Key words MR-imaging - liver - iron overload - thalassemia
1. Introduction
Numerous studies have been performed to study the distribution of hepatic proton density fat fraction (PDFF) in nonalcoholic fatty liver disease (NAFLD) patients and healthy subjects (e. g., [1 ]). Differences between segments have been reported. For the monitoring of liver iron content (LIC), on the other hand, the usefulness of the transverse relaxation rate R2 * determined by gradient echo for LIC quantification has been shown, which reflects total body iron [2 ]
[3 ]. Volumetric multi-gradient-echo sequences are available now that allow the acquisition of the entire liver in a single breath hold [4 ]. Using the multi-echo technique, acquiring several signals with different echo times after one excitation, transverse relaxation rate R2 * and PDFF can be determined simultaneously in the entire covered region. The suitability of one of these volumetric sequences for the determination of liver iron content has recently been proven by manually placed regions of interest (ROIs) in appropriate liver cross-sectional images [5 ]. Meanwhile, software solutions are available for automated or semi-automated segmentation of the liver, e. g. a user-corrected template method as described by Mory et al. [6 ]. This enables the determination of an average R2 * value for the entire liver and additionally for individual liver segments [7 ].
The aim of this study was threefold: First, to evaluate whether semiautomatic liver segmentation is feasible on single-breath-hold MR volumetric images. Second, we aimed to determine R2 * values segment by segment and to check for deviations between the R2 * values of the individual segments. Third, segmental R2 * values were studied also in patient groups, subdivided according to average hepatic R2 * levels on the one hand and the main reasons for iron overload on the other.
2. Methods
The study was conducted according to the Declaration of Helsinki (last revision 2013, Fortaleza, Brazil). After approval by the University’s Ethics Committee, patients examined by MRI in our clinic between May 2017 and April 2018 for noninvasive hepatic iron content quantification were included after written informed consent of patients or, in the case of minors, of the parents. MRI investigation was performed at 1.5 T (MAGNETOM Avanto, Siemens Healthcare, Erlangen, Germany) as published before [5 ]. Briefly, with a multi-echo 3 D GRE sequence the entire liver was acquired in a single breath hold. Six echoes were acquired at echo times (TE) of 1.2, 2.4, 3.6, 4.8, 6, and 9 ms. The 3 D volume consisted of 56 slices with a field of view (FoV) of 400 × 300 × 224 mm3 at a voxel size of 2.5 × 2.5 × 4 mm3 . The acquisition matrix was 160 × 84 × 56. Immediately after data acquisition, R2 * and PDFF values were determined voxel by voxel in a multi-step fitting process accounting for fat-water signal dephasing, and were displayed as parameter maps [8 ].
2.1 Single-slice analysis
A single slice was chosen best suited to place three circular ROIs in the liver parenchyma avoiding vessels. The size and R2 * mean of the ROIs were documented and the weighted R2 * mean was determined according to the equation:
where R2 *i is the measured average R2 * in a given ROI, npix i is its number of pixels, and 2 * for the single-slice method.
2.2 Volumetric analysis
Image quality of the volumetric sequence was evaluated using a four-point Likert scale with 1 = excellent image quality, 2 = marginal artifacts not compromising liver diagnosis, 3 = image quality impaired by breathing and/or fat-water mismatch (so-called swaps) and 4 = severe image artifacts. Patients in whom the liver was not completely covered, e. g. due to a different breathing position compared to the survey slices or because the liver was not completely depicted in these, and patients with poor image quality (Likert score 3 or 4) of the volumetric sequence were excluded.
The liver was segmented using Liver Health, an additional software tool of the IntelliSpace Portal (ISP, Philips, Hamburg, Germany), by a medical student under the supervision of an experienced radiologist (more than 15 years of liver MRI experience). From the different contrasts offered by the multi-echo sequence, best suited images were chosen. After an initial automatic segmentation, the liver contour was checked on the axial slices as well as coronal and sagittal reconstructions and corrected if necessary. In cases with moderately to highly elevated R2 * values, the R2 * map was used to explicitly identify liver boundaries. The time required for user interaction was documented. After complete segmentation of the liver, it was divided into segments using manually placed anatomical landmarks. We used the division proposed by Couinaud/Bismuth with a total of 9 segments [9 ]. The landmarks for this subdivision were taken from the reference standard and were in detail: right portal vein bifurcation, Vena (V.) cava inferior, V. hepatica dextra, V. hepatica media, Fissura umbilicalis, bifurcation of left portal vein, left hepatic tip, ligamentum venosum, attachment of the ligamentum venosum at the portal vein, and attachment of the ligamentum venosum at the V. cava. Parts of segmented tissue could be selectively excluded by their R2 * values, e. g. vessels due to their long T2 * times, so that only the liver parenchyma relevant for iron overload was included. For this purpose, the software created a T2 * map and set the threshold values for T2 * to be considered to 1 and 50 ms. These could then be adjusted manually. After segmentation, the software determined the mean values and standard deviations of the R2 * values of the whole liver and divided by segments. Also, R2 * histograms could be created for the whole liver and each segment separately. The R2 * mean value as well as the volume were documented for the entire liver and for each segment.
