Improvements in Between-Vendor MRI Harmonization of Renal T2 Mapping using Stimulated Echo Compensation.

BACKGROUND: T2 mapping is valuable to evaluate pathophysiology in kidney disease. However, variations in T2 relaxation time measurements across MR scanners and vendors may occur requiring additional correction. PURPOSE: To harmonize renal T2 measurements between MR vendor platforms, and use an extended-phase-graph-based fitting method ("StimFit") to correct stimulated echoes and reduce between-vendor variations. STUDY TYPE: Prospective. SUBJECTS: 8 healthy "travelling" volunteers (37.5% female, 32 ± 6 years) imaged on four MRI systems across three vendors at four sites, 10 healthy volunteers (50% female, 32 ± 8 years) scanned multiple times on a given MR scanner for repeatability evaluation. ISMRM/NIST system phantom scanned for evaluation of T2 accuracy. FIELD STRENGTH/SEQUENCE: 3T, multiecho spin-echo sequence. ASSESSMENT: T2 images fit using conventional monoexponential fitting and "StimFit." Mean absolute percentage error (MAPE) of phantom measurements with reference T2 values. Average cortex and medulla T2 values compared between MR vendors, with masks obtained from T2 -weighted images and T1 maps. Full-width-at-half-maximum (FWHM) T2 distributions to evaluate local homogeneity of measurements. STATISTICAL TESTS: Coefficient of variation (CV), linear mixed-effects model, analysis of variance, student's t-tests, Bland-Altman plots, P-value <0.05 considered statistically significant. RESULTS: In the ISMRM/NIST phantom, "StimFit" reduced the MAPE from 4.9%, 9.1%, 24.4%, and 18.1% for the four sites (three vendors) to 3.3%, 3.0%, 6.6%, and 4.1%, respectively. In vivo, there was a significant difference in kidney T2 measurements between vendors using a monoexponential fit, but not with "StimFit" (P = 0.86 and 0.92, cortex and medulla, respectively). The intervendor CVs of T2 measures were reduced from 8.0% to 2.6% (cortex) and 7.1% to 2.8% (medulla) with StimFit, resulting in no significant differences for the CVs of intravendor repeat acquisitions (P = 0.13 and 0.05). "StimFit" significantly reduced the FWHM of T2 distributions in the cortex and whole kidney. DATA CONCLUSION: Stimulated-echo correction reduces renal T2 variation across MR vendor platforms. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Assessment of hepatic transporter function in rats using dynamic gadoxetate-enhanced MRI: a reproducibility study.

OBJECTIVE: Previous studies have revealed a substantial between-centre variability in DCE-MRI biomarkers of hepatocellular function in rats. This study aims to identify the main sources of variability by comparing data measured at different centres and field strengths, at different days in the same subjects, and over the course of several months in the same centre. MATERIALS AND METHODS: 13 substudies were conducted across three facilities on two 4.7 T and two 7 T scanners using a 3D spoiled gradient echo acquisition. All substudies included 3-6 male Wistar-Han rats each, either scanned once with vehicle (n = 76) or twice with either vehicle (n = 19) or 10 mg/kg of rifampicin (n = 13) at follow-up. Absolute values, between-centre reproducibility, within-subject repeatability, detection limits, and effect sizes were derived for hepatocellular uptake rate (Ktrans) and biliary excretion rate (kbh). Sources of variability were identified using analysis of variance and stratification by centre, field strength, and time period. RESULTS: Data showed significant differences between substudies of 31% for Ktrans (p = 0.013) and 43% for kbh (p < 0.001). Within-subject differences were substantially smaller for kbh (8%) but less so for Ktrans (25%). Rifampicin-induced inhibition was safely above the detection limits, with an effect size of 75 ± 3% in Ktrans and 67 ± 8% in kbh. Most of the variability in individual data was accounted for by between-subject (Ktrans = 23.5%; kbh = 42.5%) and between-centre (Ktrans = 44.9%; kbh = 50.9%) variability, substantially more than the between-day variation (Ktrans = 0.1%; kbh = 5.6%). Significant differences in kbh were found between field strengths at the same centre, between centres at the same field strength, and between repeat experiments over 2 months apart in the same centre. DISCUSSION: Between-centre bias caused by factors such as hardware differences, subject preparations, and operator dependence is the main source of variability in DCE-MRI of liver function in rats, closely followed by biological between-subject differences. Future method development should focus on reducing these sources of error to minimise the sample sizes needed to detect more subtle levels of inhibition.

An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease.

Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.

Identifiability of spatiotemporal tissue perfusion models.

Objective.Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.Approach.For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth.Main results.Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters.Significance.These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.