t2 Assessment Different Softwares

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Eur Radiol DOI 10.1007/s00330-011-2208-1

CARDIAC

Heart and liver T2* assessment for iron overload using different software programs
Juliano L. Fernandes & Erika Fontana Sampaio & Monica Verissimo & Fabricio B. Pereira & Jose Alvaro da Silva & Gabriel S. de Figueiredo & Jose M. Kalaf & Otavio R. Coelho

Received: 1 April 2011 / Revised: 22 May 2011 / Accepted: 17 June 2011 # European Society of Radiology 2011

Abstract Objectives To assess the level of agreement and interchangeability among different software programs for calculation of T2* values for iron overload. Methods T2* images were analysed in 60 patients with thalassaemia major using the truncation method in three software programs. Levels of agreement were assessed using Pearson correlation and Bland-Altman plots. Categorical classification for levels of iron concentration by each software program was also compared. Results For the heart, all correlation coefficients were significant among the software programs (P <0.001 for all coefficients). The mean differences and 95% limits of agreement were 0.2 (−4.73 to 5.0); 0.1 (−4.0 to 3.9); and −0.1 (−4.3 to 4.8). For the liver all correlations were also significant with P <0.001. Bland-Altman plots showed differences of −0.02 (−0.7 to 0.6); 0.01 (−0.4 to 0.4); and −0.02 (−0.6 to 0.6). There were no significant differences in clinical classification among the software programs.
J. L. Fernandes : E. F. Sampaio : O. R. Coelho University of Campinas, Unicamp, Campinas, Brazil J. L. Fernandes : J. A. da Silva : G. S. de Figueiredo : J. M. Kalaf Radiologia Clinica de Campinas, Campinas, Brazil M. V erissimo : F. B. Pereira Centro Infantil Boldrini, Campinas, Brazil J. L. Fernandes (*) Department of Internal Medicine, Cardiology, Rua Antonio Lapa 1032, 13025-292 Campinas, SP , Brazil e-mail: [email protected]

Conclusions All tools used in this study provided very good agreement among heart and liver T2* values. The results indicate that interpretation of T2* data is interchangeable with any of the software programs tested. Key Points & Magnetic resonance imaging in iron overload assessment has become an essential tool. & Post processing options to establish T2* values have not been compared. & No differences were found on T2* of the liver or heart using 3 different techniques. & Availability of these methods should allow more widespread interpretation of iron overload by MRI.

Keywords Magnetic resonance . Iron overload . Thalassemia . Software . Hemosiderosis

Introduction The use of magnetic resonance imaging (MRI) in the iron overload assessment of patients with thalassaemia major (TM) and other inherited and acquired blood disorders is now considered routine and recommended in major clinical guidelines [1–3]. Despite significant advances in the development of new sequences used for the acquisition of images there is still some controversy on post-processing techniques and how to correctly analyse the data to obtain final T2* values in the heart and liver [4, 5]. While specific software for post-processing the acquired data have been developed and have been approved by the United States Food and Drug Administration (FDA) and European Community (CE mark) [6, 7], previous authors

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have used different manual approaches with general commercial spreadsheets or custom-written solutions [8, 9]. While the advantages and practical issues of dedicated software are not in dispute, not all MRI centres are capable of purchasing and maintaining specific licences for that purpose, especially in less developed countries or sites where only low volumes of these examinations are performed. As iron overload diseases occur with very ample distribution around the world and are predominantly concentrated in underdeveloped countries [10] the accuracy of interpreting T2* values should be sought with whatever tools are available locally despite limited resources. Moreover, because most modern MRI units have as a standard configuration the sequences that allow for T2* acquisition, the limited availability of these examinations is in most part bound to the interpretation of the data, with centres having to resort to outside services with higher costs if they want to make the examination accessible locally. Therefore, we performed this study to compare the accuracy and interchangeability of three post-processing tools using two dedicated commercial software programs with heart and liver T2* analysis modules and one approach using standard software within the MRI unit coupled with a commercial spreadsheet in order to obtain T2* values.