2.3 Statistics
Statistical analysis was performed with SPSS (V. 27, 2020, IBM, Armonk, NY). All variables were checked for normal distribution using the Shapiro-Wilk test. The R2 * values determined by the two methods were checked for agreement using linear correlation. The significance of correlation was evaluated with Spearman’s rank test.
Segmental R2 * values were tested for statistically significant differences as described below. Furthermore, they were normalized with the R2 * of the total liver according to the equation:
where rR2 *Seg denotes the relative or normalized segmental R2 * value, R2 *Seg the measured R2 * mean value of the individual segments and R2 *Liv the mean R2 *value of the whole liver. Both the actual R2 * and rR2 * values were checked for differences between liver segments by analysis of variance (ANOVA) with repeated measures. For this purpose, sphericity was analyzed in advance using Mauchly’s test. Furthermore, an additional Bonferroni correction was performed because of multiple groups. The effect size was calculated according to Cohen.
In addition, we studied whether nonuniform R2 * distribution depended on R2 * levels by dividing our participants into two groups according to their average hepatic R2 * value, group A with R2 * < 140 s–1 , group B with R2 * > 140 s–1 , a threshold which corresponds to the LIC threshold for therapy indication of 4.5 mg/g and a recently published R2 * calibration for the sequence used [5 ]
[10 ]. The significance of differences between the two groups was calculated using the Mann-Whitney-U-Test.
Finally, relative segmental R2 * values were evaluated to determine if there were any differences due to disease. Of particular interest was the reason for iron overload. We divided diseases of study participants into three groups: solely increased iron absorption, but no transfusion (mainly hemochromatosis, group 1) versus transfusion-dependent anemias without markedly increased iron absorption (group 2) and diseases where iron overload was caused by both increased iron absorption and transfusions (group 3). Significance of differences due to diseases was addressed using a Kruskal-Wallis test. Differences between liver segments within the groups were again tested by ANOVA. Analogous to the entire cohort, Mauchly's test was performed in advance, as well as a Bonferroni correction. Again, effect size was calculated according to Cohen.
In all cases, p-values < 0.05 were assumed significant, p < 0.01 highly significant.
3. Results
In the chosen time interval, 58 participants were scanned. In all cases, the liver was imaged completely. 30/58 (52 %) of the studies received a Likert score 1 for excellent image quality, 14/58 (24 %) were scored 2. A total of 14/58 participants (24 %) had to be excluded, 10 were scored 3, and 4 received a score of 4. Thus, 44 participants were evaluated (24 m, 20f, age 23.7 ± 13 years (mean ± SD), age range 4.1 to 60.6 years).
The segmentation procedure was completed successfully in all cases. The total time for segmentation varied between 7 and 14 minutes, on average 9 minutes and 26 seconds. [Fig. 1 ] shows examples of liver contours and whole-liver histograms for two participants with different average liver R2 *. Histograms show a positively skewed R2 * distribution for both participants. For the participant with a higher R2 *, distribution was broader compared to the other, indicating a relevant number of voxels with R2 * above the average value for this participant.
