 ± 82.10

< 0.01

SD, standard deviation; ns, number of segment; LV, left ventricle; LAD, left anterior descending; RCA, right coronary artery; LCx, left circumflex artery

aThe number of segments reflects six combinations of segment comparisons between four observers

bp values of comparison between normal and abnormal heart function groups by independent sample t test

Table 4

Median T1 value in different left ventricular myocardial regions

 

T1 value of patients with normal heart function

T1 value of patients with abnormal heart function

 
 

nsa

Median ± MAD (ms)

nsa

Median ± MAD (ms)

p value b

Global LV myocardium

2496

959.85 ± 30.32

2976

974.94 ± 35.07

< 0.001

LAD

936

958.52 ± 33.29

1116

971.53 ± 35.36

< 0.001

RCA

780

971.78 ± 27.95

930

987.15 ± 37.05

< 0.001

LCx

780

952.39 ± 26.26

930

964.94 ± 33.10

< 0.001

Basal

936

966.94 ± 22.61

1116

980.79 ± 33.98

< 0.001

1

Anterior

156

964.35 ± 24.48

186

969.98 ± 33.09

0.080

2

Anteroseptal

156

976.65 ± 20.45

186

1004.20 ± 39.28

0.040

3

Inferoseptal

156

971.14 ± 19.05

186

987.85 ± 36.06

< 0.001

4

Inferior

156

981.81 ± 25.15

186

990.10 ± 39.13

0.010

5

Inferolateral

156

964.20 ± 24.18

186

981.66 ± 30.88

< 0.001

6

Anterolateral

156

957.23 ± 16.38

186

960.54 ± 28.07

< 0.001

Mid-ventricular

936

956.61 ± 31.27

1116

972.02 ± 33.83

< 0.001

7

Anterior

156

938.52 ± 39.88

186

953.70 ± 28.24

< 0.001

8

Anteroseptal

156

965.38 ± 30.20

186

972.83 ± 34.06

0.110

9

Inferoseptal

156

970.71 ± 22.85

186

982.84 ± 32.18

< 0.001

10

Inferior

156

972.89 ± 27.91

186

996.98 ± 32.71

< 0.001

11

Inferolateral

156

951.55 ± 22.85

186

981.01 ± 30.19

< 0.001

Apical

624

940.58 ± 39.64

744

971.31 ± 37.26

< 0.001

12

Anterolateral

156

932.91 ± 31.00

186

946.04 ± 33.09

0.010

13

Anterior

156

919.70 ± 41.48

186

962.45 ± 31.95

< 0.001

14

Septal

156

959.46 ± 36.14

186

980.53 ± 37.96

< 0.001

15

Inferior

156

936.24 ± 46.59

186

982.10 ± 43.85

< 0.001

16

Lateral

156

953.10 ± 36.88

186

965.16 ± 49.61

< 0.001

MAD, median absolute deviation; ns, number of segments; LV, left ventricle; LAD, left anterior descending; RCA, right coronary artery; LCx, left circumflex artery

aThe number of segments reflects six combinations of segment comparisons between four observers

bp values of comparison between normal and abnormal heart function groups by the Mann-Whitney U test

Using median quantification, regional LV T1 value in normal heart function patient group was found to be significantly different in the three short-axis slices and in the three coronary artery territories attributed to the 16 AHA segments (Fig. 3a) (p < 0.001–p = 0.023). However, in abnormal heart function patient group (Fig. 3b), T1 value between apical vs. mid-ventricular short-axis slices and between left anterior descending (LAD) and left circumflex artery (LCx) coronary artery territories were not significantly different (p > 0.999 and p = 0.053, respectively).
Fig. 3

Boxplot of median T1 in different left ventricular myocardial regions. Quantifications for normal (a) and abnormal (b) heart function patients. Comparisons between the regions were made by the Dunn-Bonferroni post hoc test adjustment of the Kruskal-Wallis test result

Discussion

This study shows that median value quantification can be used for segmental native T1 assessment regardless of the distribution of pixel values and therefore can replace mean value quantification where statistical data distribution is normal. Median quantification also showed robustness regardless of the observer’s background by improving interobserver reproducibility of segmental native T1. The superiority of median T1 pixel value quantification compared with mean quantification is confirmed by better differentiation observed between patients with normal and abnormal heart function, especially in the septal regions that are least sensitive to susceptibility artefacts [31]. Therefore, median quantification would be a solution to reduce the influence of any unwanted outlier pixel-wise T1 values. Another study has already promoted MAD of fitting residuals to avoid outliers in T1 fitting process yielding a robust measurement of native T1 [32].