10-mm thickness, 35o flip angle, in-plane spatial resolution of 2.7×1.4 mm, field-of-view 350 mm, sampling bandwidth of 488 Hz/pixel. After 2005, all patients underwent a single breath hold multiecho bright blood sequence with the following parameters [12]: 9 different TEs (1.88 to 15.4 ms), 10 mm thickness, 20o flip angle, in-plane spatial resolution of 2.34×1.56 mm, field-of-view 400×300 mm, sampling bandwidth of 810 Hz/pixel. For the liver, a single axial slice was obtained in the centre of the organ again using two distinct gradient-echo sequences that differed only in the number of breath holds necessary to acquire the images: for examinations in 2004 and 2005 eight breath holds were required to obtain images with TEs ranging from 1.8 to 15 ms with a TR of 200 ms, 10-mm slice thickness, 20o flip angle, spatial resolution of 2.7×3.1 mm, field-of-view 350 × 250 mm, sampling bandwidth of 125 Hz/pixel. After 2005 a multiecho sequence with a single breath hold was used to obtain eight images with the same parameters but with TEs ranging from 1.6 to 14 ms. Image analysis For image analysis and measurement of T2*, three methods were used: CMRtools 2010 (Thalassemia-Tools, Cardiovascular Imaging Solutions, UK); CMR42 v3.2 (Circle Cardiovascular Imaging, Canada); Excel spreadsheet v14 (Microsoft Corporation, USA). All images were analysed in batches after anonymisation. To reduce any bias in image analysis a computer generated random list was made consisting of the type of image (liver or heart), the patient number and one of the three methods mentioned above. Therefore, the same patient had his or her liver or heart image interpreted nonconsecutively by each software program blindly. All images were analysed by two persons in consensus (with 6 months’ and 7 years’ experience in CMR). To more accurately reproduce the process of measuring T2* by each software program from the start, a different region of interest (ROI) was drawn each time using the tools provided within the program. The process of measurement consisted of first importing the anonymised image to the program’s database, drawing an ROI, applying the tools to measure T2* with motion correction and fitting adjustment and registering the final values as described previously [8]. For the heart, signal intensity was obtained using an ROI drawn through the full thickness of the septum wall of the myocardial short axis image. The ROI was chosen to include both endocardium and epicardium layers of the heart and to include septum from both ventricular intersections. Mean ROI size for the heart was 2.75±0.81 cm2. For the liver, the signal intensity was also provided using a ROI covering the right lobe of the liver parenchyma avoiding major vascular structures which were identified visually. Mean ROI size for the liver was 18.4±6.72 cm2. In CMRtools and CMR42 ROI

Materials and methods The local institutional review board approved the study methods and all subjects provided written informed consent. Sixty patients with TM from our database of over 150 patients were randomly selected for this study. MRI examinations had been performed between 2004 and 2009 for the assessment of both heart and liver iron overload. All patients were receiving chronic transfusion therapy and were under a single or combined iron chelation regimen. MRI image parameters Patients underwent an MRI examination without any contrast material in a commercial 1.5-T unit (Siemens Symphony, Erlangen, Germany) using a four-element phased-array coil. After localiser sequences, cine images for ventricular function evaluation were obtained using previously described steady-state free-precession (SSFP) sequences in short-axis, two-chamber and four-chamber views [11]. After that, for heart T2* images two different sequences were used depending on the year in which the examination was performed: from 2004 to 2005, a bright blood gradient-echo sequence with multiple breath holds using different TEs (ranging from 3.0 to 18 ms) was used [8]. The specific parameters for this sequence include: 9 images through the same mid-ventricular short axis slice;

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drawing and T2* fitting and reporting were already embedded in the programs; for Excel, signal intensities were obtained with the original unit’s software (Argus Syngo MR2004A, Siemens, Germany) with the values of both signal intensity and TEs manually inputted into an Excel spreadsheet (Supplemental File 1). The mean signal intensity in each slice with varying TEs was used to fit the T2* curve using the formula SI=Ke–TE/T2* in the spreadsheet. In all methods a curve-fitting truncation model consisting of a monoexponential decay curve was applied. Non-linear fitting was used in CMRtools and CMR42 and linear fit was applied in Excel. Patients were classified as having normal heart iron concentrations (T2*> 20 ms), mild to moderate (T2* between 10 and 20 ms) or severe concentrations (T2*< 10 ms) according to previously published guidelines [1, 3]. The same classification was applied to liver T2* values: normal >11.4 ms; mild between 3.8 and 11.4 ms; moderate 1.8 to 3.8 ms; severe <1.8 ms. Statistical analysis Data are presented as mean±SD. Mean T2* values were compared using a General Linear Model with Bonferroni post-hoc test. Correlations between liver and heart T2* among the software programs were obtained using Pearson correlation coefficients. Bland-Altman plots were also produced to assess agreement between the software programs using the mean absolute differences and a 95% limit of agreement. We also chose to assess intraclass correlation coefficients (ICC) on the three tools to further characterise their interchangeability. Categorical data for the clinical classification of the patients were compared using the Chi-squared test. Statistical analyses were performed using SPSS Statistics 16.0 (SPSS, USA) and MedCalc 10.2 (MedCalc Software, Belgium) with P values<0.05 considered statistically significant.