Fig. 1 Examples of liver segments (a , b) overlaid on the image with the shortest TE in one representative patient. Subfigures c to f show two participants, one with low average liver R2 * (c , e) , the other with a markedly higher average liver R2 * (d , f) . The figure shows the liver contour overlaid on the R2 * map (panels c and d , where voxels excluded due to their T2 * exceeding the given threshold are marked in orange) and whole liver histograms (e , f) . Note that both histograms are positively skewed, a characteristic which is more pronounced in the participant with a larger average R2 * (f) .Abb. 1 Beispiele von Lebersegmenten bei einem repräsentativen Patienten (a , b) , überlagert auf das Bild mit der kürzesten TE. Die Abbildungen c bis f zeigen 2 Patienten, einen mit niedrigem durchschnittlichen Leber-R2 * (c , e) , den anderen mit deutlich höherem durchschnittlichen Leber-R2 * (d , f) . Dargestellt sind die Leberkontur, die der R2 *-Karte überlagert ist (Teilabb. c und d , wobei die aufgrund einer Überschreitung des vorgegebenen T2 *-Schwellenwerts ausgeschlossenen Voxel orange markiert sind), sowie die Histogramme der gesamten Leber (e , f) . Es fällt auf, dass beide Histogramme eine linksschiefe Verteilung zeigen, eine Eigenschaft, die bei dem Patienten mit dem größeren durchschnittlichen R2 * stärker ausgeprägt ist (f) .
The total liver volume for all patients was 1428 ± 483 ml (mean ± SD), and the range of volumes was 329 to 2788 ml. The mean R2 * value of all patients was 170.2 ± 112.1 s–1 (mean ± SD) determined with the ROI-based method and 163.7 ± 112.2 s–1 (mean ± SD) for the volumetric analysis. The correlation was highly significant with r = 0.995, p < 0.001. A scatter diagram of the R2 * values of the different procedures with regression lines is given in [Fig. 2 ]. The coefficient of determination R2 = 0.993 indicates a nearly ideal correlation. The regression line was close to identity which indicates congruence of the R2 * values between the two methods.
Fig. 2 Scatterplot of R2 * values determined with both methods. The dashed line is the regression line. Its equation and coefficient of determination are given. For single-slice analysis, ROIs were placed in the right liver lobe, mostly in segment 8.Abb. 2 Streudiagramm der mit beiden Methoden ermittelten R2 *-Werte. Die gestrichelte Linie ist die Regressionsgerade, ihre Gleichung und ihr Bestimmtheitsmaß sind angegeben. Die ROIs für die Einzelschichtanalyse wurden im rechten Leberlappen platziert, vorwiegend im Segment 8.
The volume averages of the segments are listed in [Table 1 ], as well as the mean actual and normalized R2 * values. A significantly lower R2 * in liver segment 1 is noticeable. In contrast, rR2 * was slightly greater than 1 in other segments, especially in segment 7. In segment 8, the relative R2 * value was closest to 1, i. e. the actual R2 * was closest to average hepatic R2 *.
Table 1
Various values found for individual segments.Tab. 1 Verschiedene Werte der einzelnen Segmente.
S1
S2
S3
S4a
S4b
S5
S6
S7
S8
Volume [ml]
37.8
191.0
134.5
122.0
83.8
267.6
123.0
210.7
257.7
StdDev Volume
21
88
73
46
36
117
57
87
96
Mean R2 * [s–1 ]
151
164
163
164
161
163
165
169
165
SD R2 *
102
109
109
116
106
111
117
117
116
Significance for R2 * differences with respect to segment 1 (p)
–
0.0015**
0.039*
0.082
0.2
0.036*
0.031*
0.0006**
0.083
Mean relative R2 * (rR2 *)
0.927
1.002
1.006
0.991
0.990
0.987
0.994
1.035
0.999
StdDev relative R2 *
0.084
0.072
0.081
0.048
0.083
0.057
0.072
0.085
0.049
Significance for rR2 * differences with respect to segment 1 (p)
–
0.001**
0.004*
0.006*
0.056
0.023*
0.026*
0.0003**
0.001*
Values of the individual segments, averaged over all patients. Significant differences for R2 * and relative R2 * values with respect to segment 1 are marked with an asterisk (*), highly significant differences with two asterisks (**).
Werte der einzelnen Segmente, gemittelt über alle Patienten. Signifikante Differenzen der R2 *- und relativen R2 *-Werte gegenüber Segment 1 sind mit einem Stern (*) markiert, hochsignifikante Differenzen mit 2 Sternen (**).
The relative R2 * values of the segments are shown in [Fig. 3 ]. Significant differences were found for segment 1 compared to the other segments except S4b, with highly significant differences for segments 2 and 7. No significant differences occurred between the other segments 2 to 8.
