Study design and participants
This is a single-center prospective cohort study in a university hospital with ESRD patients who received a kidney transplant and underwent two CMR examinations. The first exam (baseline) was performed between the 1st and the 10th postoperative days. The second one was performed 6 months after renal transplantation. We included consecutive patients over 18 years-old who received a kidney transplant from a living or deceased donor. We excluded patients with a contraindication to CMR (e.g., pacemaker, cochlear implant, cerebral aneurysm clip, tattooing, claustrophobia) or inability to perform breath hold. For comparison a group of age- and sex-matched healthy controls was selected from the hospital records. Healthy subjects had normal kidney function, no known chronic disease, and were not on regular medication.
This study complied with the Declaration of Helsinki, and the institutional review board of Botucatu Medical School-UNESP approved the research protocol (approval number: 972.129). All participants provided witnessed, written, informed consent. Siemens Healthineers (Erlangen, Germany) provided the use of work-in-progress # 448B (VB17A) quantitative cardiac parameter mapping
(T1|T2|T2*) in this study. No person from this company had access to study data or was involved in image analysis, manuscript preparation, or any part of the study. The authors had full control of the data submitted for publication.
CMR technique and measurements
All patients underwent their examination on a 3.0-T Magnetom Verio Scanner (Siemens, Erlangen, Germany) with a phased array chest coil, according to study protocol. A cardiac cine balanced steady-state free precession (bSSFP) sequence was acquired using retrospective cardiac gating. Typically, 25 phases were acquired in 2-, 3-, and 4-chamber long axis views and a stack of short axis views. Scan parameters: field of view (FOV) 37 cm, repetition time (TR) 43.54 ms, echo time (TE) 1.38 ms, flip angle (FA) 50°, slice thickness 6 mm, in-plane image resolution 1.6 x 1.6 mm. Myocardial tissue tagging was performed with an ECG-gated bSSFP line tagging sequence with complementary spatial modulation of magnetization (CSPAMM). Image parameters were: FOV 32 cm, TR 48.15, TE 2.54, FA 10°, slice thickness 7 mm with a tag spacing of 7 mm. Short-axis tissue tagging was performed on 3 levels of the LV, positioned at 25%, 50% and 75% of the distance between the mitral valve annulus and the apex on a LV 4-chamber view in end-systole, and in 2- and 4-chamber long axis views. Quantitative T2 mapping was performed using a T2-prepared SSFP sequence in a mid-ventricular short axis view with the following imaging parameters: FOV 36-cm, TR 254.32, TE 1.07 ms, flip angle 35°, slice thickness 8 mm, in-plane image resolution 2.5 x 1.9 mm, acquisition in late diastole on every fourth heartbeat; T2 preparations: 0 ms, 25 ms and 55 ms. Quantitative T1 mapping was performed with a Modified Look-Locker Inversion-Recovery (MOLLI) sequence in mid-ventricular short axis view without intravenous contrast injection (Native T1). Scan parameters: FOV 36-cm, TR 316.09, TE 1.12 ms, flip angle 35°, slice thickness 8 mm, in-plane image resolution 2.1 x 1.4 mm, acquisition in late diastole on every other heartbeat, minimal inversion time 120 ms; increment 80 ms. The T1 mapping scheme included 5 acquisitions after the first inversion pulse, followed by a 3-heartbeat pause and a second inversion pulse followed by 3 acquisitions (5(3)3).
The biventricular end-diastolic volume (EDV) and end-systolic volume (ESV) were measured by manual segmentation of the short axis cine images, using Argus function software (Siemens, Erlangen, Germany). The endocardial borders were traced at end-diastole and end-systole, including trabeculations and papillary muscles in the blood pool. EDV and ESV were calculated for each ventricle using the method of disc summation. Ventricular stroke volume (SV) was calculated as the difference between the EDV and ESV, and ventricular ejection fraction (EF) was (SV/EDV) × 100. LV epicardial borders were draw only at end-diastole to calculate LV mass (LVM). All volume measurements were indexed for the body surface area (BSA) and expressed in ml/m2.
Myocardial feature-tracking analysis was performed processing cine images using strain module of Segment Medviso software, which was previously validated in a clinical setting . Circumferential and radial strains were analyzed in basal, medial and apical short axis slices by manual segmentation of the LV blood pool cavity and myocardium, while longitudinal strains were analyzed in 2-,3- and 4-chamber long axis views. Strain values were obtained for each segment and global values defined as the mean of all segmental values. For validation, tagging strain analysis was performed using the same software to process tagged long axis views. Figure 1 displays an example of GLS feature-tracking analysis at baseline and follow-up CMR exams.
T1 and T2 maps were automatically generated on the MR scanner with motion corrected images using a novel non-rigid registration algorithm [20, 21]. A region of interest (ROI) was then drawn conservatively in the septal myocardium for each map, accordingly previous consensus .
An experienced reader (ACVM) measured ventricular volumes, mass and EF, while another experienced reader (MFB) independently performed T1, T2 and strain analysis, blinded to former results.
Analysis of reproducibility and validation of LV GLS measurements
To determine the intraobserver reproducibility of LV GLS measured by FT-CMR, 15 exams were randomly selected and the analysis repeated by the same observer about 6 months after the initial assessment. These exams were also used to validate LV GLS measured by FT-CMR against the reference standard tagging.
Kolmogorov-Smirnov test was applied to determine appropriate parametric or nonparametric tests. Quantitative variables were expressed as mean ± standard deviation or median (interquartile range) and compared by t test or Wilcoxon signed-rank test, whereas qualitative variables were expressed by their frequencies and percentages, and compared by the chi-square test or Fisher's exact test. The relationship between changes in LV GLS and variables of interest were assessed using Pearson’s correlation coefficients for continuous normally distributed variables and Spearman’s correlation for categorical or non-normally distributed data. Linear regression analysis was used to evaluate the influence of clinical variables in LV GLS changes. Intraobserver reproducibility was assessed by analyzing Bland-Altman plot. All data were analyzed using SAS Studio 3.8 or Microsoft Excel software. A p-value of ≤ 0.05 was considered significant.