In providing early indication of cardiomyopathy disease in patients with normal cardiac MR functional parameters, native T1 showed no value according to several studies [11, 13, 15]. Our own results obtained with statistical parametric testing and (suboptimal) mean quantification also failed to differentiate between normal and abnormal heart function patients in LV segmental native T1 evaluation. In this study, however, significant increases of T1 values in abnormal heart function patients were found when using median T1 quantification with non-parametric testing instead. Our results also suggest that parametric testing must be performed in native T1 quantification to make sure of statistical normality of the pixel-wise native T1 distribution prior to using means. Alternatively, one can simply use non-parametric testing and medians (as in this study) for the investigation of patient heart condition.

Novel findings in this study of native T1 in normal heart function patients quantified by the medians in different myocardial coronary perfusion territories (i.e. LAD, right coronary artery, LCx, apical, mid-ventricular, and basal), different short-axis slices, and different AHA segments elaborate on those in smaller studies of healthy subjects [16, 19]. The observed variation of T1 value in the LV of normal heart function patients can provide regional baseline T1 values for early detection of diffuse fibrosis and infarct identification.

Suggested elsewhere [33, 34, 35, 36], heart wall T1 elevation is related to coronary microvascular dysfunction (CMD). Camici et al [36] explained that morphological changes of CMD in the absence of myocardial diseases are characterised by microvascular remodelling, endothelial dysfunction, and smooth muscle dysfunction. In patients developing hypertrophic cardiomyopathy, remodelling of intramural coronary arterioles will result in medial and intimal wall thickening [36]. This study reported the elevation of native T1 values in different LV regions of abnormal functional heart patients. Moreover, the variation of native T1 value observed in normal patients between LAD and LCx coronary artery territories was absent in abnormal function heart patients, an observation that might indicate early progression of CMD. But to validate this relationship, more invasive and non-invasive clinical assessment is needed and therefore recommended for further study.

Limitations of this study are that it is retrospective and that patient separation into those having a normal heart function and those without a normal heart function was based on the cardiac MR functional parameters defined by thresholds of just one reported study [25], being, however, very similar to those reported elsewhere [5, 8, 9, 10, 11, 12, 14, 15, 37, 38, 39]. The advice of some [17, 19, 40, 41], to correct native T1 for blood pool, heart rate, age, and gender were not followed through in this study due to the low correlation of T1 with any of these factors. Furthermore, the changes in native T1 values after correction were small and population-dependent (results not shown). Moreover, previous studies reported conflicting findings with regard to these factors’ influence on native T1 value [10, 11, 18, 39, 42]. The T1 maps generated by custom-written software yielded slightly lower values with reduced deviations for all AHA segments compared with the values produced by the Siemens Solution T1 maps (Supplementary Table S5). Investigations into T1 value differences amongst different mapping procedures and into alternative calculation algorithms to improve T1 fitting accuracy [e.g. 43] were not conducted, considered beyond the scope of this study.

Some studies reported the association between diabetes mellitus and the progression of CMD [36, 44, 45]. Another limitation of this study is that diabetes mellitus status of the patients was not recorded.

In conclusion, T1 assessment by observations of medians showed higher interobserver reproducibility compared with mean T1, regardless of statistical normality of data. Increased robustness of myocardial native T1 assessed by pixel-wise medians thus facilitates the early detection of heart function impairment and of differences between LV segments and between the different coronary artery territories.

Notes

Acknowledgements

Pandji Triadyaksa receives scholarship from Directorate General of Higher Education of Republic Indonesia (617/E4.4/K/2011).

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is P.E. Sijens, Ph.D.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Electronic Presentation Online System (EPOS) European Congress Radiology 2017 Poster No. B-1197.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6364_MOESM1_ESM.docx (57 kb)
ESM 1 (DOCX 56.9 kb)

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Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Pandji Triadyaksa
    • 1
    • 2
  • Dirkjan Kuijpers
    • 3
  • Tugba Akinci D’Antonoli
    • 1
    • 4
  • Jelle Overbosch
    • 5
  • Mieneke Rook
    • 1
  • J. Martijn van Swieten
    • 5
  • Matthijs Oudkerk
    • 1
    • 6
  • Paul E. Sijens
    • 1
    • 5
    • 7
    Email author
  1. 1.University of GroningenGroningenThe Netherlands
  2. 2.Department of PhysicsDiponegoro UniversitySemarangIndonesia
  3. 3.Department of RadiologyHMC-BronovoThe HagueThe Netherlands
  4. 4.University Hospital Basel, Clinic of Radiology & Nuclear MedicineUniversity of BaselBaselSwitzerland
  5. 5.Department of RadiologyUniversity Medical Center GroningenGroningenThe Netherlands
  6. 6.Institute for Diagnostic AccuracyGroningenThe Netherlands
  7. 7.Department of Radiology, EB45University Medical Center GroningenGroningenThe Netherlands

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