Fig. 3 Mean values (dots) and 95 % confidence interval (whiskers) for relative segmental R2 * values. Significant differences with respect to segment 1 are marked by asterisks (* = significant – p < 0.05, ** = highly significant – p < 0.01). No significant differences of rR2 * values between other liver segments were found.Abb. 3 Mittelwert (Punkte) und 95 % Konfidenzintervall (Balken) für die relativen R2 *-Werte der Segmente. Signifikante Unterschiede zu Segment 1 sind durch Sternchen gekennzeichnet (* = signifikant – p < 0,05, ** = hoch signifikant – p < 0,01). Es wurden keine signifikanten Unterschiede der rR2 *-Werte zwischen anderen Lebersegmenten festgestellt.
After dividing patients according to their mean R2 * value, we got 21 patients (12 m, age 21.8 ± 8.9y [mean ± SD], age range 4.9 to 38.6y) in group A with a mean hepatic R2 * < 140 s–1 and 23 patients (12 m, age 25.4 ± 15.9 [mean ± SD], age range 4.1 to 60.6y) in group B. Segmental R2 * distribution was comparable in both groups, as seen from relative R2 * values shown in [Table 2 ]. There were no significant differences in rR2 * values between the groups for each segment, but significant segmental differences within each group only occurred in group B, and only in three segments S2, S6 and S7. Note that, as above, relative R2 * values were closest to 1 in segment 8 for both groups.
Table 2
Segmental R2 * values in groups with different mean hepatic R2 *.Tab. 2 Segmentweise R2 *-Werte der Gruppen mit verschiedenen mittleren R2 *-Werten der gesamten Leber.
Whole liver
S1
S2
S3
S4a
S4b
S5
S6
S7
S8
Group A
Mean R2 * [s–1 ]
84.1
79.2
84.4
86.0
83.4
84.9
82.6
82.4
86.0
83.5
StdDev R2 *
31.5
30.1
32.3
33.4
32.3
34.1
35.6
33.2
30.5
30.4
Mean relative R2 * (rR2 *)
–
0.944
1.008
1.025
0.991
1.007
0.971
0.975
1.035
0.993
StdDev relative R2 *
–
0.072
0.058
0.072
0.045
0.090
0.056
0.075
0.098
0.053
Group B
Mean R2 * [s–1 ]
236.6
216.5
236.1
234.1
236.9
230.4
235.7
240.3
244.6
238.5
StdDev R2 *
109.8
102.0
103.5
106.5
116.1
102.1
107.3
115.1
115.2
116.1
Significance for R2 * differences with respect to segment 1 (p)
–
–
0.017*
0.44
0.30
1.00
0.12
0.06
0.005**
0.28
Mean relative R2 * (rR2 *)
–
0.909
1.000
0.986
0.993
0.973
1.006
1.016
1.035
1.006
StdDev relative R2 *
–
0.094
0.086
0.088
0.053
0.074
0.053
0.063
0.070
0.045
Significance for rR2 * differences with respect to segment 1 (p)
–
–
0.032*
0.302
0.125
1.00
0.055
0.004*
0.0004**
0.072
Mean R2 * and mean relative R2 * values for all segments in groups split by mean hepatic R2 * values. Patients in group A had a mean R2 * < 140 s–1 , patients in group B R2 * > 140 s–1 . For the whole liver, the volumetric R2 * values are given. The R2 * values determined by ROIs were 94.7 ± 40.4 s–1 (mean ± SD) for group A and 252.9 ± 107.3 s–1 (mean ± SD) for group B. There were no significant differences in relative segmental R2 * values between group A and B. However, significant differences with respect to segment 1 occurred only in group B (marked with * for significant differences, ** for highly significant differences), whereas in group A there were no significant differences between segments at all.
Mittlere R2 *- und mittlere relative R2 *-Werte für alle Segmente in den Gruppen, aufgeteilt nach mittleren hepatischen R2 *-Werten. Die Patienten der Gruppe A hatten einen mittleren R2 *-Wert < 140 s–1 , die Patienten der Gruppe B einen R2 *-Wert > 140 s–1 . Für die gesamte Leber sind die volumetrischen R2 *-Werte angegeben. Die durch ROIs ermittelten R2 *-Werte betrugen 94,7 ± 40,4 s–1 (Mittelwert ± SD) für Gruppe A und 252,9 ± 107,3 s–1 (Mittelwert ± SD) für Gruppe B. Es gab keine signifikanten Unterschiede bei den relativen segmentalen R2 *-Werten zwischen Gruppe A und B. Signifikante Unterschiede zu Segment 1 traten jedoch nur in Gruppe B auf (gekennzeichnet mit * für signifikante Unterschiede, ** für hochsignifikante Unterschiede), während es in Gruppe A keine signifikanten Unterschiede zwischen den Segmenten gab.
