Repeatability and reproducibility of longitudinal relaxation rate in 12 small-animal MRI systems

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.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

Monitoring longitudinal disease progression in a novel murine Kit tumor model using high-field MRI

Animal models are an indispensable platform used in various research disciplines, enabling, for example, studies of basic biological mechanisms, pathological processes and new therapeutic interventions. In this study, we applied magnetic resonance imaging (MRI) to characterize the clinical picture of a novel N-ethyl-N-nitrosourea-induced Kit-mutant mouse in vivo. Seven C3H Kit^N824K/WT mutant animals each of both sexes and their littermates were monitored every other month for a period of twelve months. MRI relaxometry data of hematopoietic bone marrow and splenic tissue as well as high-resolution images of the gastrointestinal organs were acquired. Compared with controls, the mutants showed a dynamic change in the shape and volume of the cecum and enlarged Peyer´s patches were identified throughout the entire study. Mammary tumors were observed in the majority of mutant females and were first detected at eight months of age. Using relaxation measurements, a substantial decrease in longitudinal relaxation times in hematopoietic tissue was detected in mutants at one year of age. In contrast, transverse relaxation time of splenic tissue showed no differences between genotypes, except in two mutant mice, one of which had leukemia and the other hemangioma. In this study, in vivo MRI was used for the first time to thoroughly characterize the evolution of systemic manifestations of a novel Kit-induced tumor model and to document the observable organ-specific disease cascade.

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T₁ ), the spin-spin relaxation time (T₂ ), and the spin-lattice relaxation in the rotating frame (T_1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T₁ -, T₂ -, or T_1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

Regulation of Oxygen Homeostasis at the Intestinal Epithelial Barrier Site

The unique biology of the intestinal epithelial barrier is linked to a low baseline oxygen pressure (pO₂), characterised by a high rate of metabolites circulating through the intestinal blood and the presence of a steep oxygen gradient across the epithelial surface. These characteristics require tight regulation of oxygen homeostasis, achieved in part by hypoxia-inducible factor (HIF)-dependent signalling. Furthermore, intestinal epithelial cells (IEC) possess metabolic identities that are reflected in changes in mitochondrial function. In recent years, it has become widely accepted that oxygen metabolism is key to homeostasis at the mucosae. In addition, the gut has a vast and diverse microbial population, the microbiota. Microbiome–gut communication represents a dynamic exchange of mediators produced by bacterial and intestinal metabolism. The microbiome contributes to the maintenance of the hypoxic environment, which is critical for nutrient absorption, intestinal barrier function, and innate and/or adaptive immune responses in the gastrointestinal tract. In this review, we focus on oxygen homeostasis at the epithelial barrier site, how it is regulated by hypoxia and the microbiome, and how oxygen homeostasis at the epithelium is regulated in health and disease.

Experimental Protocols for MRI Mapping of Renal T1

The water proton longitudinal relaxation time, T₁, is a common and useful MR parameter in nephrology research. Here we provide three step-by-step T₁-mapping protocols suitable for different types of nephrology research. Firstly, we provide a single-slice 2D saturation recovery protocol suitable for studies of global pathology, where whole-kidney coverage is unnecessary. Secondly, we provide an inversion recovery type imaging protocol that may be optimized for specific kidney disease applications. Finally, we also provide imaging protocol for small animal kidney imaging in a clinical scanner.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development

INTRODUCTION

Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response.

MATERIALS AND METHODS

ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms.

RESULTS

Overall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners.

CONCLUSION

Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.

A novel gamma GLM approach to MRI relaxometry comparisons

PURPOSE

To demonstrate that constant coefficient of variation (CV), but nonconstant absolute variance in MRI relaxometry (T₁ , T₂ , R₁ , R₂ ) data leads to erroneous conclusions based on standard linear models such as ordinary least squares (OLS). We propose a gamma generalized linear model identity link (GGLM-ID) framework that factors the inherent CV into parameter estimates. We first examined the effects on calculations of contrast agent relaxivity before broadening to other applications such as analysis of variance (ANOVA) and liver iron content (LIC).

METHODS

Eight models including OLS and GGLM-ID were initially fit to data obtained on sulfated dextran iron oxide (SDIO) nanoparticles. Both a resampling simulation on the data as well as two separate Monte Carlo simulations (with and without concentration error) were performed to determine mean square error (MSE) and type I error rate. We then evaluated the performance of OLS/GGLM-ID on R₁ repeatability and LIC data sets.

RESULTS

OLS had an MSE of 4-5× that of GGLM-ID as well as a type I error rate of 20-30%, whereas GGLM-ID was near the nominal 5% level in the relaxivity study. Only OLS found statistically significant effects of MRI facility on relaxivity in an R₁ repeatability study, but no significant differences were found in a resampling, whereas GGLM was more consistent. GGLM-ID was also superior to OLS for modeling LIC.

CONCLUSIONS

OLS leads to erroneous conclusions when analyzing MRI relaxometry data. GGLM-ID factors in the inherent CV of an MRI experiment, leading to more reproducible conclusions.

Influence of water based embedding media composition on the relaxation properties of fixed tissue

BACKGROUND

In MRI of formalin-fixed tissue one of the problems is the dependence of tissue relaxation properties on formalin composition and composition of embedding medium (EM) used for scanning. In this study, we investigated molecular mechanisms by which the EM composition affects T2 relaxation directly and T1 relaxation indirectly.

OBJECTIVE

To identify principal components of formaldehyde based EM and the mechanism by which they affect relaxation properties of fixed tissue.

METHODS

We recorded high resolution ¹H NMR spectra of common formalin fixatives at temperatures in the range of 5 °C to 45 °C. We also measured T1 and T2 relaxation times of various organs of formalin fixed (FF) zebrafish at 7 T at 21 °C and 31 °C in several EM with and without fixative or gadolinium contrast agents.

RESULTS

We showed that the major source of T2 variability is chemical exchange between protons from EM hydroxyls and water, mediated by the presence of phosphate ions. The exchange rate increases with temperature, formaldehyde concentration in EM and phosphate concentration in EM. Depending on which side of the coalescence the system resides, the temperature increase can lead to either shortening or prolongation of T2, or to no noticeable change at all when very close to the coalescence. Chemical exchange can be minimized by washing out from EM the fixative, the phosphate or both.

CONCLUSION

The dependence of T2 in fixed tissue on the fixative origin and composition described in prior literature could be attributed to the phosphate buffer accelerated chemical exchange among the fixative hydroxyls and the tissue water. More consistent results in the relaxation measurements could be obtained by stricter control of the fixative composition or by scanning fixed tissue in PBS without fixative.