Table 1 Baseline characteristics Parameters Age (years) Male sex, N (%) Height (m) Weight (kg) Ferritin (μg/L) Transfusion requirements (mgFe/kg/year) Haemoglobin (g/dL) Ejection fraction Indexed systolic volume (mL/m2) Indexed diastolic volume (mL/m2) Indexed left ventricle mass (g/m2) Chelation therapy, N (%) Deferoxamine Deferiprone Deferasirox Deferoxamine+deferiprone V alues 19.2±7.8 29 (48%) 1.54±0.15 49.9±15.4 1839±1082 125.7±21.9 9.7±0.4 0.65±0.06 26.5±8.0 75.9±15.5 46.9±10.8 15 16 14 15 (25) (27) (23) (25)

Results Baseline characteristics of the study patients are presented in Table 1. The cohort consisted of relatively young TM patients (19.2±7.8 years old, range 7 to 35) with predominantly normal heart function (mean ejection fraction of 0.65±0.06, range 0.56 to 0.82). All patients were receiving chelation therapy, either with a single drug or using combined therapy. Heart T2* comparisons

CMR42 and Excel respectively, P=0.99). The correlations between each value among the software programs are presented in Fig. 1. All methods were significantly correlated with each other (0.971 for CMRtools vs Excel; 0.981 for CMRtools vs CMR42; 0.975 CMR42 vs Excel; P <0.001 for all Pearson correlation coefficients). The Bland-Altman plot also showed very good agreement among the different software programs in Fig. 2. Mean differences were small among the methods (0.2, 0.1 and −0.1 ms for CMRtools vs Excel, CMRtools vs CMR42 and CMR42 vs Excel respectively). The 95% limits of agreement plotted on these graphs were within acceptable ranges with outliers restricted to relatively higher values. Finally, ICC analysis of the data for heart T2* showed a very high absolute agreement of 0.9917 (95%CI of 0.9873 to 0.9948). We also separately analysed data only among the eight patients with a T2* lower than 20 ms by CMRTools and compared those values using the same methods as for the whole group in order to assess whether the software programs differed in the pathological clinical range. As with the whole data, correlation coefficients between all software programs was very high (0.986 for CMRtools vs Excel; 0.982 for CMRtools vs CMR42; 0.979 CMR42 vs Excel; P <0.001)— Fig. 3. Bland-Altman plots (Fig. 4) showed small mean differences among the software programs in this selected group (0.3, −0.9 and 1.2 ms for CMRtools vs Excel, CMRtools vs CMR42 and CMR42 vs Excel respectively). Liver T2* comparisons

Mean heart T2* values measured by the three post-processing software programs assessed were not significantly different (31.2±10.3, 31.3±10.1 and 31.0±10.3 ms using CMRtools,

In accordance with the results found regarding heart T2* values, all methods provided similar results for liver T2* as

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Fig. 1 Bivariate plots with linear regression and confidence intervals for heart and liver T2* values among the three software programs studied

well with mean values of 5.3±4.6, 5.3±4.6 and 5.4±4.4 ms (P = 0.99) using CMRTools, CMR42 and Excel. The correlation and Bland-Altman plots showing the comparison among the different methods are shown in Figs. 1 and 2. Pearson correlation coefficients were significant among the software programs (0.998, 0.999 and 0.998 for