The number of patients in different disease groups and their diseases are given in [Table 3 ].
Table 3
Groups of study participants split by disease.Tab. 3 Gruppen der Studienteilnehmer unterteilt nach Krankheitsbildern.
Disease
Male
Female
Sum
Group 1
Primary Hemochromatosis
4
0
4
Pyruvate Kinase Deficiency
1
0
1
Congenital Dyserythropoietic Anemia
1
0
1
Hemolytic Anemia due to Hb Youngstown
1
0
1
Sum group 1
7
0
7
Group 2
Diamond Blackfan Anemia
3
2
5
Sickle Cell Disease
0
1
1
Congenital Hemolytic Anemia
0
1
1
Severe Aplastic Anemia
0
1
1
Leukemia after Bone Marrow Transplant
1
1
2
Osteopetrosis
1
0
1
Sum group 2
5
6
11
Group 3
Thalassemia
11
13
24
MDS after Bone Marrow Transplant
1
1
2
Sum group 3
12
14
26
Overall Sum
24
20
44
Disease patterns and number of participants in different groups, split by the reason for iron overload. Group 1: diseases with increased gastrointestinal iron resorption, but not receiving blood transfusions; group 2: transfusion-dependent anemias with only marginally increased iron resorption; and group 3: diseases requiring transfusion, but also cause increased iron resorption.
Krankheitsbilder und Anzahl der Teilnehmer in verschiedenen Gruppen, aufgeteilt nach dem Grund der Eisenüberladung. Gruppe 1: Erkrankungen mit erhöhter gastrointestinaler Eisenresorption, die jedoch keine Bluttransfusionen erfordern; Gruppe 2: transfusionsabhängige Anämien mit nur geringfügig erhöhter Eisenresorption; und Gruppe 3: Erkrankungen, die eine Transfusion erfordern, aber ebenfalls eine erhöhte Eisenresorption zur Folge haben.
Considering the different reasons for iron overload, there were seven patients in group 1, cf. [Table 3 ]. This group showed the lowest mean relative R2 * (rR2 *) in segment 1 with 0.92. This means that for this group of patients in segment 1 the R2 * value was on average 8 % lower than in the other segments. Eleven patients had transfusion-dependent anemia without inadequately increased iron resorption (Group 2, see [Table 3 ]). This group contained the participant with the highest rR2 * of segment 1 (1.13) in our collective. The mean value of rR2 * in segment 1 was 0.93, the same value as observed in group 3 (n = 26 participants, mostly thalassemia).
Significant differences between patient groups, however, with small effect sizes, were found in segments 3 to 5 and 7. Details are given in [Table 4 ].
Table 4
Relative and actual R2 * values for disease groups in selected liver segments.Tab. 4 Relative und tatsächlich gemessene R2 *-Werte der Krankheits-Gruppen in einzelnen Leber-Segmenten.
Whole liver
Segment 3
Segment 4a
Segment 4b
Segment 5
Segment 7
Relative R2 * (mean ± SD)
Group 1
–
1.049 ± 0.055
1.056 ± 0.044
1.023 ± 0.068
0.970 ± 0.028
0.983 ± 0.052
Group 2
–
0.959 ± 0.11
0.982 ± 0.054
0.945 ± 0.091
1.006 ± 0.037
1.032 ± 0.0849
Group 3
–
1.015 ± 0.065
0.978 ± 0.033
1.002 ± 0.079
0.985 ± 0.067
1.050 ± 0.089
Significance for rR2 * differences (p)
Group 1 vs. 2
–
0.033 *
0.0098 **
0.042 *
0.026 *
0.113
Group 1 vs. 3
–
0.252
0.002 **
0.66
0.113
0.035 *
Actual R2 * (mean ± SD)
Group 1
168.6 ± 113
175.7 ± 114
178.2 ± 122
168.1 ± 103
162.8 ± 109
168.7 ± 121
Group 2
208.5 ± 149
199.9 ± 146
207.3 ± 156
196.3 ± 142
210.2 ± 149
213.3 ± 150
Group 3
143.4 ± 90
144.7 ± 89
141.2 ± 91
144.1 ± 89
142.5 ± 90
149.7 ± 97
Relative and actual R2 * values in participant groups divided by disease for selected liver segments and significance of relative R2* values for differences between groups. Significant differences are marked by an asterisk. No significant differences were found between groups 2 and 3, and there were no significant differences between segments not mentioned here. There were no significant differences for actual R2 * values, neither for the whole liver nor for segments.