CMRTools vs Excel, CMRTools vs CMR42 and CMR42 vs Excel respectively, P <0.001 for all). Mean differences among the values obtained were also very small among all software programs (−0.02, 0.01 and −0.02 for CMRTools vs Excel, CMRTools vs CMR42 and CMR42 vs Excel respectively). ICC analysis also showed

Fig. 2 Bland-Altman plot comparing mean absolute values of heart and liver T2* with the differences among the three software programs studied. The mean difference and the 95% limits of agreement are shown

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Fig. 3 Bivariate plots with linear regression and confidence intervals for heart and liver T2* values among the three software programs studied only in patients with iron heart overload (n =8, T2*<20 ms) or liver iron overload (n =55, T2*<11.4 ms)

close reproducibility between the solutions used with a value of 0.999 (95%CI 0.9990 to 0.9996). As was done with the heart analysis we also looked at differences among the software programs only in patients with pathological values of liver T2*<11.4 ms. In the 55 patients that fitted this criterion, results were closely

matched with those of the whole population (Figs. 3 and 4). Correlation coefficients were 0.996 for CMRtools vs Excel; 0.999 for CMRtools vs CMR42; 0.996 CMR42 vs Excel (P <0.001) with Bland-Altman plots with mean differences of −0.05, 0 and −0.05 for CMRTools vs Excel, CMRTools vs CMR42 and CMR42 vs Excel respectively.

Fig. 4 Bland-Altman plot comparing mean absolute values of heart and liver T2* with the differences among the three software programs studied only in patients with iron heart overload (n =8, T2*<20 ms) or

liver iron overload (n =55, T2*<11.4 ms). The mean difference and the 95% limits of agreement are shown

Eur Radiol Table 2 Pearson correlation coefficients among the methods using multiple and single breath hold techniques

Methods CMRTools vs Excel CMRTools vs CMR42 CMR42 vs Excel

Multiple Breath Holds 0.975 0.976 0.982

Single Breath Hold 0.960 0.971 0.977

P 0.98 0.99 0.98

Multiples versus single breath hold techniques Of our study patients, 21 individuals (35%) underwent imaging using the relatively outdated multiple breath hold method which is more prone to registration misalignments that could increase errors in the post-processing analysis. Therefore, we also analysed whether this would interfere with the heart T2* values provided by each method where this could be more of a problem. Apparently this did not change any of the results presented for the whole group and the correlations remained significant for both subgroups (Table 2) with no differences among mean T2* values (for multiple breath hold 31.4±11.3, 31.4± 11.4 and 31.2±11.4 ms for CMRtools, CMR42 and Excel respectively, P=0.99; for single breath hold 30.8±8.4, 31.0±8.4 and 30.6±8.6 ms for CMRtools, CMR42 and Excel respectively, P=0.99) Clinical significance To assess whether all the software programs would provide similar results in the clinical scenario where patients are classified into degrees of iron loading, we evaluated the percentages of patients in each category for the heart and liver using each software program (Table 3). No significant differences were observed among the classification provided by the software programs for both heart and liver. As can be seen, all patients were classified in the same way as having severe iron and liver concentrations by the three methods. For the liver, the three software programs classified the same patients as having normal concentrations as well with only one patient being classified as having mild iron overload by Excel and
Table 3 Clinical classification comparisons Heart iron concentration, N (%) Normal Mild to Moderate Severe Liver iron concentration, N (%) Normal Mild Moderate Severe CMRTools 52 (87) 6 (10) 2 (3) 7 25 16 12 (12) (42) (26) (20) CMR42 51 (85) 7 (12) 2 (3) 7 25 16 12 (12) (42) (26) (20) Excel 53 (88) 5 (9) 2(3) 7 26 15 12 (12) (43) (25) (20)

moderate by CMRTools and CMR42. For the heart, two patients were differently categorised by Excel in comparison to CMR42 and one patient in the comparison of CMRTools versus CMR42. These patients had T2* values that bordered the 20 ms cut-off and were placed either in the normal or mild-moderate category if their values fell slightly above or below this cut-off.

Discussion Our results show that the three software methods for postprocessing heart and liver T2* images in patients with TM provided very similar results with acceptable interchangeability. Moreover, no significant differences in the clinical classification of iron overload were observed among the methods which should allow for the confident use of any of the software programs analysed in centres that read these images. In a recent estimation of annual deaths due to iron overload, more than 95% of those deaths occurred in underdeveloped regions of the world [10]. In these areas of scarce resources, limited access to liver and heart MRI monitoring might partially explain these poor prognostic results. The results presented in this manuscript demonstrate that the use of a relatively inexpensive commercial spreadsheet generates similar results to those of very well developed and dedicated software programs which opens the possibility of using this approach in centres where these tools are not available. From a technical aspect, while it was not our objective to assess the ease of use of each method, it seemed clear that the automated approach embedded in each dedicated software program consumed much less time to postprocess the images than the manual transfer of the TE and ROI figures to a separate spreadsheet. Not only that, imprecisions and errors during these processes can occur more frequently than when one uses an integrated methodology. However, when we analysed the final clinical classification of these patients based on each software program, it appears that with any of them patients would be correctly classified and treated accordingly based on published guidelines [1, 3]. This is especially important because it is known that the linear monoexponential fitting model used in Excel has a slightly higher coefficient of variation compared with the non-linear fitting used in

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CMRtools and CMR42 [4]. While this may have some implication in the final T2* values observed, it does not seem to be a significant problem in a general clinical setting. When we look at the Bland-Altman plots for the liver and the heart, one can observe that the mean differences for the liver as well as the 95% limits of agreement are much lower than for the heart. This implies that at least part of the differences among the values observed might have been only due to inherent interstudy variation in the interpretation of the data itself which has also been shown to be higher in the heart compared with the liver [13]. However, it is reassuring to point out that when comparing multiple and single breath hold acquired images, the similarities among the software programs persisted in both subgroups despite the lower reproducibility described for the former [12]. When we analysed data only from patients with pathological values for T2* in the heart and liver, the results found were also very similar to those observed in the whole population. Despite the low number of patients with significant heart iron overload, in this group all software programs also demonstrated very close correlations. This again could be further verified by the fact that the clinical classification of patients did not change significantly despite the analysis method used. Moreover, as the variability of T2* values is greater at values over 20 ms, the fact that the differences observed in this study were small even at these values suggests that the results found verify the interchangeability of the methods tested. Finally, while our data are rather limited in patients with very severe heart iron overload, it does cover the full range of values most commonly found in clinical practice where these patients are fortunately becoming less common [14]. While we could have analysed the data with the same ROI values obtained only once and used in all software programs, we preferred to compare the values obtained with the ROIs obtained by each method independently to better reproduce what would be done if each approach was used individually. Ideally it would have been more accurate to compare the values of T2* obtained in each software program with the correlative direct measurement of iron whether in the heart or liver. However, we believed that these procedures would be impractical in the heart and too invasive in the liver considering the large amount of validation data obtained in previous research [15, 16]. While no definitive T2* post-processing gold standard has been established, CMRtools has been the most widely available and most frequently used tool to date with many multicentre trials based on it [17, 18]. Therefore, finding similar values with the other two approaches used in comparison to that standard seemed encouraging. Another limitation is that we restricted our comparisons to only three solutions while other tools like MRmap [19] and

Hippo-MIOT IFC-CNR [20] are also available and might provide similar findings. We also could have used free spreadsheet software to verify the data as we did with the commercial software Excel. However, while not free, this software is accessible with educational discounts and priced significantly lower than dedicated software. Moreover, as Excel has a market share of over 95% [21], we believe that it would be the most widely accessible program for most physicians. This study only looked into cross-sectional single point data and did not evaluate T2* values obtained serially. Therefore we cannot affirm how the differences observed among the software programs might clinically impact on the follow-up of patients who need to repeat the examination several times. Despite this, as the differences observed were much smaller than published variations in interstudy data, the interference of this minor observation should be insignificant. Finally, our centre has been performing these examinations for over 7 years. We do not know if the results obtained especially with the Excel spreadsheet can be reproduced in less experienced centres, to which theoretically this particular approach is most beneficial.

Conclusions Our data show that post-processing images for obtaining liver and heart T2* values for the assessment of iron overload can be performed interchangeably within the approaches studied. The availability of any of these software solutions might suffice to allow for clinical interpretation of liver and heart iron overload in MRI centres around the world.
Acknowledgements This work was supported by a grant from the public funding agency Fundacao de Amparo a Pesquisa do Estado de Sao Paulo.

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