Relative und tatsächliche R2 *-Werte für ausgewählte Lebersegmente in den Patientengruppen unterteilt nach Krankheit sowie Signifikanz der Unterschiede der relativen R2 *-Werte zwischen den Gruppen. Signifikante Unterschiede sind durch ein Sternchen gekennzeichnet. Zwischen den Gruppen 2 und 3 wurden keine signifikanten Unterschiede festgestellt, und auch zwischen den hier nicht aufgeführten Segmenten gab es keine signifikanten Unterschiede. Ebenfalls wurden bei den tatsächlichen R2 *-Werten keine signifikanten Unterschiede gefunden, weder für die gesamte Leber noch für einzelne Segmente.
4. Discussion
Liver iron content determination by MRI is widely used today [2 ]
[5 ]
[11 ]
[12 ]
[13 ]
[14 ]
[15 ]
[16 ]
[17 ]
[18 ]
[19 ]. The volumetric acquisition of the liver in a single breath hold and the inline generation of R2 * maps represent a decisive progress. We used contiguous imaging of the whole liver to analyze segmental R2 * distribution.
Volumetric evaluation offers an innovative view on the mechanism of hepatic iron accumulation by segmental analysis. It worked well despite limited specific software features for MRI data analysis. Iron overload was studied segment by segment previously, but based on MRI data acquired only in a few slices, with one ROI placed in each segment [20 ]. The lower relative R2 * value of segment 1, also called the caudate lobe, compared to the total liver found here is most likely caused by its special vascular supply. R2 * differences in the other segments may also be caused by different blood flow in various portal vein segments, originating from vessel curvature and diameter, causing some segmental R2 * differences to be significant, while others are not. Also, outliers, which have a high influence due to small patient numbers, may influence the significance of differences.
The caudate lobe represents a physiologically distinct liver segment that has its own arterial and venous vessel system unlike the rest of the liver parenchyma. Physiologists, therefore, often consider it to be an independent lobe, which is supplied arterially via both the right and left hepatic artery. The venous drainage of the caudate lobe occurs via separate veins (Spieghel veins) directly into the vena cava caudal to the venous star [21 ]. For this reason, the increased gastrointestinal iron absorption in the majority of diseases in our patient population could have less impact on this segment which may explain the lower R2 * in the caudate lobe observed in all disease patterns, as well as the smaller differences between S1 and other segments in the group with a lower average R2 *. It may be of interest that a special vessel situation has previously served as explanation for the aberrant fat content of S4 [22 ].
To account for the variety of average hepatic R2 * values, we introduced the relative segmental values. Surprisingly, significant segmental R2 * differences with respect to the caudate lobe were also found for actual R2 * values. On the other hand, deviations of segmental R2 * values from average whole-liver R2 * values were lowest in segment 8.
Hernando et al. made sure to cover all segments in their recent ROI-based R2 * analysis of volumetric data but did not address segmental differences [23 ].
Ghugre et al. found inhomogeneous hepatic iron deposition on different microscopic length scales [24 ]. Our results indicate nonuniform R2 * distribution even on a larger scale, pointing to segmental differences in iron concentration. However, we were not able to judge whether there are compartments different from liver segments, but rather restricted our analysis to anatomical liver segments. Probably, a more sophisticated analysis approach like independent component analysis might reveal other functional hepatic subunits than anatomic segments.
R2 * differences between participant groups were significant in segments 3 to 5 and 7, but not in segment 1. Therefore, the special vascular supply and drainage of S1 did not play a role when comparing disease effects. The group with predominant resorption showed higher rR2 * than the other groups in S3 to S4b. In S5 and S7, the opposite was observed, while not all differences were significant, c.f. [Table 3 ]. These deviations related to underlying diseases possibly point to previously unknown functional disparity of hepatic segments.
It has to be stated that R2 * differences do not necessarily mean deviations in iron content since GRE sequences are more sensitive to aggregated than to dispersed iron [25 ]. Also, iron relaxivity was shown to depend on its oxygenation state, namely ferric vs. ferrous ions [26 ]. However, R2 * deviations point to metabolic effects differing between segments and disease patterns whether they are caused by different iron concentrations or a different form or oxygenation state of the iron.
Liver segmentation, which is mandatory for whole-liver volumetric analysis, required time-consuming user interaction in our study. Supported by artificial intelligence, fully automatic segmentation already works reliably enough to enable quick whole-liver volumetric R2 * analysis [27 ]
[28 ]. Segmental analysis, however, might still be a challenge at the moment, whereas automated volumetric R2 * determination has already been introduced.
The exclusion of datasets with insufficient image quality (Likert scores 3 and 4) does not mean that they were unsuitable for volumetric analysis. In order to evaluate feasibility, we wanted to minimize any factors probably affecting preliminary results. Since R2 * maps were less influenced by sequence limitations than PDFF maps, R2 * values could have been obtained in all patients with the volumetric approach, but probably while impairing consistency with respect to the ROI-based results.
The sequence used also provided MR-PDFF maps. A detailed presentation of results is beyond the scope of this paper. However, we would like to state that there are PDFF differences between segments which are not congruent to R2 * differences (manuscript in preparation for submission).
4.1 Limitations
The Liver Health software tool is intended for the evaluation of liver computed tomography data. For MRI liver investigations, a contrast-enhanced T1 -weighted sequence in the hepatobiliary phase is recommended. Since contrast agent is not indicated for liver iron quantification, segmentation was performed on the native data.
Due to its small volume and the difficult definition of the lig. venosum, the segmentation of S1 was most critical. An intra-observer analysis (data not shown) yielded an r value of 0.75 for S1 (and S4b), whereas all other segments were more reliably segmented with r > 0.8 for their volume. Regarding segmental R2 *, however, the r values were > 0.9 for all segments.
To determine the mean parenchymal R2 *, T2 * values outside manually defined thresholds were systematically excluded in our approach. A maximum T2 * value of 50 ms was helpful for the elimination of large vessels, but small vessels could not be handled with this approach because of partial volume effects. Therefore, our results might be influenced by vessel density differing between hepatic segments. An adaptive threshold might be helpful in the future to improve the validity of results.
The inline fitting of the volumetric sequence that was used was a prototype version. In the meantime, the fitting procedure has been optimized to minimize errors like fat-water swaps [29 ]. First experiences with a new scanner (Magnetom SOLA, Siemens Healthcare GmbH) let us expect that fewer patients will be found to be unsuitable for analysis in the future.
R2 * analysis was performed in this study solely on patients suspected of having liver iron overload, but not on healthy subjects. It would be of interest to study R2 * distribution also in healthy subjects and diseases different from conditions causing iron overload. Too few patients in our cohort had normal liver iron content, but we were able to demonstrate that R2 * distribution was similar in patients with an LIC below the therapy threshold compared to patients for whom therapy was required. The lack of significance for differences of rR2 * values observed in S2–8 to S1 for subgroups A and B is probably caused by the small number of participants in these groups. Accordingly, we observed significant rR2 * differences between segments only in the largest disease subgroup 3 containing 26 participants. Larger studies in healthy subjects might clarify whether relative R2 * deviations have physiologic reasons or are caused by hematologic diseases.
Dividing patients according to deviating reasons for iron overload led to small subgroups except group 3. Probably due to the small number of patients, segmental relative R2 * differences were not significant within these subgroups, while a consistent tendency was observed since S1 showed the lowest rR2 * in all subgroups, whereas the largest rR2 * value was observed in S7 for all subgroups except group 1 (increased iron absorption).
5. Conclusion
Despite all limitations, the suitability of the tested software for semi-automatic liver segmentation and division into segments based on volumetric MRI data was demonstrated. R2 * values determined for the whole liver have the advantage of higher reliability and better statistic power due to the larger number of voxels compared to data from individual ROIs drawn on a single slice. Moreover, the volumetric GRE sequence has significant potential beyond single-slice acquisition, e. g. less susceptibility to magnetic field inhomogeneities. Probable benefits of segmental R2 * determination for the clinical routine should be evaluated with larger patient numbers.
In a volumetric analysis covering the whole liver, we were able to demonstrate that hepatic R2 * values deviate between segments.
Segment 7 shows the highest R2 * except for disease group 1 (increased iron resorption) and should be avoided for LIC determination.
To minimize sampling errors, segment 8 should be used for noninvasive hepatic iron quantification, since segment 8 shows the least deviations with respect to average whole-liver R2 *.
Funding
Siemens Healthineers, Master Research Agreement
